CN118826294A

CN118826294A - Operation and maintenance digital management method and system based on transformer substation

Info

Publication number: CN118826294A
Application number: CN202411269952.3A
Authority: CN
Inventors: 姜立丹; 洪万沈; 刘田; 于洋; 付春龙
Original assignee: Shenyang Brothers Electronic Technology Co ltd
Current assignee: Shenyang Brothers Electronic Technology Co ltd
Priority date: 2024-09-11
Filing date: 2024-09-11
Publication date: 2024-10-22

Abstract

The invention relates to an operation and maintenance digital management method and system based on a transformer substation, and relates to the technical field of transformer substations, wherein the method comprises the following steps: collecting operation data of all equipment of a transformer substation in real time; cleaning and preprocessing original operation data to obtain preprocessed data; based on the preprocessed data, evaluating the states of all the devices through a device health evaluation model to obtain health scores of all the devices; analyzing the historical operation data of each device through a time sequence analysis method and a machine learning model to predict potential device faults and obtain fault prediction results of each device; generating an operation and maintenance plan and an operation and maintenance priority of each device according to the health degree score and the fault prediction result of each device; when the health degree score or the fault probability of any one device meets the preset condition, automatically triggering an alarm and providing corresponding maintenance suggestions and emergency measures for the device triggering the alarm. The invention can improve the accuracy and efficiency of operation and maintenance of the transformer substation.

Description

Operation and maintenance digital management method and system based on transformer substation

Technical Field

The invention relates to the technical field of substations, in particular to an operation and maintenance digital management method, system, electronic equipment and non-transitory computer readable storage medium based on a substation.

Background

At present, in the operation and maintenance process of the transformer substation, manual inspection, periodic maintenance and fault treatment are mainly relied on. The operator typically checks the operating state of the equipment in the field, records parameters and anomalies, and performs maintenance and repair of the equipment according to a predetermined maintenance schedule. In addition, operation and maintenance personnel can rely on experience and intuition to judge the health condition of equipment and take emergency measures when the equipment breaks down. The normal operation of the transformer substation is guaranteed to a certain extent by the mode, and particularly when the equipment has obvious faults, the equipment can be processed in time, so that the risk of power failure is reduced.

However, manual inspection and recording is susceptible to human factors, which may result in missing or inaccurate data. Second, the manner in which periodic maintenance is often based on time periods rather than actual operating conditions of the equipment may result in unnecessary maintenance or failure to discover potential faults in time. In addition, experience and intuition have strong dependence, and accurate data support cannot be provided, so that the accuracy of fault diagnosis and prediction is limited.

With the expansion of the power grid scale and the increase of the complexity, the traditional operation and maintenance method of the transformer substation has difficulty in meeting the requirements of the modern transformer substation on high efficiency, safety and precision operation and maintenance, and a management method based on a digital technology is needed to improve the operation and maintenance efficiency and the management level.

Disclosure of Invention

Aiming at the technical problems in the prior art, the invention provides a substation-based operation and maintenance digital management method, a system, electronic equipment and a non-transitory computer readable storage medium, which can improve the accuracy and efficiency of operation and maintenance of a substation.

The technical scheme for solving the technical problems is as follows:

The invention provides an operation and maintenance digital management method based on a transformer substation, which comprises the following steps:

collecting operation data of all equipment of a transformer substation in real time through a sensor network;

Cleaning and preprocessing the original operation data to obtain preprocessed data;

Based on the preprocessed data, evaluating the state of each device through a device health evaluation model to obtain health scores of each device;

analyzing the historical operation data of each device through a time sequence analysis method and a machine learning model to predict potential device faults and obtain fault prediction results of each device;

generating an operation and maintenance plan and an operation and maintenance priority of each device according to the health degree score and the fault prediction result of each device;

when the health degree score or the fault probability of any one device meets the preset condition, automatically triggering an alarm and providing corresponding maintenance suggestions and emergency measures for the device triggering the alarm.

Optionally, the evaluating, based on the preprocessed data, the state of each device through a device health evaluation model to obtain a health score of each device includes:

acquiring the current power, the current temperature, the vibration intensity and the running acceleration of each device;

Acquiring rated power, normal working temperature, allowable highest temperature, allowable lowest temperature, critical vibration intensity and running acceleration of each device;

And processing the current power, the current temperature, the vibration intensity and the running acceleration of the equipment based on the rated power, the normal working temperature, the allowable highest temperature, the allowable lowest temperature, the critical vibration intensity and the running acceleration of each equipment through the equipment health evaluation model to obtain processing results of each type of parameters, and carrying out weighted summation on the processing results by combining the first weight, the second weight, the third weight and the fourth weight to obtain health scores of each equipment.

Optionally, the health score for each of the devices is expressed as:

；

wherein, Is the health score for the i-th device,Is the current power of the i-th device,Is the rated power of the device and,Is the current temperature of the i-th device,Is the normal operating temperature of the device,Is the highest temperature allowed by the device and,Is the minimum temperature allowed by the device and,Is the intensity of the vibration of the device,Is the critical vibration intensity of the device,Is the acceleration of the operation of the device,Is the maximum acceleration at which the device is operated,The first weight, the second weight, the third weight and the fourth weight of the health degree model are respectively.

Optionally, analyzing the historical operation data of each device through a time sequence analysis method and a machine learning model to predict potential device faults, so as to obtain a fault prediction result of each device, including:

acquiring time points at which historical faults of the equipment occur and weight of each time point;

Obtaining an influence coefficient of each potential failure mode of the equipment and a linear expression of each potential failure mode;

And determining a fault detection result of the equipment according to the time point of the historical fault occurrence of the equipment, the weight of each time point, the influence coefficient of each potential fault mode and the linear expression of each potential fault mode.

Optionally, the failure probability of the device is expressed as:

;

wherein, Is the probability of failure of the device at the current time t,Is the jth point in time when the historical fault occurs,Is the weight of the j-th point in time,Is the width parameter at the j-th time point, M is the number of fault events in the history data,The coefficient of influence of the kth potential failure mode,Is a linear expression of the kth latent fault mode, and Y is the number of latent fault modes.

Optionally, the generating the operation and maintenance priority of each device according to the health degree score and the fault prediction result of each device includes:

acquiring the fault probability of the equipment and the moment corresponding to the fault probability;

according to the moment corresponding to the probability and the Lagrangian function, processing to obtain a first derivative and a second derivative of the Lagrangian function;

And determining the operation and maintenance priority of the equipment according to the health degree score, the fault probability and the first derivative and the second derivative of the Lagrangian function of the equipment.

Optionally, the operation and maintenance priority of each device is expressed as:

;

wherein, Is the operation and maintenance priority of the i-th device,Is the health score for the i-th device,Is the probability of failure of the device at the current time t,The first influence coefficient, the second influence coefficient and the third influence coefficient, respectively, L is a lagrangian function.

Optionally, when the health degree score or the fault probability of any one device meets a preset condition, automatically triggering an alarm and providing corresponding maintenance advice and emergency measures for the device triggering the alarm, including:

acquiring a health degree scoring threshold value and a fault probability threshold value;

When the health degree score of any device is smaller than the health degree score threshold value or the fault probability is larger than the fault probability threshold value, the device meets the preset condition, the alarm operation for the device is triggered, and corresponding maintenance suggestions and emergency measures are provided for the device.

Optionally, the method further comprises:

acquiring a historical data report and a maintenance record of the running state of the equipment;

A plurality of time periods counted from a historical data report of the device, and a historical health score corresponding to each of the time periods;

calculating an average value of the historical health scores of the equipment according to a plurality of the time periods and the historical health score of each time period;

And analyzing the historical health score average value, and determining standby maintenance suggestions for the equipment.

The invention also provides a substation-based operation and dimension digital management system, which comprises:

The data acquisition module is used for acquiring the operation data of each device of the transformer substation in real time through the sensor network;

The data processing module is used for cleaning and preprocessing the original operation data to obtain preprocessed data;

the equipment scoring module is used for evaluating the state of each piece of equipment through the equipment health degree evaluation model based on the preprocessed data to obtain health degree scores of the pieces of equipment;

The fault prediction module is used for analyzing the historical operation data of each device through a time sequence analysis method and a machine learning model so as to predict potential device faults and obtain a fault prediction result of each device;

the operation and maintenance planning module is used for generating an operation and maintenance plan and an operation and maintenance priority of each device according to the health degree score and the fault prediction result of each device;

And the operation and maintenance execution module is used for automatically triggering an alarm and providing corresponding maintenance suggestions and emergency measures for the equipment triggering the alarm when the health degree score or the fault probability of any equipment meets the preset condition.

In addition, to achieve the above object, the present invention also proposes an electronic device including: a memory for storing a computer software program; and the processor is used for reading and executing the computer software program so as to realize the operation and maintenance digital management method based on the transformer substation.

In addition, in order to achieve the above object, the present invention also proposes a non-transitory computer readable storage medium, in which a computer software program is stored, which when executed by a processor, implements a substation-based operation and maintenance digital management method as described above.

The beneficial effects of the invention are as follows:

(1) The invention can dynamically evaluate the health condition and the fault risk of the equipment by comprehensively considering the real-time operation data (such as power, temperature, vibration and acceleration) and the historical fault data of the equipment. Based on the method, the generated operation and maintenance priority can accurately guide operation and maintenance personnel to conduct targeted equipment maintenance, unnecessary maintenance work is reduced, and operation and maintenance efficiency is improved;

(2) The invention adopts complex mathematical models (such as Gaussian distribution, logistic regression and Lagrange optimization) to more accurately predict the potential faults of the equipment, combines the analysis of historical fault data and potential fault modes, so that the fault prediction is more comprehensive and reliable, is beneficial to discovering and processing hidden dangers in advance, and avoids shutdown or accidents caused by sudden faults;

(3) According to the invention, through digital management and automatic analysis, interference of human factors is reduced, objectivity of data and accuracy of judgment are ensured, and therefore, scientificity of operation and maintenance decision is improved.

(4) According to the invention, the Lagrangian function and the derivative thereof are introduced, so that the operation and maintenance priority can be dynamically adjusted, and the change trend and the acceleration of the system state along with time are considered, so that the method is not only suitable for evaluating the current state, but also can be used for pre-judging the future trend, thereby realizing more flexible and prospective operation and maintenance decision.

(5) According to the invention, through accurate operation and maintenance priority evaluation, operation and maintenance resources can be reasonably allocated, limited resources are preferentially used for equipment needing emergency maintenance, and resource waste is avoided. Meanwhile, the optimal configuration is beneficial to prolonging the service life of equipment and reducing the overall operation and maintenance cost.

(6) The invention can discover and process equipment faults in time through comprehensive monitoring, accurate evaluation and effective prediction, reduce downtime and ensure stable operation of the transformer substation. Meanwhile, the probability of occurrence of sudden faults can be reduced by preventive maintenance measures, and the overall safety of the system is improved.

In conclusion, the invention remarkably improves the operation and maintenance efficiency and safety of the transformer substation through digital and intelligent operation and maintenance management, reduces the cost and risk, and has remarkable practical value and popularization prospect.

Drawings

Fig. 1 is a block diagram of a substation-based operation and maintenance digital management method;

FIG. 2 is a flow chart of a substation-based operation and maintenance digital management method provided by the invention;

fig. 3 is a schematic structural diagram of a substation-based operation and dimension digitizing management system;

fig. 4 is a schematic hardware structure of one possible electronic device according to the present invention;

fig. 5 is a schematic hardware structure of a possible computer readable storage medium according to the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

In the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.

In the description of the present invention, the term "for example" is used to mean "serving as an example, instance, or illustration. Any embodiment described as "for example" in this disclosure is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the invention. In the following description, details are set forth for purposes of explanation. It will be apparent to one of ordinary skill in the art that the present invention may be practiced without these specific details. In other instances, well-known structures and processes have not been described in detail so as not to obscure the description of the invention with unnecessary detail. Thus, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

Referring to fig. 1, fig. 1 is a block diagram of an operation and maintenance digital management method based on a transformer substation. As shown in fig. 1, the terminal 10 and the server 20 are connected through a network, for example, a wired or wireless network connection. The terminal 10 may include, but is not limited to, mobile terminals such as mobile phones and tablets, and fixed terminals such as computers, inquiries, and advertising players, in which applications of various network platforms are installed. The server 20 provides various business services for users, including a service push server, a user recommendation server, and the like.

It should be noted that, the block diagram of the operation and maintenance digital management method based on the transformer substation shown in fig. 1 is only an example, and the terminal 10, the server 20 and the application scenario described in the embodiment of the present invention are for more clearly describing the technical solution of the embodiment of the present invention, and do not generate the limitation of the technical solution provided by the embodiment of the present invention, and as a person of ordinary skill in the art can know that, with the evolution of the system and the appearance of a new service scenario, the technical solution provided by the embodiment of the present invention is applicable to similar technical problems.

Wherein the terminal 10 may be configured to:

Referring to fig. 2, a flowchart of a substation-based operation and maintenance digital management method of the present invention is provided, including the following steps:

and step 201, collecting operation data of all equipment of the transformer substation in real time through a sensor network.

In some embodiments, multiple types of sensors are deployed on each critical device of the substation, such as temperature sensors, vibration sensors, current sensors, acceleration sensors, and the like.

Each sensor is responsible for monitoring specific equipment parameters, ensuring that the operating state of the equipment can be covered over the whole area. For example, a temperature sensor is used to monitor the temperature of the device, a vibration sensor monitors the vibration condition of the mechanical part, and an acceleration sensor is used to detect the movement state of the device.

In some embodiments, the sensor network is capable of continuously and uninterruptedly collecting operational data of the device and transmitting to the central system immediately after collection. The timeliness ensures the timeliness of the data and can reflect the current state of the equipment. The data acquisition frequency can be adjusted according to the importance and operating conditions of the device, and the acquisition frequency of important devices or key parameters can be higher so as to ensure that any abnormal changes can be captured in time.

In some embodiments, the sensor network transmits the collected data to a central monitoring system, typically by wired or wireless communication. Common communication technologies include industrial ethernet, wireless Sensor Network (WSN), or internet of things (IoT) technologies. The data in the transmission process should be encrypted and checked to ensure the integrity and the safety of the data and prevent the data from being disturbed or tampered in the transmission process.

In some embodiments, the central monitoring system receives the sensor data and processes and stores it in real time. These data can be used for subsequent health assessment, fault prediction and operation and maintenance priority calculation. The data storage usually adopts a database or cloud storage mode so as to call and analyze the historical data at any time, and provides basis for long-term trend analysis of the running condition of the equipment.

In some embodiments, the central monitoring system may make timely decisions, such as triggering alarms, scheduling maintenance personnel, or adjusting operating parameters, based on the current status of the device, via real-time data collected by the sensor network. The data also provides a basis for health assessment, fault prediction and operation and maintenance priority calculation in the invention, and the scientificity and the accuracy of decision making are ensured.

In summary, the main function of the real-time data acquisition mode is to ensure that the running condition of the substation equipment can be continuously monitored and timely mastered, and the support system can rapidly take countermeasures when an abnormality or potential fault is found. The method improves the automation level of equipment management, reduces the dependence on manual inspection, and improves the overall operation safety and reliability of the system.

Step 202, cleaning and preprocessing the original operation data to obtain preprocessed data.

In some embodiments, raw data that may be collected from a sensor network may contain various problems such as noise, outliers, data loss, or duplicate recordings. These problems may arise due to sensor failures, environmental disturbances, network transmission errors, etc. Raw data is directly used for analysis without treatment, which may lead to inaccuracy and even errors of results, and therefore data cleaning and preprocessing are required.

In some embodiments, the data may be smoothed to eliminate random noise due to sensor accuracy limitations or environmental disturbances. For example, the data may be smoothed using weighted average filtering, moving average, or Kalman filtering, to obtain a more realistic measurement. Abnormal values, such as values that deviate extremely from the normal range, are detected and corrected. These outliers may be caused by sensor failures or short-lived external disturbances. The processing method includes replacement with an average value, a median value or a nearest normal value. Missing values may occur in the data acquisition, which may be due to a short sensor failure or communication disruption. Common data completion methods include interpolation (e.g., linear interpolation, spline interpolation) or predictive filling based on historical data.

In some embodiments, to eliminate dimensional differences between different devices or different parameters, the data is normalized or normalized. For example, temperature data may be normalized by subtracting the mean value divided by the standard deviation, making the data distribution more suitable for subsequent statistical analysis or machine learning models. For time series data with significant trend or periodicity, the data may be preprocessed by a trending or decycling method so that the data is more prone to capture short term fluctuations or anomalies. For example, a difference method or wavelet transform is used to remove the trend component. The data can be further smoothed, eliminating high frequency fluctuations, making the data smoother and more stable. Common methods include weighted moving average, exponential smoothing, and the like.

In some embodiments, the cleaned and preprocessed data is more reliable and accurate, eliminating noise, outliers and other interference factors, and has better continuity and stability. The preprocessed data provides a solid foundation for subsequent health assessment, fault prediction and operation and maintenance priority calculation, and the accuracy of analysis results and the scientificity of decisions are ensured.

By cleaning and preprocessing the original operation data, the invention can ensure the quality of the data, thereby improving the accuracy and reliability of the subsequent analysis. The preprocessed data reflects the running condition of the equipment more truly, is helpful for accurately evaluating the health degree of the equipment, predicting the fault risk and reasonably arranging the maintenance plan. The process reduces errors caused by human or environmental factors in the data, so that the decision based on the data is more reliable, and the overall operation efficiency and the safety of the system are improved.

And 203, evaluating the state of each device through a device health evaluation model based on the preprocessed data to obtain health scores of each device.

In some embodiments, step 203 may comprise the steps of:

In some embodiments, the health score for each of the devices is expressed as:

;

In a specific implementation of the present invention,Measuring the current power of a deviceRated power of equipmentThe effect of the ratio of (2) on the health of the device. When (when)Proximity toWhen the ratio is close to 1, the equipment works according to the design load, and the health degree is good; if it isFar below or aboveThe ratio will be far from 1 and the health score will decrease.

The nonlinear influence of power on the health degree is enhanced by adopting the third power, namely, the larger the power deviation is, the more remarkable the negative influence on the health degree is. First weightThe extent to which the power factor affects the health score is determined. If it isThe influence of power variation on health is larger.

Indicating the current temperature of the deviceAnd normal operating temperatureThe influence of the deviation of (2) on the health of the device. Denominator of denominatorThe temperature deviation is standardized in the working range of the temperature, so that the temperature characteristics of different devices are comparable. The influence of temperature deviation on the health degree is increased by adopting the fourth power, and the influence of the temperature deviation on the health degree is larger as the temperature deviates from a normal value. Second weightThe influence of temperature factors is controlled, and the effect of the temperature factors on the health degree is along withAnd increases and enhances.

Measuring vibration intensity of equipmentRelative to critical vibration intensityIs a ratio of (2). The influence of vibration intensity change on health degree can be relieved by adopting a logarithmic function, so that the change is smoother. The closer or more the vibration intensity is to the threshold, the less the health score decreases.Introducing equipment operation accelerationIs a function of (a) and (b). The exponential function causes the negative effects of acceleration to manifest in a non-linear fashion, the greater the acceleration, the more significant the health score decreases.

Third weightFor controlling the influence of vibration factors on health, the influence of vibration variation is along withAnd increases to enhance. Fourth weightThe weakening effect of the acceleration on the health degree is controlled, and the larger the value is, the more remarkable the negative influence of the acceleration on the health degree is.

The formula calculatesThe power, temperature, vibration, acceleration and other parameters of the equipment are weighted and combined through the weights, so that different influences of the parameters on the health degree of the equipment are reflected. Deviations in power and temperature, as well as increases in vibration intensity and acceleration, all result in a decrease in health score. Wherein the weight isThe setting of (c) allows for adjusting the magnitude of the contribution of factors to the final fitness score, thereby providing a flexible fitness assessment model for different devices or scenarios.

And 204, analyzing the historical operation data of each device through a time sequence analysis method and a machine learning model to predict potential device faults and obtain fault prediction results of each device.

In some embodiments, step 204 may include the steps of:

In some embodiments, the probability of failure of a device may be expressed as:

;

In a specific implementation of the present invention,Is the probability of failure of the device at the current time t.Is the j-th point in time at which the historical fault occurred.Is the weight of the j-th time point and reflects the importance or influence degree of the fault event. The greater the weight, the greater the contribution of the event to the probability of failure at the current time.Is the width parameter at the j-th time point, which determines the range of influence of the historical fault. Larger sizeThe influence range of the fault event is wider, and the time decay is slower; smaller and smallerIt means that the impact range is narrower and the time decay is faster. M is the number of fault events in the history data.The influence coefficient of the kth potential fault mode determines the contribution degree of the fault mode to the fault probability. The larger the coefficient, the more significant the effect of the pattern on the probability of failure.

Is a linear expression of the kth latent fault mode describing the combined effect of various input variables (e.g., plant parameters, operating conditions, etc.) on the risk of faults in that mode.In the general form of: Wherein, the method comprises the steps of, wherein, Is the weight of the ith input variable in the kth mode; is an input variable (e.g., sensor data, environmental conditions, etc.); Is a bias term. Is a logistic regression function (sigmoid function) for expressing the linearityThe transition to a probability value between 0 and 1 reflects the likelihood of the device failing in this mode. Y is the number of potential failure modes and represents the total number of different failure mechanisms or modes considered in the model.

In particular, the method comprises the steps of,This reflects in part the impact of the historical fault events on the current fault probability. Through Gaussian function form calculation, the influence of each historical fault event decays with time, and the fault event which is closer to the current time has larger influence on the current fault probability.

This section reflects the impact of risk assessment based on potential failure modes on the current failure probability. The risk of each potential failure mode is assessed by a logistic regression model and integrated into the total failure probability.

Probability of failureConsists of two main parts:

Influence of historical failure data: and the historical fault data is weighted and summed through a Gaussian distribution function, so that the influence of the historical fault event on the fault risk at the current moment is reflected. This section relies mainly on past empirical data, taking into account time decay effects, the more recent historical faults have a greater impact on the current time.

Effects of latent failure modes: and evaluating potential fault mode risks through a logistic regression model, and correcting and supplementing the fault probability at the current moment by combining the modes. The depth and breadth of prediction are enhanced through the comprehensive influence of a plurality of fault modes, and the method is applicable to different fault mechanisms.

The formula combines historical data and a machine learning model, can utilize past fault experience and can predict possible faults in the future in a data driving mode, and a comprehensive fault risk assessment method is provided.

And 205, generating an operation and maintenance plan and an operation and maintenance priority of each device according to the health degree score and the fault prediction result of each device.

In some embodiments, step 205 may comprise the steps of:

In some embodiments, the operation and maintenance priority of each of the devices is expressed as:

;

In a specific implementation of the present invention,Is the operation and maintenance priority of the i-th device,Is the health score of the ith device, the lower the health score (i.e.Smaller),The larger the higher the operation priority that the device needs.Is the probability of failure of the device at the current time t,A first influence coefficient, a second influence coefficient and a third influence coefficient respectively,The rise of the fault probability directly improves the operation and maintenance priority. L is a lagrangian function.

In particular, the method comprises the steps of,This section reflects the impact of device health and probability of failure on operation and maintenance priority. Health scoreDevice priority (i.e., device status difference)Higher, but failure probabilityWhen the operation and maintenance priority is increased, the operation and maintenance priority is also increased.The health state and the fault risk of the equipment are comprehensively considered, and the operation and maintenance priority is ensured to be rapidly increased under the conditions of poor equipment health and high fault probability.

These two parts introduce the first and second derivatives of the time-dependent lagrangian function L for further tuning of the operation and maintenance priority, taking into account the dynamic changes of the system over time. In the optimization problem, the Lagrangian function L is typically used to handle a combination of objective functions and constraints. Here, it may represent a comprehensive factor of operation and maintenance costs of the system, resource allocation, and the like.

First derivativeThe change rate of the Lagrangian function along with time is represented, and the instant change trend of the system operation cost or the resource allocation is reflected.Is a weight coefficient associated with the rate of change. If it isPositive, indicating that the system state may deteriorate, the operation and maintenance priority may need to be increased.

Second derivativeThe acceleration change of the Lagrangian function with time is represented, and the acceleration of the system state change is reflected.Is a weight coefficient associated with the acceleration. If it isPositive, indicating that the system state is deteriorating, the operation and maintenance priority may need to be increased more greatly.

By calculating the operation and maintenance priority of each deviceThe method is an index for comprehensively evaluating the equipment maintenance requirement, comprehensively considers the current state, the historical data and the future trend of the equipment, provides a comprehensive and dynamic equipment operation and maintenance priority evaluation method, is beneficial to optimizing the allocation of operation and maintenance resources, and ensures the stable operation of the system.

And 206, automatically triggering an alarm and providing corresponding maintenance suggestions and emergency measures for the equipment triggering the alarm when the health degree score or the fault probability of any equipment meets the preset condition.

In some embodiments, step 206 may include the steps of:

It will be appreciated that the health score is a composite score calculated from the operating parameters of the device (e.g., power, temperature, vibration, acceleration, etc.), reflecting the current health status of the device. The health degree scoring threshold is a preset value used for judging whether the equipment is in a normal running state or not. The threshold is typically set based on historical operating data of the device, manufacturer recommended safety standards, or operating and maintenance experience. When the health score of the device is below this threshold, meaning that the operating state of the device has deteriorated to an unsafe level, maintenance or overhaul may be required immediately.

It will also be appreciated that the probability of failure of a device is a value that is predicted based on historical failure data and potential failure modes of the device, indicating the likelihood of the device failing at the current time. The failure probability threshold is a set threshold value that indicates how likely the device needs to be of particular interest and handling. The threshold may be determined based on statistical analysis of fault history data, industry standards, or operation and maintenance policies. When the probability of failure of the device exceeds this threshold, it is stated that the device may be at a higher risk of failure, and preventive measures need to be taken in advance.

In some embodiments, a device is considered potentially risky when either the health score of the device is below a health score threshold, or the probability of failure exceeds a failure probability threshold. When the preset conditions are met, the system can automatically trigger alarm operation and send out warning signals to operation and maintenance personnel. The alarm can be in the forms of sound, light, short message notification, mail notification and the like, so that operation and maintenance personnel can be ensured to know the abnormal state of the equipment in time.

In some embodiments, the system may provide corresponding maintenance recommendations based on the specific conditions of the device while triggering an alarm. These suggestions may include immediately checking a component, adjusting operating parameters of the equipment, scheduling downtime for maintenance, etc., to assist the service personnel in making the correct decisions. In the event that the equipment is at risk of serious failure, the system may also recommend emergency measures, such as reducing equipment load, switching to backup equipment, temporarily stopping equipment operation, etc., to prevent further deterioration of the failure or to cause a greater range of blackout incidents.

By setting the health degree scoring threshold and the fault probability threshold, the scheme can monitor the running state of the equipment in real time and give an alarm in time when potential risks occur. Therefore, the equipment can be prevented from running continuously in a hidden trouble state, the occurrence rate and the severity of equipment faults can be reduced in an early intervention mode, and the safe and stable running of the transformer substation is ensured. In addition, maintenance suggestions and emergency measures generated automatically by the system help operation and maintenance personnel to respond quickly, and the downtime and operation and maintenance cost are effectively reduced.

In some embodiments, the method of the present invention may further comprise:

The historical data report is a document automatically generated by the system and records the running state data of the equipment in a past period of time. These data typically include key parameters of power, temperature, vibration, acceleration, etc. of the device and are arranged in time series. Maintenance records are records of all maintenance activities that the device has taken during past operation and maintenance. The maintenance records detail the activities of inspecting, maintaining, repairing or replacing parts, etc. performed by the device at various points in time, and the results of these activities.

In some embodiments, the historical data report may be divided into a plurality of time periods (e.g., monthly, quarterly, yearly, etc.) based on the operating characteristics of the device and the frequency of data collection, each time period representing the operating state of the device during that period of time. During each time period, the system calculates a historical health score based on the operational data of the device, the score reflecting the overall operational status of the device during the time period. The higher the health score, the better the device operating state; the lower the score, the more problematic the device may be.

For each device, the system calculates an average of its historical health scores over a plurality of time periods. This average provides a long-term, comprehensive assessment of the health of the device.

In some embodiments, the system may be able to identify trending problems that may exist with the device in long-term operation by analyzing the historical health score averages of the device. For example, if the average health score gradually decreases, it may indicate that the status of the device is deteriorating and that an early intervention is required for maintenance.

Determination of backup maintenance recommendations: based on the analysis of the historical health scores, the system generates backup maintenance recommendations. These suggestions may include enhancing the frequency of monitoring certain components, replacing wearing parts in advance, scheduling more preventive maintenance activities, etc., to extend the service life of the device, preventing sudden failures.

In summary, by acquiring and analyzing the historical operating state and maintenance records of the equipment, the invention can be used for deeply knowing the long-term health condition of the equipment and identifying potential trend problems. The calculated historical health score average provides a quantitative assessment for the overall running quality of the equipment, helping the operation and maintenance personnel to more accurately predict the maintenance requirements of the equipment. Based on the standby maintenance suggestions formulated by the analysis results, the operation and maintenance strategy is more active and preventive, the occurrence frequency of equipment faults is reduced, the service life of the equipment is prolonged, the configuration of operation and maintenance resources is optimized, and therefore the operation safety and reliability of the transformer substation are improved.

Referring to fig. 3, fig. 3 is a schematic structural diagram of a substation-based operation and dimension digitizing management system according to the present invention.

As shown in fig. 3, an operation and maintenance digital management system based on a transformer substation according to an embodiment of the present invention includes:

The data acquisition module 301 is configured to acquire operation data of each device of the substation in real time through a sensor network;

the data processing module 302 is configured to perform cleaning and preprocessing on the original operation data to obtain preprocessed data;

The device scoring module 303 is configured to evaluate, based on the preprocessed data, a state of each device through a device health evaluation model, to obtain a health score of each device;

The fault prediction module 304 is configured to analyze the historical operation data of each device through a time sequence analysis method and a machine learning model, so as to predict potential device faults, and obtain a fault prediction result of each device;

An operation and maintenance planning module 305, configured to generate an operation and maintenance plan and an operation and maintenance priority of each device according to the health score and the failure prediction result of each device;

The operation and maintenance execution module 306 is configured to automatically trigger an alarm and provide corresponding maintenance suggestions and emergency measures for the device that triggers the alarm when the health score or the fault probability of any one device meets a preset condition.

Referring to fig. 4, fig. 4 is a schematic diagram of an embodiment of an electronic device according to an embodiment of the invention. As shown in fig. 4, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420, wherein the processor 420 executes the computer program 411 to implement the following steps:

Referring to fig. 5, fig. 5 is a schematic diagram of an embodiment of a computer readable storage medium according to an embodiment of the invention. As shown in fig. 5, the present embodiment provides a computer-readable storage medium 500 having stored thereon a computer program 411, which computer program 411, when executed by a processor, performs the steps of:

In the foregoing embodiments, the descriptions of the embodiments are focused on, and for those portions of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.

It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.

It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

1. An operation and maintenance digital management method based on a transformer substation, which is characterized by comprising the following steps:

2. The substation-based operation and maintenance digital management method according to claim 1, wherein the evaluating the state of each device through a device health evaluation model based on the preprocessed data to obtain the health score of each device comprises:

3. The substation-based operation and maintenance digital management method according to claim 2, wherein the health score of each of the devices is expressed as:

;

4. The substation-based operation and maintenance digital management method according to claim 3, wherein analyzing the historical operation data of each device by a time series analysis method and a machine learning model to predict potential device faults, obtaining a fault prediction result of each device comprises:

5. The substation-based operation and maintenance digital management method according to claim 4, wherein the failure probability of the equipment is expressed as:

;

6. The substation-based operation and maintenance digital management method according to claim 5, wherein the generating the operation and maintenance priority of each device according to the health score and the fault prediction result of each device comprises:

7. The substation-based operation and maintenance digital management method according to claim 6, wherein the operation and maintenance priority of each of the devices is expressed as:

;

8. The substation-based operation and maintenance digital management method according to claim 7, wherein when the health score or the fault probability of any one device meets a preset condition, automatically triggering an alarm and providing corresponding maintenance advice and emergency measures for the device triggering the alarm, comprising:

9. The substation-based operation and maintenance digital management method according to claim 8, further comprising:

10. A substation-based operation and dimension digitizing management system, the system comprising: