CN118157325B - Real-time monitoring method and system for new energy power - Google Patents
Real-time monitoring method and system for new energy power Download PDFInfo
- Publication number
- CN118157325B CN118157325B CN202410565788.4A CN202410565788A CN118157325B CN 118157325 B CN118157325 B CN 118157325B CN 202410565788 A CN202410565788 A CN 202410565788A CN 118157325 B CN118157325 B CN 118157325B
- Authority
- CN
- China
- Prior art keywords
- power
- monitoring
- data
- determining
- check
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 208
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000004088 simulation Methods 0.000 claims abstract description 38
- 238000010248 power generation Methods 0.000 claims abstract description 27
- 238000013524 data verification Methods 0.000 claims abstract description 22
- 230000003213 activating effect Effects 0.000 claims abstract description 8
- 238000012795 verification Methods 0.000 claims description 47
- 238000013507 mapping Methods 0.000 claims description 22
- 238000004458 analytical method Methods 0.000 claims description 10
- 238000005192 partition Methods 0.000 claims description 10
- 230000002159 abnormal effect Effects 0.000 claims description 9
- 230000002452 interceptive effect Effects 0.000 claims description 7
- 238000007726 management method Methods 0.000 claims description 6
- 238000012806 monitoring device Methods 0.000 description 8
- 230000005856 abnormality Effects 0.000 description 6
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 230000004044 response Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 238000012300 Sequence Analysis Methods 0.000 description 1
- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000003990 capacitor Substances 0.000 description 1
- 230000001364 causal effect Effects 0.000 description 1
- 230000008614 cellular interaction Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 230000002195 synergetic effect Effects 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Economics (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Power Engineering (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The application provides a real-time monitoring method and a system for new energy power, which relate to the technical field of power monitoring, wherein the method comprises the following steps: determining a new energy power distribution network, identifying key distribution points meeting a fluctuation threshold, building a simulation monitoring module, executing analog quantity monitoring and evaluating power indexes on a new energy power generation system, traversing the key distribution points, determining and activating sensing monitoring equipment, returning monitoring data and checking, judging whether a consistency tolerance interval is met, determining a judging characteristic value, and performing compensation adjustment to serve as a real-time monitoring result. The application can solve the problems that the prior art cannot accurately position key distribution points, is difficult to monitor analog quantity, has single data verification mode and inaccurate monitoring result, realizes comprehensive and accurate monitoring of the new energy power distribution network, effectively reduces the risk of power grid faults and ensures the stable operation of the new energy power system.
Description
Technical Field
The application relates to the technical field of power monitoring, in particular to a real-time monitoring method and system for new energy power.
Background
As the duty ratio of new energy power such as wind energy and solar energy in the power grid gradually increases, the inherent intermittence and fluctuation of the new energy power provide new challenges for the stable operation of the power system. Therefore, monitoring the state and performance of new energy power in real time becomes critical. The significant changes of the power load structure in the modern power system, such as nonlinear and impact loads such as a semiconductor rectifier, a variable frequency adjusting device and the like, also cause the problems of distortion, voltage fluctuation, flicker, three-phase unbalance and the like of the voltage waveform of the power grid. These problems not only affect the quality of the supplied electrical energy, but may also pose a threat to the safe operation of the power system.
At present, the prior art detects through carrying out electric power analog quantity mode, and is simple convenient and easy to maintain, and interference killing feature is weaker, and monitoring accuracy is not high.
In summary, the prior art cannot accurately position the key distribution points, is difficult to perform analog monitoring, has a single data verification mode, and has inaccurate monitoring results.
Disclosure of Invention
The application aims to provide a real-time monitoring method and a real-time monitoring system for new energy power, which are used for solving the problems that key distribution points cannot be accurately positioned, analog quantity monitoring is difficult to perform, a data verification mode is single, and a monitoring result is inaccurate in the prior art.
In view of the above problems, the present application provides a method and a system for real-time monitoring of new energy power.
In a first aspect, the present application provides a real-time monitoring method for new energy power, the method being implemented by a real-time monitoring system for new energy power, wherein the method includes: determining a new energy power distribution network which is monitored in advance, and identifying key distribution points which meet a fluctuation threshold, wherein the fluctuation threshold is a critical value based on interference influence degree; based on the new energy power distribution network and the key distribution points, building a simulation monitoring module, wherein the simulation monitoring module has partition relatively independent supervision and adjacent area interactive supervision; reading a monitoring target of a pre-monitoring task, performing analog quantity monitoring on a new energy power generation system based on the analog monitoring module, and evaluating an electric power index based on analog electric data, wherein the electric power index comprises a dynamic scheduling dimension and an electric quality dimension; traversing the key distribution points, randomly determining power check points and activating configured sensing monitoring equipment, wherein the power check points comprise at least two; returning sensing monitoring data based on the electric power check points, mapping the analog electric data, checking, and determining a data check result, wherein the check standard comprises inter-point check and analog check; identifying the data verification result, judging whether a consistency tolerance interval is met, and determining a judgment characteristic value, wherein the consistency tolerance interval is a verification allowable deviation range; and based on the judging characteristic value, performing compensation adjustment of the electric power index as a real-time monitoring result.
In a second aspect, the present application also provides a real-time monitoring system for new energy power, for performing a real-time monitoring method for new energy power according to the first aspect, wherein the system comprises: the system comprises a key distribution point acquisition unit, a power distribution unit and a power distribution unit, wherein the key distribution point acquisition unit is used for determining a new energy power distribution network which is monitored in advance, and identifying key distribution points which meet a fluctuation threshold, and the fluctuation threshold is a critical value based on interference influence degree; the simulation monitoring module building unit is used for building a simulation monitoring module based on the new energy power distribution network and the key distribution points, and the simulation monitoring module has partition relatively independent supervision and adjacent area interactive supervision; the analog quantity monitoring unit is used for reading a monitoring target of a pre-monitoring task, performing analog quantity monitoring on a new energy power generation system based on the analog monitoring module, and evaluating an electric power index based on analog electric data, wherein the electric power index comprises a dynamic scheduling dimension and an electric quality dimension; the sensing monitoring equipment determining unit is used for traversing the key distribution points, randomly determining power check points and activating the configured sensing monitoring equipment, wherein the power check points comprise at least two; the verification result determining unit is used for returning sensing monitoring data based on the power verification points, mapping the analog power data and performing verification to determine a data verification result, wherein the verification standard comprises inter-point verification and analog verification; the judging characteristic value acquisition unit is used for identifying the data verification result, judging whether a consistency tolerance interval is met or not, and determining a judging characteristic value, wherein the consistency tolerance interval is a verification allowable deviation range; and the monitoring result acquisition unit is used for carrying out compensation adjustment on the electric power index based on the judging characteristic value to serve as a real-time monitoring result.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
Determining a new energy power distribution network to be monitored, and identifying key distribution points meeting a fluctuation threshold, wherein the fluctuation threshold is a critical value based on interference influence degree; based on the new energy power distribution network and the key distribution points, building a simulation monitoring module, wherein the simulation monitoring module has partition relatively independent supervision and adjacent area interactive supervision; reading a monitoring target of a pre-monitoring task, performing analog quantity monitoring on a new energy power generation system based on the analog monitoring module, and evaluating an electric power index based on analog electric data, wherein the electric power index comprises a dynamic scheduling dimension and an electric quality dimension; traversing the key distribution points, randomly determining power check points and activating configured sensing monitoring equipment, wherein the power check points comprise at least two; returning sensing monitoring data based on the electric power check points, mapping the analog electric data, checking, and determining a data check result, wherein the check standard comprises inter-point check and analog check; identifying the data verification result, judging whether a consistency tolerance interval is met, and determining a judgment characteristic value, wherein the consistency tolerance interval is a verification allowable deviation range; based on the judging characteristic value, the compensation adjustment of the electric power index is carried out, and the electric power index is used as a real-time monitoring result, so that the problems that key distribution points cannot be accurately positioned, analog quantity monitoring is difficult to carry out, the data verification mode is single in the prior art, the monitoring result is inaccurate, the comprehensive and accurate monitoring of a new energy electric power distribution network is realized, the risk of power grid faults is effectively reduced, and the stable operation of a new energy electric power system is ensured.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent. It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the application or to delineate the scope of the application. Other features of the present application will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the application or the technical solutions of the prior art, the following brief description will be given of the drawings used in the description of the embodiments or the prior art, it being obvious that the drawings in the description below are only exemplary and that other drawings can be obtained from the drawings provided without the inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for real-time monitoring of new energy power according to the present application;
fig. 2 is a schematic structural diagram of a real-time monitoring system for new energy power according to the present application.
Reference numerals illustrate:
The system comprises a key distribution point acquisition unit 11, an analog monitoring module construction unit 12, an analog quantity monitoring unit 13, a sensing monitoring device determination unit 14, a verification result determination unit 15, a judgment characteristic value acquisition unit 16 and a monitoring result acquisition unit 17.
Detailed Description
The application provides the real-time monitoring method and the system for the new energy power, which solve the problems that the key distribution points cannot be accurately positioned, analog quantity monitoring is difficult to carry out, the data verification mode is single, the monitoring result is inaccurate, the comprehensive and accurate monitoring of the new energy power distribution network is realized, the risk of power grid faults is effectively reduced, and the stable operation of the new energy power system is ensured.
In the following, the technical solutions of the present application will be clearly and completely described with reference to the accompanying drawings, and it should be understood that the described embodiments are only some embodiments of the present application, but not all embodiments of the present application, and that the present application is not limited by the exemplary embodiments described herein. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application. It should be further noted that, for convenience of description, only some, but not all of the drawings related to the present application are shown.
Embodiment one:
Referring to fig. 1, the application provides a real-time monitoring method for new energy power, wherein the method is applied to a real-time monitoring system for new energy power, and the method specifically comprises the following steps:
s1: determining a new energy power distribution network which is monitored in advance, and identifying key distribution points which meet a fluctuation threshold, wherein the fluctuation threshold is a critical value based on interference influence degree;
Specifically, the new energy power distribution network is comprehensively analyzed, including the topological structure of the power network, access points of the new energy power, historical operation data and the like. The overall condition of the power grid is obtained, including the distribution of new energy power, the capacity and state of a transmission line and possible weak links. A fluctuation threshold is set. This threshold is set based on a critical value of the degree of interference impact for determining which nodes' power fluctuations may have a significant impact on the whole grid. When the fluctuation threshold is set, the stability requirement of the power grid, the characteristics of new energy power, such as the fluctuation range of wind power and photovoltaic output and the condition that the power grid is interfered in historical data, are comprehensively considered. Key distribution points are identified. These key distribution points are those nodes where the power fluctuations exceed the fluctuation threshold, which pose a potential threat to the stable operation of the whole grid. The process of identifying these key points may be by monitoring the power data of each node of the grid in real time and then comparing with a fluctuation threshold.
S2: based on the new energy power distribution network and the key distribution points, building a simulation monitoring module, wherein the simulation monitoring module has partition relatively independent supervision and adjacent area interactive supervision;
Specifically, basic data of a new energy power distribution network is collected, including a power grid topological structure, line impedance, transformer parameters, positions and capacities of new energy access points and the like. For the identified key distribution points, detailed historical operating data is collected, including voltage, current, power factor, and the like. The whole framework of the simulation monitoring module is designed, so that the module can receive and process data in real time, and simulation monitoring is carried out. And a data processing function is added into the module and is used for analyzing and processing the data collected from the new energy power distribution network in real time. The method also comprises independent supervision and adjacent cell interaction supervision, an early warning threshold is set, and when the electric power parameter of the key distribution point is monitored to exceed a preset fluctuation threshold, early warning can be automatically triggered.
S3: reading a monitoring target of a pre-monitoring task, performing analog quantity monitoring on a new energy power generation system based on the analog monitoring module, and evaluating an electric power index based on analog electric data, wherein the electric power index comprises a dynamic scheduling dimension and an electric quality dimension;
specifically, the monitoring target is the real-time scheduling capability and response speed of the new energy power generation system. The power generation system can monitor the power output condition of the new energy power generation system in real time through the simulation monitoring module, and evaluate the scheduling flexibility of the system according to the power grid requirement and the prediction data of the new energy power generation. The specific indexes comprise scheduling response time, and the time difference from the sending of the scheduling instruction to the actual response of the new energy power generation system reflects the response speed of the system. Output adjustment range: the new energy power generation system can adjust the output range in a short time, and the scheduling capability of the system is embodied. The electric quality dimension focuses on the electric energy quality output by the new energy power generation system. The electric energy quality is directly related to the stable operation of the power grid and the normal operation of the electric equipment. Specific indexes include voltage fluctuation and flicker, which may be caused by the output change of the new energy power generation system, and these all affect the stability of electric energy. Harmonic components possibly generated by the new energy power generation system, and the harmonic components can pollute the power grid and influence the normal operation of other electric equipment.
S4: traversing the key distribution points, randomly determining power check points and activating configured sensing monitoring equipment, wherein the power check points comprise at least two;
Specifically, all key distribution points identified are traversed. During the traversal, the position, current state and related parameters of each key point are recorded. At least two points are randomly selected from the key distribution points to serve as power check points. This ensures randomness and representativeness of the verification while reducing interference with the overall grid. The key point index is selected, for example, using a random number generator. At the selected power checkpoint, the configured sensing and monitoring device is activated. The sensing and monitoring equipment may include voltage and current transformers, power factor meters, power quality analyzers, etc. for real-time monitoring and recording of power parameters. Once the sensing and monitoring devices are activated, they will begin to collect and transmit real-time power data. The collected data is verified to verify its accuracy and integrity. This includes comparing data consistency between different sensors, checking whether the data is within a reasonable range, etc.
S5: returning sensing monitoring data based on the electric power check points, mapping the analog electric data, checking, and determining a data check result, wherein the check standard comprises inter-point check and analog check;
Specifically, power data, such as voltage, current, power factor, frequency, etc., is collected in real time at a power checkpoint by an activated sensing and monitoring device. The collected data is transmitted back to the data center or the control system in real time through a reliable communication network, such as wireless communication, an optical fiber network and the like. And the returned sensing monitoring data are corresponding to the analog electric data, so that the consistency and comparability of the data are ensured. And carrying out pretreatment operations such as cleaning, denoising, normalization and the like on the data so as to improve the data quality and analysis accuracy. The verification standard mainly comprises inter-point verification and analog verification. The check between points is to check whether the data time stamps of different check points are consistent so as to ensure that the data are collected at the same time point. And comparing the data among different check points, and checking whether the data have reasonable spatial correlation. For example, there should be some degree of correlation between the voltage or current data of adjacent checkpoints. It is checked whether there is an abnormal jump or break in the data sequence to ensure continuity of the data. The simulation verification is to compare the actual sensing monitoring data with the simulation electric data and check the coincidence degree between the two data. If the variance is too large, it may be indicative of a problem with the actual data. And reconstructing an operation scene of the power system based on the simulation data and the actual data to verify the rationality and the accuracy of the data. The error between the analog data and the actual data is analyzed to determine the source and magnitude of the error to assess the reliability of the data. And judging the accuracy and the reliability of the sensing and monitoring data according to the results of the inter-point check and the simulation check. If the data passes all of the check criteria, the data is considered valid.
S6: identifying the data verification result, judging whether a consistency tolerance interval is met, and determining a judgment characteristic value, wherein the consistency tolerance interval is a verification allowable deviation range;
Specifically, the consistency tolerance interval is set based on the accuracy requirement of the system and the history data. For example, for voltage measurements, the tolerance interval may be ±5% of the nominal voltage value. And comparing the actual measurement data of each check point with the analog data or the standard data, and calculating the deviation value. And checking whether the deviation of each check point exceeds a consistency tolerance interval. If the deviation is within the tolerance interval, the data of the point is considered to be consistent; if the deviation exceeds the tolerance interval, the data is considered to have inconsistency. The judging characteristic value is a quantization index and is used for indicating whether the data checking result meets the consistency requirement. This value may be a simple binary identification, such as 0 indicating unsatisfied, 1 indicating satisfied, or a specific percentage of deviation or other quantitative indicator. And calculating a judging characteristic value for each check point according to the comparison of the check result and the consistency tolerance interval. For example, if the data deviation of a certain check point is within a tolerance interval, the determination characteristic value may be 1; if the tolerance interval is exceeded, the eigenvalue may be 0 or a percentage value of deviation. And summarizing and outputting the judging characteristic values of all the check points.
S7: and based on the judging characteristic value, performing compensation adjustment of the electric power index as a real-time monitoring result.
Specifically, these determination characteristic values reflect the degree of deviation between the actual monitoring data and the analog data or the preset standard. The determination characteristic values include voltage deviation, current deviation, power factor deviation and the like, which are key indexes of compensation adjustment. And determining a corresponding compensation strategy according to the size and the direction of the judging characteristic value. For example, if the voltage monitoring value is low, forward compensation is required; if the voltage monitoring value is high, negative compensation is needed. The compensation strategy includes adjusting the taps of the transformer, changing the input or cut-out state of the capacitor, etc.
Further, the step S1 of the present application further includes:
Identifying the new energy power distribution network, dividing distribution points based on fluctuation grades, and determining a multi-level distribution point set;
Traversing the multi-level distribution point set by taking the maximum fluctuation level as the fluctuation threshold value, and identifying the key distribution points;
And traversing the multi-level distribution point set, and marking the new energy power distribution network by measuring the inter-level difference based on the fluctuation level, wherein the inter-level difference is the amplitude difference between each fluctuation level and the fluctuation threshold value.
Specifically, the new energy power distribution network includes key information such as a topological structure of the power grid, line impedance, transformer configuration, and positions and capacities of new energy access points. And analyzing the power fluctuation condition of each point in the power grid according to the historical data and the real-time monitoring data. Distribution points are divided into different levels of fluctuation based on the magnitude and frequency of the fluctuation. These levels may reflect the severity of the power fluctuations at each point and the impact on grid stability. After the wave level division is completed, a multi-level distribution point set is obtained. This set contains all distribution points in the grid and their corresponding fluctuation level information. Each stage represents a population of distribution points with similar fluctuation characteristics. The maximum fluctuation level is selected as the fluctuation threshold. This threshold represents the maximum power fluctuation range allowed in the grid. Then, a multi-level distribution point set is traversed, and distribution points with fluctuation levels reaching or exceeding the threshold value are identified as key distribution points. These key distribution points are the locations in the grid where the power fluctuations are the greatest and most significant to the stability of the grid. Finally, the multi-level distribution point set is traversed again, and the magnitude difference between each fluctuation level and the fluctuation threshold value, namely the inter-level difference, is calculated. The difference reflects the difference between the power fluctuation of each point and the maximum allowable fluctuation, and the new energy power distribution network can be marked according to the size of the inter-stage difference so as to intuitively display the power fluctuation condition of each region in the power network.
Further, the application also comprises:
Identifying the monitoring target, analyzing the distribution relevance of the monitoring partition, and determining a simulation monitoring mode, wherein the simulation monitoring mode comprises an independent mode and an associated assistant mode;
Based on the simulation monitoring mode, temporarily configuring the simulation monitoring module, and executing analog quantity monitoring on the new energy power generation system.
Specifically, the monitoring objective is which parts or parameters of the new energy power generation system need to be monitored. For example, the output voltage of the solar panel, the rotational speed of the wind power generator, etc. may be mentioned. The new energy power generation system may be distributed in different geographic locations or areas. For example, whether the power generation efficiency of some regions is affected by other regions, or whether there is some synergistic relationship between the respective regions. A suitable analog monitoring mode is determined. The system comprises a co-assist mode and an independent mode, wherein the independent mode is used for parts of the power generation system which are not strongly associated with other areas, and can be independently monitored and analyzed. The association co-assist mode is used for the part of the power generation system which has strong association with other areas, and the data of a plurality of areas need to be comprehensively considered for analysis. And carrying out corresponding configuration on the analog monitoring module according to the selected analog monitoring mode. Including setting the data acquisition frequency, selecting a particular sensor or monitoring device, etc. After the above configuration is completed, analog quantity monitoring is started to be performed on the new energy power generation system.
Further, step S5 of the present application further includes:
extracting analog electric data of the electric power check points, performing mapping check with the sensing monitoring data, and determining one-dimensional check results, wherein the one-dimensional check results at least comprise a first check result of a first check point and a second check result of a second check point;
Checking the first checking result and the second checking result, carrying out the same-frequency analysis of the checking result between points, and determining a two-dimensional checking result;
and integrating the one-dimensional check result and the two-dimensional check result to serve as the data check result.
Specifically, simulated electrical data for the power checkpoints is extracted from the power system, which is typically generated based on a system model and a predictive algorithm. And meanwhile, acquiring sensing monitoring data of corresponding check points, wherein the data are acquired in real time through sensors arranged on key nodes of the power grid. The analog electrical data and the sensing monitoring data are time synchronized, so that the analog electrical data and the sensing monitoring data are consistent in time scale. And respectively comparing the analog electric data of the first check point and the second check point with the sensing monitoring data, and calculating the difference or error between the analog electric data and the sensing monitoring data. Such differences may be manifested as deviations in values, phase differences, or inconsistencies in other power parameters. And judging one-dimensional check results of each check point, namely a first check result and a second check result, according to a preset error range or standard. And comparing the sensing monitoring data between the first check point and the second check point, and checking the correlation and consistency between the first check point and the second check point. And (3) carrying out spectrum analysis on the data of the two check points, and checking whether the amplitude and the phase on the same frequency component are consistent or similar. And integrating the one-dimensional check result, the first check result and the second check result with the two-dimensional check result. Such integration may be achieved by weighted averaging, voting mechanisms, or other data fusion techniques. The final data verification result is a comprehensive evaluation integrating a plurality of verification dimensions and verification points and is used for reflecting the accuracy and reliability of the power verification data.
Further, the application also comprises:
the consistency tolerance interval comprises a difference tolerance interval of analog electric data and sensing monitoring data and a same-frequency tolerance interval between the analog electric data and the sensing monitoring data and the electric power check point;
Identifying the data verification result, and if the one-dimensional verification result meets the difference tolerance interval and the two-dimensional verification result meets the same-frequency tolerance interval, determining that the judgment characteristic value is yes;
Otherwise, determining whether the judging characteristic value is negative, and marking the consistency difference.
Specifically, a margin interval of the difference defines a maximum allowable difference between the analog electrical data and the sensing monitor data. For example, a percentage or a specific numerical range may be set as the differential tolerance interval. And the same-frequency tolerance interval is used for measuring the synchronicity between the data of different power check points. It defines an acceptable phase difference or time delay range between different checkpoint data at the same frequency. And comparing the analog electric data with the sensing monitoring data, and calculating the difference between the analog electric data and the sensing monitoring data. If the delta falls within the delta tolerance interval, the one-dimensional verification result is considered to satisfy the requirement. The data of the different power checkpoints are analyzed for synchronicity, such as calculating a phase difference or a time delay. And if the parameters are in the same-frequency tolerance interval, considering that the two-dimensional verification result meets the requirement. If the one-dimensional check result satisfies the difference tolerance interval and the two-dimensional check result satisfies the same-frequency tolerance interval, the determination characteristic value is determined to be yes, which indicates that the data consistency is good. If the one-dimensional or two-dimensional verification result does not meet the corresponding consistency tolerance interval, the judgment characteristic value is determined to be NO, and the consistency difference is marked, namely the deviation degree of the actual difference and the tolerance interval is recorded.
Further, the application also comprises:
if the judging characteristic value is yes, taking the electric power index as the real-time monitoring result;
If the judging characteristic value is negative, measuring the difference degree between the analog electric data of the electric power check point and the analog electric data, and determining the data difference;
And determining mapping power index amplitude modulation based on the data difference, calibrating the power index, and determining a compensation power index as the real-time monitoring result.
Specifically, if the determination characteristic value is determined to be yes, it is interpreted that the consistency of the data is within an acceptable range. In this case, the power index corresponding to the sensing monitoring data of the power check point may be directly used as the real-time monitoring result. When the determination characteristic value is no, it is necessary to analyze the difference between the analog electrical data and the sensing monitor data in detail. By calculating the degree of difference, such as absolute difference, relative error, etc., between the two, a specific data difference can be obtained. Based on the calculated data delta, an amplitude modulation mapped to the power indicator is determined. This amplitude modulation reflects the amount of adjustment that is needed to adjust the power indicator due to data inconsistencies. The raw power indicator is calibrated using the mapped power indicator amplitude modulation. Including additive and subtractive adjustments to the power metrics to reflect the actual differences between the sensed monitoring data and the analog electrical data. The calibrated power index is referred to as a compensation power index. This index takes into account the data differences and makes corresponding adjustments to more accurately reflect the real-time status of the power system. And finally, outputting the compensation power index as a real-time monitoring result. This result takes into account the inconsistencies found during the data verification process and compensates accordingly, and is therefore more accurate and reliable than the original power indicator.
Further, the application also comprises:
Based on the mapping power index amplitude modulation, performing power index adjustment on the power check points, and determining a zone of calibration index;
identifying the inter-stage delta, and determining a multi-stage amplitude modulation standard by taking the amplitude modulation of the mapping power index as a base line, wherein the inter-stage delta is inversely related to the amplitude modulation standard;
Traversing the multi-level distribution point set to determine, adjusting the power index based on the multi-level amplitude modulation standard, and determining a two-region calibration index;
And integrating the first-region calibration index and the second-region calibration index to determine the compensation power index.
Specifically, the power index of the power checkpoint is adjusted. The adjustment includes increasing or decreasing the value of the power indicator to calibrate for inaccuracies due to data differences. The electric power index of the adjusted electric power check point is a zone calibration index. And analyzing the power fluctuation level difference, namely the inter-stage difference, between each point in the multi-stage distribution point set and the power check point. And determining the amplitude modulation standard of each point in the multi-level distribution point set according to the interstage difference by taking the amplitude modulation of the mapping power index as a base line. Since the inter-stage delta is inversely related to the amplitude modulation standard, i.e., the larger the inter-stage delta is, the smaller the amplitude modulation standard should be; and vice versa. Each point in the multi-level distribution point set is traversed. For each point in the multi-level distribution point set, the power index is adjusted according to its corresponding amplitude modulation standard. The electric power index concentrated by the adjusted multi-stage distribution points is the two-zone calibration index. And integrating the first-region calibration index and the second-region calibration index to form a comprehensive and calibrated electric power index system. The integrated power index system is the final compensation power index, and takes the data difference, the inter-stage difference and the corresponding adjustment strategy into consideration, so that the real-time state of the power system can be reflected more accurately.
Further, the application also comprises:
Identifying the real-time monitoring result and determining an abnormal power index;
performing traceability analysis and evolution prediction on the abnormal power indexes, determining power early warning information and warning;
and identifying the wind control grade of the electric power early warning information, and if the wind control threshold is met, traversing a new energy electric power plan library to determine a wind control plan, and carrying out emergency wind control management.
Specifically, the data is analyzed by a preset algorithm to identify power indexes which are not in compliance with the normal mode, and the indexes may be represented as sudden power fluctuation, voltage abnormality, frequency deviation and the like. These abnormality indexes are marked as abnormality power indexes. Tracing the abnormal power index and analyzing possible reasons for the abnormality. The status of the associated electrical equipment, external environmental factors such as weather, temperature, grid structure changes, etc. are checked. Data analysis tools, such as fault tree analysis, causal graphs, etc., are utilized to help locate the root cause of the anomaly. Based on the historical data and the current abnormality index, a prediction model such as time sequence analysis, machine learning model and the like is utilized to predict the future development trend of the abnormality power index. It is evaluated whether the anomaly may be amplified and may affect other grid parts. And formulating electric power early warning information according to the results of the traceability analysis and the evolution prediction. The pre-warning information should contain detailed descriptions of anomalies, possible impact ranges, predicted durations, etc. Early warning information is timely transmitted to relevant operation and maintenance personnel and decision makers through warning systems such as audible and visual warning, short message notification, email and the like. Different wind control levels, such as low, medium and high, are set, each corresponding to a different degree of urgency and treatment priority. And determining the corresponding wind control level according to the severity and the possible influence range of the electric power early warning information. If the wind control level of the electric power early warning information meets a preset wind control threshold, if the wind control level reaches a middle level or a high level, triggering an emergency wind control management flow. And traversing a new energy power plan library, and searching a wind control plan matched with the current abnormal situation. Emergency treatment is carried out by organizing personnel rapidly according to the plan, such as isolating fault areas, adjusting the running mode of a power grid, starting a standby power supply and the like.
In summary, the real-time monitoring method for new energy power provided by the application has the following technical effects:
Determining a new energy power distribution network to be monitored, and identifying key distribution points meeting a fluctuation threshold, wherein the fluctuation threshold is a critical value based on interference influence degree; based on the new energy power distribution network and the key distribution points, building an analog monitoring module; based on the simulation monitoring module, performing analog quantity monitoring on a new energy power generation system, and evaluating an electric power index based on analog electric data, wherein the electric power index comprises a dynamic scheduling dimension and an electric quality dimension; traversing the key distribution points, randomly determining power check points and activating configured sensing monitoring equipment, wherein the power check points comprise at least two; returning sensing monitoring data based on the electric power check points, mapping the analog electric data, checking, and determining a data check result, wherein the check standard comprises inter-point check and analog check; identifying the data verification result, judging whether a consistency tolerance interval is met, and determining a judgment characteristic value, wherein the consistency tolerance interval is a verification allowable deviation range; based on the judging characteristic value, the compensation adjustment of the electric power index is carried out, and the electric power index is used as a real-time monitoring result, so that the problems that key distribution points cannot be accurately positioned, analog quantity monitoring is difficult to carry out, the data verification mode is single in the prior art, the monitoring result is inaccurate, the comprehensive and accurate monitoring of a new energy electric power distribution network is realized, the risk of power grid faults is effectively reduced, and the stable operation of a new energy electric power system is ensured.
Embodiment two:
Based on the same inventive concept as the real-time monitoring method for new energy power in the foregoing embodiment, the present application also provides a real-time monitoring system for new energy power, referring to fig. 2, the system includes:
The key distribution point acquisition unit 11 is used for determining a new energy power distribution network to be monitored in advance, identifying key distribution points meeting a fluctuation threshold, wherein the fluctuation threshold is a critical value based on interference influence degree, and the simulation monitoring module has partition relatively independent supervision and adjacent area interactive supervision;
The simulation monitoring module building unit 12 is used for reading a monitoring target of a pre-monitoring task and building a simulation monitoring module based on the new energy power distribution network and the key distribution points;
The analog quantity monitoring unit 13 is used for performing analog quantity monitoring on the new energy power generation system based on the analog monitoring module, and evaluating an electric power index based on analog electric data, wherein the electric power index comprises a dynamic scheduling dimension and an electric quality dimension;
A sensing monitoring device determining unit 14, where the sensing monitoring device determining unit 14 is configured to traverse the key distribution points, randomly determine power check points, and activate configured sensing monitoring devices, where the power check points include at least two;
The verification result determining unit 15 is used for returning sensing monitoring data based on the electric power verification points, mapping the analog electric data and performing verification to determine a data verification result, wherein the verification standard comprises inter-point verification and analog verification;
a determination feature value obtaining unit 16, where the determination feature value obtaining unit 16 is configured to identify the data verification result, determine whether a consistency tolerance interval is satisfied, and determine a determination feature value, where the consistency tolerance interval is a verification allowable deviation range;
And a monitoring result obtaining unit 17, where the monitoring result obtaining unit 17 is configured to perform compensation adjustment of the power index based on the determination feature value, as a real-time monitoring result.
Further, the key distribution point obtaining unit 11 in the system is further configured to:
Identifying the new energy power distribution network, dividing distribution points based on fluctuation grades, and determining a multi-level distribution point set;
Traversing the multi-level distribution point set by taking the maximum fluctuation level as the fluctuation threshold value, and identifying the key distribution points;
And traversing the multi-level distribution point set, and marking the new energy power distribution network by measuring the inter-level difference based on the fluctuation level, wherein the inter-level difference is the amplitude difference between each fluctuation level and the fluctuation threshold value.
Further, the system also comprises a pseudo-quantity monitoring module, wherein the pseudo-quantity monitoring module is used for:
Identifying the monitoring target, analyzing the distribution relevance of the monitoring partition, and determining a simulation monitoring mode, wherein the simulation monitoring mode comprises an independent mode and an associated assistant mode;
Based on the simulation monitoring mode, temporarily configuring the simulation monitoring module, and executing analog quantity monitoring on the new energy power generation system.
Further, the verification result determining unit 15 in the system is further configured to:
extracting analog electric data of the electric power check points, performing mapping check with the sensing monitoring data, and determining one-dimensional check results, wherein the one-dimensional check results at least comprise a first check result of a first check point and a second check result of a second check point;
Checking the first checking result and the second checking result, carrying out the same-frequency analysis of the checking result between points, and determining a two-dimensional checking result;
and integrating the one-dimensional check result and the two-dimensional check result to serve as the data check result.
Further, the system also comprises a verification result identification unit, wherein the verification result identification unit is used for:
the consistency tolerance interval comprises a difference tolerance interval of analog electric data and sensing monitoring data and a same-frequency tolerance interval between the analog electric data and the sensing monitoring data and the electric power check point;
Identifying the data verification result, and if the one-dimensional verification result meets the difference tolerance interval and the two-dimensional verification result meets the same-frequency tolerance interval, determining that the judgment characteristic value is yes;
Otherwise, determining whether the judging characteristic value is negative, and marking the consistency difference.
Further, the system further includes a compensation power index determination unit for:
if the judging characteristic value is yes, taking the electric power index as the real-time monitoring result;
If the judging characteristic value is negative, measuring the difference degree between the analog electric data of the electric power check point and the analog electric data, and determining the data difference;
And determining mapping power index amplitude modulation based on the data difference, calibrating the power index, and determining a compensation power index as the real-time monitoring result.
Further, the two-zone calibration index determining unit in the system is used for:
Based on the mapping power index amplitude modulation, performing power index adjustment on the power check points, and determining a zone of calibration index;
identifying the inter-stage delta, and determining a multi-stage amplitude modulation standard by taking the amplitude modulation of the mapping power index as a base line, wherein the inter-stage delta is inversely related to the amplitude modulation standard;
Traversing the multi-level distribution point set to determine, adjusting the power index based on the multi-level amplitude modulation standard, and determining a two-region calibration index;
And integrating the first-region calibration index and the second-region calibration index to determine the compensation power index.
Further, the system also comprises a wind control management unit, wherein the wind control management unit is used for:
Identifying the real-time monitoring result and determining an abnormal power index;
performing traceability analysis and evolution prediction on the abnormal power indexes, determining power early warning information and warning;
and identifying the wind control grade of the electric power early warning information, and if the wind control threshold is met, traversing a new energy electric power plan library to determine a wind control plan, and carrying out emergency wind control management.
In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the foregoing real-time monitoring method and specific example for new energy power in the first embodiment of fig. 1 are also applicable to a real-time monitoring system for new energy power in this embodiment, and by the foregoing detailed description of the real-time monitoring method for new energy power, those skilled in the art can clearly know that the real-time monitoring system for new energy power in this embodiment is not described in detail herein for brevity of the specification. For the system disclosed in the embodiment, since the system corresponds to the method disclosed in the embodiment, the description is simpler, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and the equivalent techniques thereof, the present application is also intended to include such modifications and variations.
Claims (6)
1. A method for real-time monitoring of new energy power, the method comprising:
Determining a new energy power distribution network which is monitored in advance, and identifying key distribution points which meet a fluctuation threshold, wherein the fluctuation threshold is a critical value based on interference influence degree;
Based on the new energy power distribution network and the key distribution points, building a simulation monitoring module, wherein the simulation monitoring module has partition relatively independent supervision and adjacent area interactive supervision;
Reading a monitoring target of a pre-monitoring task, performing analog quantity monitoring on a new energy power generation system based on the analog monitoring module, and evaluating an electric power index based on analog electric data, wherein the electric power index comprises a dynamic scheduling dimension and an electric quality dimension;
Traversing the key distribution points, randomly determining power check points and activating configured sensing monitoring equipment, wherein the power check points comprise at least two;
Returning sensing monitoring data based on the electric power check points, mapping the analog electric data, checking, and determining a data check result, wherein the check standard comprises inter-point check and analog check;
Identifying the data verification result, judging whether a consistency tolerance interval is met, and determining a judgment characteristic value, wherein the consistency tolerance interval is a verification allowable deviation range;
based on the judging characteristic value, performing compensation adjustment on the electric power index to serve as a real-time monitoring result;
wherein said mapping and verifying said analog electrical data comprises:
extracting analog electric data of the electric power check points, performing mapping check with the sensing monitoring data, and determining one-dimensional check results, wherein the one-dimensional check results at least comprise a first check result of a first check point and a second check result of a second check point;
Checking the first checking result and the second checking result, carrying out the same-frequency analysis of the checking result between points, and determining a two-dimensional checking result;
Integrating the one-dimensional check result and the two-dimensional check result to serve as the data check result;
Wherein the determining whether the consistency tolerance interval is satisfied or not, determining the determination characteristic value includes:
the consistency tolerance interval comprises a difference tolerance interval of analog electric data and sensing monitoring data and a same-frequency tolerance interval between the analog electric data and the sensing monitoring data and the electric power check point;
Identifying the data verification result, and if the one-dimensional verification result meets the difference tolerance interval and the two-dimensional verification result meets the same-frequency tolerance interval, determining that the judgment characteristic value is yes;
Otherwise, determining whether the judging characteristic value is negative, and marking the consistency difference;
Wherein the performing compensation adjustment of the power index based on the determination feature value includes:
if the judging characteristic value is yes, taking the electric power index as the real-time monitoring result;
If the judging characteristic value is negative, measuring the difference degree of the sensing monitoring data of the power check point and the analog electric data, and determining the data difference;
And determining mapping power index amplitude modulation based on the data difference, calibrating the power index, and determining a compensation power index as the real-time monitoring result.
2. The method of claim 1, wherein the identifying key distribution points that meet a fluctuation threshold, the method further comprising:
Identifying the new energy power distribution network, dividing distribution points based on fluctuation grades, and determining a multi-level distribution point set;
Traversing the multi-level distribution point set by taking the maximum fluctuation level as the fluctuation threshold value, and identifying the key distribution points;
And traversing the multi-level distribution point set, and marking the new energy power distribution network by measuring the inter-level difference based on the fluctuation level, wherein the inter-level difference is the amplitude difference between each fluctuation level and the fluctuation threshold value.
3. The method of claim 1, wherein the monitoring target is a monitoring demand of at least one section of the new energy power distribution network, the method further comprising:
Identifying the monitoring target, analyzing the distribution relevance of the monitoring partition, and determining a simulation monitoring mode, wherein the simulation monitoring mode comprises an independent mode and an associated assistant mode;
Based on the simulation monitoring mode, temporarily configuring the simulation monitoring module, and executing analog quantity monitoring on the new energy power generation system.
4. The method of claim 2, wherein determining a mapped power indicator amplitude modulation based on the data delta, calibrating the power indicator, the method further comprising:
Based on the mapping power index amplitude modulation, performing power index adjustment on the power check points, and determining a zone of calibration index;
identifying the inter-stage delta, and determining a multi-stage amplitude modulation standard by taking the amplitude modulation of the mapping power index as a base line, wherein the inter-stage delta is inversely related to the amplitude modulation standard;
Traversing the multi-level distribution point set to determine, adjusting the power index based on the multi-level amplitude modulation standard, and determining a two-region calibration index;
And integrating the first-region calibration index and the second-region calibration index to determine the compensation power index.
5. The method of claim 1, wherein after obtaining the real-time monitoring result, the method further comprises:
Identifying the real-time monitoring result and determining an abnormal power index;
performing traceability analysis and evolution prediction on the abnormal power indexes, determining power early warning information and warning;
and identifying the wind control grade of the electric power early warning information, and if the wind control threshold is met, traversing a new energy electric power plan library to determine a wind control plan, and carrying out emergency wind control management.
6. A real-time monitoring system for new energy power, characterized by the steps for implementing the method according to any one of claims 1 to 5, said system comprising:
the system comprises a key distribution point acquisition unit, a power distribution unit and a power distribution unit, wherein the key distribution point acquisition unit is used for determining a new energy power distribution network which is monitored in advance, and identifying key distribution points which meet a fluctuation threshold, and the fluctuation threshold is a critical value based on interference influence degree;
The simulation monitoring module building unit is used for building a simulation monitoring module based on the new energy power distribution network and the key distribution points, and the simulation monitoring module has partition relatively independent supervision and adjacent area interactive supervision;
The analog quantity monitoring unit is used for reading a monitoring target of a pre-monitoring task, performing analog quantity monitoring on a new energy power generation system based on the analog monitoring module, and evaluating an electric power index based on analog electric data, wherein the electric power index comprises a dynamic scheduling dimension and an electric quality dimension;
The sensing monitoring equipment determining unit is used for traversing the key distribution points, randomly determining power check points and activating the configured sensing monitoring equipment, wherein the power check points comprise at least two;
The verification result determining unit is used for returning sensing monitoring data based on the power verification points, mapping the analog power data and performing verification to determine a data verification result, wherein the verification standard comprises inter-point verification and analog verification;
The judging characteristic value acquisition unit is used for identifying the data verification result, judging whether a consistency tolerance interval is met or not, and determining a judging characteristic value, wherein the consistency tolerance interval is a verification allowable deviation range;
And the monitoring result acquisition unit is used for carrying out compensation adjustment on the electric power index based on the judging characteristic value to serve as a real-time monitoring result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410565788.4A CN118157325B (en) | 2024-05-09 | 2024-05-09 | Real-time monitoring method and system for new energy power |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410565788.4A CN118157325B (en) | 2024-05-09 | 2024-05-09 | Real-time monitoring method and system for new energy power |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118157325A CN118157325A (en) | 2024-06-07 |
| CN118157325B true CN118157325B (en) | 2024-07-30 |
Family
ID=91298847
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410565788.4A Active CN118157325B (en) | 2024-05-09 | 2024-05-09 | Real-time monitoring method and system for new energy power |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118157325B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2882796A1 (en) * | 2007-05-16 | 2009-02-12 | Power Analytics Corporation | Real-time predictive systems for intelligent energy monitoring and management of electrical power networks |
| CN104092297A (en) * | 2014-06-24 | 2014-10-08 | 国家电网公司 | Monitoring system and method for monitoring running performance of power grid system in real time |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112836921A (en) * | 2020-12-02 | 2021-05-25 | 国网浙江省电力有限公司 | Data monitoring analysis and optimization method based on power grid micro-data fusion |
| CN114362212B (en) * | 2022-03-21 | 2022-06-10 | 北京鼎诚鸿安科技发展有限公司 | Multi-type power grid load resource data processing and adjusting method and system |
| CN116207845B (en) * | 2022-11-28 | 2024-05-28 | 无锡广盈集团有限公司 | Automatic monitoring method and system for protecting power equipment |
| CN117251812A (en) * | 2023-09-22 | 2023-12-19 | 河南博兆电子科技有限公司 | High-voltage power line operation fault detection method based on big data analysis |
-
2024
- 2024-05-09 CN CN202410565788.4A patent/CN118157325B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2882796A1 (en) * | 2007-05-16 | 2009-02-12 | Power Analytics Corporation | Real-time predictive systems for intelligent energy monitoring and management of electrical power networks |
| CN104092297A (en) * | 2014-06-24 | 2014-10-08 | 国家电网公司 | Monitoring system and method for monitoring running performance of power grid system in real time |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118157325A (en) | 2024-06-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109146093B (en) | Power equipment field investigation method based on learning | |
| CN109298379B (en) | Data monitoring-based intelligent electric meter field error abnormity identification method | |
| CN116069079A (en) | Intelligent heat dissipation control method and system for intelligent switch cabinet | |
| CN117808456B (en) | Equipment fault early warning method and device based on intelligent operation management | |
| CN117590159A (en) | Tunnel cable power supply state monitoring method and system based on deep learning | |
| CN110221976B (en) | Quantitative evaluation method for quality of metering terminal software based on measurement technology | |
| CN113239132A (en) | Online out-of-tolerance identification method for voltage transformer | |
| CN117060409A (en) | Automatic detection and analysis method and system for power line running state | |
| CN116914917A (en) | Big data-based monitoring and management system for operation state of power distribution cabinet | |
| CN117674249A (en) | Distributed photovoltaic-containing power distribution network fault self-healing control and evaluation method | |
| CN112580858A (en) | Equipment parameter prediction analysis method and system | |
| CN114400776B (en) | Digital mirror image-based substation automation equipment state diagnosis method and system | |
| CN116840767A (en) | Electric energy metering device overall error assessment method and device, storage medium and terminal | |
| CN118070199A (en) | Power equipment detection method and detection system | |
| CN117977816A (en) | Intelligent power supply system for electric power safety | |
| CN118226179A (en) | Distribution network automatic relay protection test system | |
| CN118157325B (en) | Real-time monitoring method and system for new energy power | |
| CN117951633B (en) | Photovoltaic power generation equipment fault diagnosis method and system | |
| CN118731829B (en) | Digital twinning electric energy meter abnormity monitoring method | |
| CN117272844B (en) | Method and system for predicting service life of distribution board | |
| CN118604681B (en) | Short circuit diagnosis method and system based on 4G Internet of things | |
| CN118473097B (en) | Intelligent line loss detection and alarm method and device for power distribution network | |
| CN117031211B (en) | Fault diagnosis method for power grid of transformer area | |
| CN118521082A (en) | Distribution network planning system based on distribution network frame | |
| CN118839866A (en) | Power line state evaluation method and system based on artificial intelligence |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |