CN118469255A - Wind power network service flow control system - Google Patents
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Abstract
The invention provides a wind power network service flow control system, belonging to the technical field of wind power network service flow; the system comprises: the environment acquisition module is used for: acquiring and analyzing specific environmental information of the target wind power equipment at each execution time to obtain an environmental coefficient and an operation monitoring value of the target wind power equipment at the corresponding execution time; an information capturing module: acquiring a network service flow issued to the target wind power equipment, and capturing execution information of the target wind power equipment according to each execution time of the network service flow; an anomaly prediction module: the environment coefficient, the operation monitoring value and the execution information under each execution time are subjected to abnormal prediction, and the service adjusting instruction is fed back to the future execution time to control the corresponding target wind power equipment to continuously work, so that the effectiveness of judging the abnormality of the wind power equipment is improved, and a data base is provided for the stable operation of the wind power equipment.
Description
Technical Field
The invention relates to the technical field of wind power network service flow, in particular to a wind power network service flow control system.
Background
At present, the occurrence of wind power network business flow control systems is derived from the development and application of wind power generation technology. With the large-scale and complicated wind power generation equipment, the traditional manual detection and maintenance mode can not meet the requirements for efficient and accurate management of the wind power generation equipment. The wind power operation and maintenance management mode based on network monitoring and control is widely applied.
The wind power equipment is monitored by installing the monitoring component on the wind power equipment, so that whether the abnormality exists or not is judged, the abnormality is only aimed at the abnormality at the current moment, and the wind power equipment is seriously dependent on the current environment wind power condition due to the fact that the abnormality is only monitored on the wind power equipment, so that the effectiveness of abnormality determination is definitely reduced, and the reliability of stable operation of the wind power equipment is further reduced.
Therefore, the invention provides a wind power network business flow control system.
Disclosure of Invention
The invention provides a wind power network business flow control system which is used for effectively predicting the abnormality of wind power equipment by capturing the execution result of the wind power equipment according to network business flow and combining the real-time acquisition of the environment, so as to facilitate the timely regulation and control of future time, ensure the stable operation of the wind power equipment and improve the reliability of the wind power equipment.
The invention provides a wind power network business flow control system, which comprises:
The environment acquisition module is used for: acquiring and analyzing specific environmental information of the target wind power equipment at each execution time to obtain an environmental coefficient and an operation monitoring value of the target wind power equipment at the corresponding execution time;
An information capturing module: acquiring a network service flow issued to the target wind power equipment, and capturing execution information of the target wind power equipment according to each execution time of the network service flow;
an anomaly prediction module: and carrying out abnormal prediction on the environmental coefficient, the operation monitoring value and the execution information at each execution time, and feeding back a service adjusting instruction to the future execution time to control the corresponding target wind power equipment to continuously work.
In one possible implementation, the method further includes:
the label setting module: setting a first electronic tag for each target wind power equipment, and setting a second electronic tag for each environment monitoring sensing equipment;
And a mapping matching module: mapping and matching the first electronic tag and the second electronic tag, determining an environment monitoring sensing device for monitoring the same target wind power device, and acquiring specific environment information.
In one possible implementation manner, the environment obtaining module includes:
Distribution unit: acquiring a current distribution position of each target wind power device, a geographic environment of the current distribution position and a device type, and constructing a detection area based on the target wind power device;
And a detection unit: and detecting and obtaining specific environmental information of each target wind power equipment under each execution time according to the position relation between the environmental monitoring sensing equipment and the detection area.
In one possible implementation manner, the environment obtaining module further includes:
Matrix construction unit: extracting wind power parameters from the detected specific environmental information of each target wind power equipment, and constructing a wind power parameter matrix aiming at the target wind power equipment, wherein the row of the wind power parameter matrix contains all wind power parameters related to the same execution subframe, and the column contains the same wind power parameters related to different execution subframes;
Matrix analysis unit: performing feature calculation on the wind power parameter matrix to obtain a feature vector, and performing corresponding sub-frame parameter comprehensive analysis on each row in the wind power parameter matrix to obtain a first coefficient and performing moment parameter trend analysis on each column in the wind power parameter matrix to obtain a second coefficient;
coefficient determination unit: determining and obtaining the environment coefficient corresponding to the execution time based on all the first coefficients and the second coefficients;
a value determination unit: and obtaining an operation monitoring value matched with the characteristic vector from a vector-value mapping table.
In one possible implementation, the information capturing module includes:
traffic flow comparison unit: acquiring a network service flow issued to the target wind power equipment and a current service flow received by the target wind power equipment, and judging whether the current service flow is consistent with the network service flow;
An information capturing unit: if yes, capturing first information of the target wind power equipment at each execution time according to the network service flow;
Otherwise, capturing second information of the target wind power equipment according to the current service flow at each execution moment;
an information processing unit: and preprocessing the finally acquired information to obtain execution information.
In one possible implementation, the information processing unit includes:
A first determination block: carrying out logic analysis on each piece of sub information in the last acquired information under each execution time based on a logic analysis model, carrying out space mapping on each piece of sub information based on a logic-space mapping table, and determining to obtain service logic and a logic space matched with the service logic;
A second determination block: carrying out quantity statistics on service logics in the same logic space, and determining service importance and information concentration of the same logic space;
; wherein n1 represents service logic statistics data in the same logic space; representing the logic length of the ith 1 service logic in the same logic space; representing the total length of the accommodation information of the same logic space; Spatial weights representing the same logical space; The representation is based on all Is a variance of (2); logic weight of the ith 1 st business logic in the same logic space is represented; representing the total maximum weight of the same logical space when filled; representing the total average weight of the same logical space when filled; z1 represents the business importance of the same logical space; r1 represents the information concentration of the same logic space; Representing the total planned logic number of the same logic space when being filled; lg represents the sign of a logarithmic function;
screening: and screening and reserving sub-information from all sub-information under the same execution time according to the service importance and the information concentration to obtain the execution information under the same execution time.
In one possible implementation, the anomaly prediction module includes:
standard threshold determining unit: acquiring environment coefficients at corresponding execution time Operation monitoring valueBased on a preset standard threshold YH for each environmental parameter in the specific environmental information:
; wherein, Representing the data average value after the standardized processing of the specific environmental information under the corresponding execution time; Representing the value after the standardization processing of the jth environmental parameter under the corresponding execution time; representing the variance of the specific environment information under the corresponding execution time after the standardized processing; 0.01 represents an error coefficient of the entire environment; n represents the number of environmental parameters related to the specific environmental information; representing a preset standard threshold value under the jth environmental parameter under the corresponding execution time; representation is based on an operation monitoring value The threshold value adjusting coefficient of (3) is (0.01,0.02);
Operation monitoring abnormality judging unit: extracting features of the execution information of the corresponding execution time, analyzing the feature value and a preset standard threshold value, and judging whether the corresponding target wind power equipment is abnormal at the corresponding execution time;
; wherein, Representing all preset standard thresholds under corresponding execution timeIs the average value of (2); m represents the total number of sub-information in the execution information existing under the corresponding execution time; representing a signal value of the kth sub-information under the corresponding execution time after standardized processing according to the environmental parameters; an analysis value indicating a corresponding execution time; ln represents the sign of the logarithmic function;
And if the analysis value is larger than the preset execution value, judging that the target wind power equipment corresponding to the execution time is not abnormal.
In one possible implementation manner, the anomaly prediction module further includes:
Analysis unit: counting analysis values under different execution time points, constructing an analysis curve, and carrying out periodic analysis and abnormal occurrence frequency analysis under the same period on the analysis curve;
Prediction unit: according to the periodic analysis result and the analysis result of the occurrence times of the anomalies, determining the high-frequency anomalies in the future period, locking the future execution time consistent with the high-frequency anomalies, calling normal execution parameters consistent with the high-frequency anomalies and abnormal-to-normal abnormal regulation parameters from the analysis curve, and carrying out prediction and regulation feedback on the future execution parameters at the future execution time to control the corresponding target wind power equipment to continuously work.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a block diagram of a wind power network traffic flow control system in an embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1:
The invention provides a wind power network business flow control system, as shown in figure 1, comprising:
The environment acquisition module is used for: acquiring and analyzing specific environmental information of the target wind power equipment at each execution time to obtain an environmental coefficient and an operation monitoring value of the target wind power equipment at the corresponding execution time;
An information capturing module: acquiring a network service flow issued to the target wind power equipment, and capturing execution information of the target wind power equipment according to each execution time of the network service flow;
an anomaly prediction module: and carrying out abnormal prediction on the environmental coefficient, the operation monitoring value and the execution information at each execution time, and feeding back a service adjusting instruction to the future execution time to control the corresponding target wind power equipment to continuously work.
In this embodiment, the wind power plant is a plant for converting wind energy into electrical energy. The material comprises the following main components: fan, generator, pylon, control system, converter and electric wire netting connected system.
In this embodiment, the environmental information includes the surroundings of the wind power plant: basic environmental data such as wind speed, wind direction, temperature, humidity, etc.
In this embodiment, the environmental factor refers to a parameter for evaluating the performance and reliability of the wind power plant under specific environmental conditions. The influence degree of the environment on the running of the wind power equipment is reflected, and the adaptability of the equipment under different environmental conditions is reflected.
In the embodiment, the operation monitoring value refers to an index or a numerical value reflecting the operation state and performance of the wind power equipment, which is obtained on the basis of the environmental information acquired and analyzed by the environmental acquisition module. Is used for evaluating the health condition and the operation efficiency of the wind power equipment.
In this embodiment, the network traffic refers to the propagation of various instructions, data and information transmitted through the network during operation of the wind power plant. Various communication and data exchange processes required for monitoring, controlling and managing wind power equipment are included.
In this embodiment, the execution information refers to specific operation and state information of the target wind power equipment according to the received network service flow at each execution time. Including information about the parameter values, current state, etc. performed by the device.
In this embodiment, the service adjustment instruction refers to an instruction sent to the target wind power device according to a prediction result and a control requirement given by the anomaly prediction module, and is used for adjusting operations such as an operation state, parameter setting or alarm processing of the device. For example, if the abnormality prediction module finds that the device may fail, the operation parameters of the device may be adjusted by giving corresponding service adjustment instructions, so as to reduce the probability of failure.
The working principle and the beneficial effects of the technical scheme are as follows: the system monitors and predicts the equipment by collecting environment information, capturing execution information and applying an anomaly prediction module, thereby realizing the control of the continuous working state of the equipment. By analyzing the environment information and the execution information, the accuracy of judging the wind turbine at the current moment is improved, and the situation that the running of the wind power equipment is unstable is reduced.
Example 2:
On the basis of the above embodiment 1, the method further includes:
the label setting module: setting a first electronic tag for each target wind power equipment, and setting a second electronic tag for each environment monitoring sensing equipment;
And a mapping matching module: mapping and matching the first electronic tag and the second electronic tag, determining an environment monitoring sensing device for monitoring the same target wind power device, and acquiring specific environment information.
In this embodiment, the first electronic tag is an electronic identifier for identifying and tracking the target wind power plant.
In this embodiment, the environmental monitoring sensing device is a device for collecting and monitoring various environmental parameters in a wind farm, including various sensors and associated data acquisition systems, such as wind speed, temperature and humidity, air pressure, etc.
In this embodiment, the second electronic tag is an electronic identifier for identifying and tracking the environmental monitoring sensor device.
In this embodiment, mapping and matching refer to a process of matching or matching a first electronic tag with another second electronic tag.
The working principle and the beneficial effects of the technical scheme are as follows: through the label setting module and the mapping matching module, accurate matching of the target wind power equipment and the environment monitoring sensing equipment can be achieved, and therefore collection of specific environment information is achieved. Providing high quality environmental data and reducing the possibility of inaccurate data acquisition caused by abnormal matching.
Example 3:
on the basis of the above embodiment 1, the environment acquisition module includes:
Distribution unit: acquiring a current distribution position of each target wind power device, a geographic environment of the current distribution position and a device type, and constructing a detection area based on the target wind power device;
And a detection unit: and detecting and obtaining specific environmental information of each target wind power equipment under each execution time according to the position relation between the environmental monitoring sensing equipment and the detection area.
In this embodiment, the geographic environment refers to a specific geographic situation where the target wind power equipment is located, including: specific geographic features such as topography, climate conditions, soil types, hydrologic conditions and the like.
In this embodiment, the device type refers to a kind of wind power device, and is used to describe specific characteristics and performance of the target wind power device.
In this embodiment, the detection area refers to an area in which monitoring and detection operations in terms of wind power equipment status, performance, and the like are performed within a specific area.
In this embodiment, the positional relationship refers to the relative position and spatial arrangement between the environmental monitoring sensing device and the target wind power device in the detection area.
The working principle and the beneficial effects of the technical scheme are as follows: through the environment monitoring sensing equipment, specific environment information of each target wind power equipment at each execution time can be obtained in real time, wherein the specific environment information comprises meteorological parameters, humiture, wind speed and the like. The data abnormal error caused by insensitivity to the change condition of the environment where the wind power equipment is located is reduced, and the possibility of unreliable data is reduced.
Example 4:
on the basis of the above embodiment 1, the environment obtaining module further includes:
Matrix construction unit: extracting wind power parameters from the detected specific environmental information of each target wind power equipment, and constructing a wind power parameter matrix aiming at the target wind power equipment, wherein the row of the wind power parameter matrix contains all wind power parameters related to the same execution subframe, and the column contains the same wind power parameters related to different execution subframes;
Matrix analysis unit: performing feature calculation on the wind power parameter matrix to obtain a feature vector, and performing corresponding sub-frame parameter comprehensive analysis on each row in the wind power parameter matrix to obtain a first coefficient and performing moment parameter trend analysis on each column in the wind power parameter matrix to obtain a second coefficient;
coefficient determination unit: determining and obtaining the environment coefficient corresponding to the execution time based on all the first coefficients and the second coefficients;
a value determination unit: and obtaining an operation monitoring value matched with the characteristic vector from a vector-value mapping table.
In this embodiment, the wind power parameters refer to wind power related parameters extracted from environmental information, including wind speed, wind direction, temperature, wind intensity environmental parameters.
In this embodiment, the wind parameter matrix is a multi-dimensional matrix for storing and organizing wind parameter data of the detected target wind power plant. The rows of the matrix represent all wind power parameters related under the same execution subframe, and assuming that the current wind power parameter matrix has 3 parameters and 2 execution subframes, the current wind power parameter matrix is: Wherein A first parameter representing a first execution subframe,A first parameter representing a first execution subframe,A first parameter representing a first execution subframe.
In this embodiment, performing the sub-frame refers to one cycle of sensor acquisition and data processing for the target wind power plant within a specific period of time. During this period, the sensor will acquire and record the environmental parameters surrounding the target device.
In this embodiment, the vector-value mapping table is a mapping table representing the relationship of the run monitor values for which the feature vectors match.
In this embodiment, the first coefficient refers to a characteristic value calculated when the parameter of each row in the wind power parameter matrix is comprehensively analyzed, and the second coefficient refers to a characteristic value obtained when the parameter trend analysis at the execution time is performed on each column in the wind power parameter matrix. Describing the variation trend of the wind power parameters in different execution subframes, namely, after parameter standardization is carried out on the wind power parameter matrix, calculating the numerical value and the weight of the same-row parameters, namely, obtaining the first coefficient, carrying out numerical value accumulation and averaging on the same-row parameters, carrying out averaging again on the fitting values obtained after fitting analysis, and finally multiplying the fitting values with the weight of the corresponding row parameters to obtain the second coefficient.
The environmental coefficient is obtained by dividing the sum of all the first coefficients and the sum of all the second coefficients by the product of the number of rows and the number of columns of the wind power parameter matrix.
The working principle and the beneficial effects of the technical scheme are as follows: the wind power parameter extraction and analysis of the environmental information of the target wind power equipment are realized by combining the matrix construction unit and the matrix analysis unit. The environment information provides a more comprehensive and visual data expression mode, reduces the possibility of calculation errors of data and reduces the condition of invalid abnormal judgment.
Example 5:
On the basis of the above embodiment 1, the information capturing module includes:
traffic flow comparison unit: acquiring a network service flow issued to the target wind power equipment and a current service flow received by the target wind power equipment, and judging whether the current service flow is consistent with the network service flow;
An information capturing unit: if yes, capturing first information of the target wind power equipment at each execution time according to the network service flow;
Otherwise, capturing second information of the target wind power equipment according to the current service flow at each execution moment;
an information processing unit: and preprocessing the finally acquired information to obtain execution information.
In this embodiment, the current traffic flow is the network traffic flow currently received by the target wind power plant, representing the real-time network data flow being processed.
In this embodiment, the first information is a signal received by the target wind power device at each execution time according to the network traffic flow when the lower traffic flow coincides with the network traffic flow.
In this embodiment, the second information is a signal received by the target wind power device at each execution time according to the network service flow when the lower service flow is inconsistent with the network service flow, and the first information and the second information are both related operation information after the execution of the service flow.
In this embodiment, preprocessing is to perform preliminary processing and screening on the acquired information to obtain more useful and efficient execution information.
The working principle and the beneficial effects of the technical scheme are as follows: the method for comparing the network traffic flows and capturing the information improves the efficiency of monitoring and data processing of the wind power equipment, reduces the low analysis efficiency and reduces the possibility of unstable running conditions of the wind power equipment.
Example 6:
On the basis of the above embodiment 1, the information processing unit includes:
A first determination block: carrying out logic analysis on each piece of sub information in the last acquired information under each execution time based on a logic analysis model, carrying out space mapping on each piece of sub information based on a logic-space mapping table, and determining to obtain service logic and a logic space matched with the service logic;
A second determination block: carrying out quantity statistics on service logics in the same logic space, and determining service importance and information concentration of the same logic space;
; wherein n1 represents service logic statistics data in the same logic space; representing the logic length of the ith 1 service logic in the same logic space; representing the total length of the accommodation information of the same logic space; Spatial weights representing the same logical space; The representation is based on all Is a variance of (2); logic weight of the ith 1 st business logic in the same logic space is represented; representing the total maximum weight of the same logical space when filled; representing the total average weight of the same logical space when filled; z1 represents the business importance of the same logical space; r1 represents the information concentration of the same logic space; Representing the total planned logic number of the same logic space when being filled; lg represents the sign of a logarithmic function;
screening: and screening and reserving sub-information from all sub-information under the same execution time according to the service importance and the information concentration to obtain the execution information under the same execution time.
In this embodiment, the logical parsing model is a model for logically analyzing and interpreting natural language. And analyzing the sentence in the text in grammar and converting the sentence into a logic form so as to carry out logic reasoning and understanding, wherein a certain logic necessarily exists in the result fed back by each piece of sub-information, and the space existing for the logic is preset.
In this embodiment, the sub-information is a component of the entire traffic flow, including the current operating state of the device, wind speed and direction, temperature and humidity, and other execution information.
In this embodiment, logical parsing is a process of converting natural language text in execution information into a logical form.
In this embodiment, the logical-to-spatial mapping table is a mapping table representing the logical information versus logical space.
In this embodiment, the space mapping refers to mapping the sub-information obtained by logic analysis into a corresponding logic space, so as to facilitate further processing and analysis.
In the embodiment, the service logic expresses the service flow of the wind power equipment according to the information obtained by logic analysis, and is a description and expression logic relationship.
In this embodiment, logical space refers to a structured space for storing logical relationships.
In this embodiment, the service importance refers to the importance of service logic in the current logic space. Reflecting the importance of one business logic to implement the current logic.
In this embodiment, the degree of information density about business logic in the current logic space is referred to.
In this embodiment, the logical length is the number of symbols or bits required to represent a piece of logic.
In this embodiment, the spatial weight is a weight parameter of the service logic in the current logic space, and is preset.
In the process of screening and reserving sub-information, the business importance is larger than the preset importance, the information concentration is larger than the preset concentration, and the preset importance and the preset concentration are preset.
The working principle and the beneficial effects of the technical scheme are as follows: and screening and reserving the sub-information with higher business importance and information concentration from all the sub-information under the same execution time according to the evaluation results of the business importance and the information concentration, and obtaining the execution information under the same execution time. The screening and filtering processes of the execution information can be optimized, and the probability of invalid judgment abnormality is reduced.
Example 7:
On the basis of the above embodiment 1, the anomaly prediction module includes:
standard threshold determining unit: acquiring environment coefficients at corresponding execution time Operation monitoring valueBased on a preset standard threshold YH for each environmental parameter in the specific environmental information:
; wherein, Representing the data average value after the standardized processing of the specific environmental information under the corresponding execution time; Representing the value after the standardization processing of the jth environmental parameter under the corresponding execution time; representing the variance of the specific environment information under the corresponding execution time after the standardized processing; 0.01 represents an error coefficient of the entire environment; n represents the number of environmental parameters related to the specific environmental information; representing a preset standard threshold value under the jth environmental parameter under the corresponding execution time; representation is based on an operation monitoring value The threshold value adjusting coefficient of (3) is (0.01,0.02);
Operation monitoring abnormality judging unit: extracting features of the execution information of the corresponding execution time, analyzing the feature value and a preset standard threshold value, and judging whether the corresponding target wind power equipment is abnormal at the corresponding execution time;
; wherein, Representing all preset standard thresholds under corresponding execution timeIs the average value of (2); m represents the total number of sub-information in the execution information existing under the corresponding execution time; representing a signal value of the kth sub-information under the corresponding execution time after standardized processing according to the environmental parameters; an analysis value indicating a corresponding execution time; ln represents the sign of the logarithmic function;
And if the analysis value is larger than the preset execution value, judging that the target wind power equipment corresponding to the execution time is not abnormal.
In this embodiment, the preset standard threshold is a preset threshold obtained according to the environmental coefficient and the operation monitoring value at a specific time.
In this embodiment, the normalization process refers to the conversion of specific environmental information under execution into data of a specific range.
In this embodiment, the error coefficient refers to an error constant that exists fixedly in calculating a preset standard threshold, here taken to be 0.01.
In this embodiment, the threshold adjustment coefficient is a constant representing the threshold adjustment range that the operation is to control, and the value range is (0.01,0.02);
In the embodiment, the feature extraction is to extract features capable of reflecting the state of the target wind power equipment from the execution information, and judge whether the equipment is abnormal or not according to the features.
In this embodiment, the preset execution value is generally 0.5.
In this embodiment, the feature value is a numerical value obtained by feature extraction of the execution information.
The working principle and the beneficial effects of the technical scheme are as follows: and extracting the characteristics of the execution information at the corresponding execution time, analyzing the characteristic value and a preset standard threshold value, and judging whether the corresponding target wind power equipment is abnormal at the corresponding execution time. Multiple data processing methods are adopted, so that the possibility of judging errors of a single method is reduced.
Example 8:
On the basis of the above embodiment 1, the anomaly prediction module further includes:
Analysis unit: counting analysis values under different execution time points, constructing an analysis curve, and carrying out periodic analysis and abnormal occurrence frequency analysis under the same period on the analysis curve;
Prediction unit: according to the periodic analysis result and the analysis result of the occurrence times of the anomalies, determining the high-frequency anomalies in the future period, locking the future execution time consistent with the high-frequency anomalies, calling normal execution parameters consistent with the high-frequency anomalies and abnormal-to-normal abnormal regulation parameters from the analysis curve, and carrying out prediction and regulation feedback on the future execution parameters at the future execution time to control the corresponding target wind power equipment to continuously work.
In this embodiment, the analysis value is one parameter used in the standard threshold determination unit and the fortune abnormality judgment unit, describing a comprehensive evaluation value corresponding to the device state at the execution time.
In this embodiment, the analysis curve is a continuous curve drawn by analysis values corresponding to a plurality of execution moments.
In this embodiment, the periodic analysis is a process of analyzing the periodic characteristics in the analysis curve.
In this embodiment, the high-frequency outlier refers to an outlier that occurs a high number of times in the analysis curve.
In this embodiment, the abnormal adjustment parameter refers to a parameter required for adjusting the device from an abnormal state to a normal operation state, for example, when the value of the parameter 1 is a1, the execution state is abnormal, and when the value of the parameter 1 is a2, the execution state is normal, and when the parameter 1 is the abnormal adjustment parameter.
In this embodiment, the future execution time refers to, for example, a time t2 at which the high-frequency outlier generally appears is one cycle, and then the future execution time is, that is, a time t2 at which the future cycle is locked.
In this embodiment, the purpose of prediction and feedback adjustment is to ensure that the target wind power plant can normally and continuously operate at the future time t 2.
The working principle and the beneficial effects of the technical scheme are as follows: and predicting high-frequency abnormal points in a future period through analysis curves, periodic analysis and abnormal occurrence frequency analysis, and continuously working through adjusting feedback control equipment, so that the possibility of unstable operation of the wind power equipment is reduced, and the analysis accuracy of the wind power equipment is improved.
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, if such modifications and variations of the present invention fall within the scope of the present invention and the equivalent techniques thereof, the present invention is also intended to include such modifications and variations.
Claims (8)
1. A wind power network traffic flow control system, comprising:
The environment acquisition module is used for: acquiring and analyzing specific environmental information of the target wind power equipment at each execution time to obtain an environmental coefficient and an operation monitoring value of the target wind power equipment at the corresponding execution time;
An information capturing module: acquiring a network service flow issued to the target wind power equipment, and capturing execution information of the target wind power equipment according to each execution time of the network service flow;
an anomaly prediction module: and carrying out abnormal prediction on the environmental coefficient, the operation monitoring value and the execution information at each execution time, and feeding back a service adjusting instruction to the future execution time to control the corresponding target wind power equipment to continuously work.
2. A wind power network traffic flow control system according to claim 1 and also comprising:
the label setting module: setting a first electronic tag for each target wind power equipment, and setting a second electronic tag for each environment monitoring sensing equipment;
And a mapping matching module: mapping and matching the first electronic tag and the second electronic tag, determining an environment monitoring sensing device for monitoring the same target wind power device, and acquiring specific environment information.
3. A wind power network traffic flow control system according to claim 2 and wherein said environment acquisition module comprises:
Distribution unit: acquiring a current distribution position of each target wind power device, a geographic environment of the current distribution position and a device type, and constructing a detection area based on the target wind power device;
And a detection unit: and detecting and obtaining specific environmental information of each target wind power equipment under each execution time according to the position relation between the environmental monitoring sensing equipment and the detection area.
4. A wind power network traffic flow control system according to claim 3 and wherein said environment acquisition module further comprises:
Matrix construction unit: extracting wind power parameters from the detected specific environmental information of each target wind power equipment, and constructing a wind power parameter matrix aiming at the target wind power equipment, wherein the row of the wind power parameter matrix contains all wind power parameters related to the same execution subframe, and the column contains the same wind power parameters related to different execution subframes;
Matrix analysis unit: performing feature calculation on the wind power parameter matrix to obtain a feature vector, and performing corresponding sub-frame parameter comprehensive analysis on each row in the wind power parameter matrix to obtain a first coefficient and performing moment parameter trend analysis on each column in the wind power parameter matrix to obtain a second coefficient;
coefficient determination unit: determining and obtaining the environment coefficient corresponding to the execution time based on all the first coefficients and the second coefficients;
a value determination unit: and obtaining an operation monitoring value matched with the characteristic vector from a vector-value mapping table.
5. The wind power network traffic flow control system of claim 1, wherein the information capture module comprises:
traffic flow comparison unit: acquiring a network service flow issued to the target wind power equipment and a current service flow received by the target wind power equipment, and judging whether the current service flow is consistent with the network service flow;
An information capturing unit: if yes, capturing first information of the target wind power equipment at each execution time according to the network service flow;
Otherwise, capturing second information of the target wind power equipment according to the current service flow at each execution moment;
an information processing unit: and preprocessing the finally acquired information to obtain execution information.
6. A wind power network traffic flow control system according to claim 5 and wherein said information processing unit comprises:
A first determination block: carrying out logic analysis on each piece of sub information in the last acquired information under each execution time based on a logic analysis model, carrying out space mapping on each piece of sub information based on a logic-space mapping table, and determining to obtain service logic and a logic space matched with the service logic;
A second determination block: carrying out quantity statistics on service logics in the same logic space, and determining service importance and information concentration of the same logic space;
; wherein n1 represents service logic statistics data in the same logic space; representing the logic length of the ith 1 service logic in the same logic space; representing the total length of the accommodation information of the same logic space; Spatial weights representing the same logical space; The representation is based on all Is a variance of (2); logic weight of the ith 1 st business logic in the same logic space is represented; representing the total maximum weight of the same logical space when filled; representing the total average weight of the same logical space when filled; z1 represents the business importance of the same logical space; r1 represents the information concentration of the same logic space; Representing the total planned logic number of the same logic space when being filled; lg represents the sign of a logarithmic function;
screening: and screening and reserving sub-information from all sub-information under the same execution time according to the service importance and the information concentration to obtain the execution information under the same execution time.
7. The wind power network traffic flow control system of claim 1, wherein the anomaly prediction module comprises:
standard threshold determining unit: acquiring environment coefficients at corresponding execution time Operation monitoring valueBased on a preset standard threshold YH for each environmental parameter in the specific environmental information:
; wherein, Representing the data average value after the standardized processing of the specific environmental information under the corresponding execution time; Representing the value after the standardization processing of the jth environmental parameter under the corresponding execution time; representing the variance of the specific environment information under the corresponding execution time after the standardized processing; 0.01 represents an error coefficient of the entire environment; n represents the number of environmental parameters related to the specific environmental information; representing a preset standard threshold value under the jth environmental parameter under the corresponding execution time; representation is based on an operation monitoring value The threshold value adjusting coefficient of (3) is (0.01,0.02);
Operation monitoring abnormality judging unit: extracting features of the execution information of the corresponding execution time, analyzing the feature value and a preset standard threshold value, and judging whether the corresponding target wind power equipment is abnormal at the corresponding execution time;
; wherein, Representing all preset standard thresholds under corresponding execution timeIs the average value of (2); m represents the total number of sub-information in the execution information existing under the corresponding execution time; representing a signal value of the kth sub-information under the corresponding execution time after standardized processing according to the environmental parameters; an analysis value indicating a corresponding execution time; ln represents the sign of the logarithmic function;
And if the analysis value is larger than the preset execution value, judging that the target wind power equipment corresponding to the execution time is not abnormal.
8. The wind power network traffic flow control system of claim 1, wherein the anomaly prediction module further comprises:
Analysis unit: counting analysis values under different execution time points, constructing an analysis curve, and carrying out periodic analysis and abnormal occurrence frequency analysis under the same period on the analysis curve;
Prediction unit: according to the periodic analysis result and the analysis result of the occurrence times of the anomalies, determining the high-frequency anomalies in the future period, locking the future execution time consistent with the high-frequency anomalies, calling normal execution parameters consistent with the high-frequency anomalies and abnormal-to-normal abnormal regulation parameters from the analysis curve, and carrying out prediction and regulation feedback on the future execution parameters at the future execution time to control the corresponding target wind power equipment to continuously work.
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