CN117595504A - Intelligent monitoring and early warning method for power grid running state - Google Patents
Intelligent monitoring and early warning method for power grid running state Download PDFInfo
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Abstract
The invention discloses an intelligent monitoring and early warning method for an operation state of a power grid, which relates to the technical field of intelligent early warning, and comprises the following steps: classifying the power grid operation fault event database based on a power grid attribute classification rule, integrating the power grid operation fault event database according to the fault event attribute information obtained by classification to generate a power grid operation fault attribute event set, and performing traceability analysis to obtain a power grid abnormal interference signal set; training based on the information of a plurality of power grid operation scenes and the abnormal power grid interference signal sets, constructing a power grid operation abnormality prediction network library, further carrying out matching prediction on target application scenes and real-time interference signals, outputting a power grid danger prediction coefficient, and generating a danger early warning signal to carry out operation early warning when the danger prediction coefficient reaches a preset danger alarm threshold value. The intelligent monitoring of the running state of the power grid is realized, the monitoring analysis accuracy is improved, the state early warning timeliness is ensured, and the technical effect of safe running of the power grid is further ensured.
Description
Technical Field
The invention relates to the technical field of intelligent early warning, in particular to an intelligent monitoring and early warning method for the running state of a power grid.
Background
In order to ensure the safe operation of the power grid system, the operation state of the power grid system needs to be monitored in real time, and the purpose of the monitoring is to timely and accurately master the operation state of the power grid by adopting an effective monitoring means and an analysis and diagnosis technology, so that the safe, reliable and economic operation of the power grid system is ensured, the faults occurring in the power grid system are timely operated and maintained, and the accident endangering the safety is avoided. However, the intelligent degree of the power grid running state monitoring in the prior art is low, so that state early warning is not accurate in time.
Disclosure of Invention
The intelligent monitoring and early warning method for the running state of the power grid solves the technical problems that in the prior art, the intelligent degree of monitoring the running state of the power grid is low, and the state early warning is not accurate enough in time, achieves the intelligent monitoring of the running state of the power grid through the abnormal running prediction network of the power grid, improves the accuracy of monitoring and analysis, ensures the timeliness of the state early warning, and further ensures the technical effect of safe running of the power grid.
In view of the above problems, the invention provides an intelligent monitoring and early warning method for the running state of a power grid.
In a first aspect, the present application provides an intelligent monitoring and early warning method for an operation state of a power grid, where the method includes: acquiring a power grid operation fault event database through big data; acquiring a power grid attribute classification rule, classifying the power grid operation fault event database based on the power grid attribute classification rule, and acquiring fault event attribute information; integrating the power grid operation fault event database according to the fault event attribute information to generate a power grid operation fault attribute event set; performing traceability analysis on the power grid operation fault attribute event set to obtain a power grid abnormal interference signal set; respectively performing risk assessment training based on the information of a plurality of power grid operation scenes and the power grid abnormal interference signal set, and constructing a power grid operation abnormal prediction network library; monitoring, identifying and acquiring a real-time interference signal in a target application scene, and carrying out matching prediction based on the target application scene, the real-time interference signal and the power grid operation abnormality prediction network library to output a power grid danger prediction coefficient; and when the power grid danger prediction coefficient reaches a preset danger alarm threshold value, generating a danger early warning signal to perform operation early warning.
On the other hand, the application also provides an intelligent monitoring and early warning system for the running state of the power grid, and the system comprises: the database acquisition module is used for acquiring a power grid operation fault event database through big data; the attribute classification module is used for acquiring a power grid attribute classification rule, classifying the power grid operation fault event database based on the power grid attribute classification rule, and acquiring fault event attribute information; the database integration module is used for integrating the power grid operation fault event database according to the fault event attribute information to generate a power grid operation fault attribute event set; the tracing analysis module is used for tracing analysis on the power grid operation fault attribute event set to obtain a power grid abnormal interference signal set; the risk assessment training module is used for respectively carrying out risk assessment training based on the information of a plurality of power grid operation scenes and the power grid abnormal interference signal set and constructing a power grid operation abnormal prediction network library; the power grid risk prediction module is used for monitoring, identifying and acquiring a real-time interference signal under a target application scene, carrying out matching prediction on the basis of the target application scene, the real-time interference signal and the power grid operation abnormality prediction network library, and outputting a power grid risk prediction coefficient; and the power grid operation early warning module is used for generating a danger early warning signal to perform operation early warning when the power grid danger prediction coefficient reaches a preset danger alarm threshold value.
In a third aspect, the present application provides an electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, the computer program implementing the steps of any of the methods described above when executed by the processor.
In a fourth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of any of the methods described above.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
because the power grid operation fault event database is acquired through big data, and is classified based on the power grid attribute classification rule, fault event attribute information is acquired, and the power grid operation fault event database is integrated to generate a power grid operation fault attribute event set; performing traceability analysis on the power grid operation fault attribute event set to obtain a power grid abnormal interference signal set, performing risk assessment training on the basis of the power grid operation scene information and the power grid abnormal interference signal set, building a power grid operation abnormal prediction network library, performing matching prediction on the basis of the target application scene obtained by monitoring and identification, the real-time interference signal and the power grid operation abnormal prediction network library, and outputting a power grid risk prediction coefficient; and when the power grid danger prediction coefficient reaches a preset danger alarm threshold value, generating a danger early warning signal to perform operation early warning. The intelligent monitoring of the running state of the power grid is realized through the abnormal running prediction network of the power grid, the accuracy of monitoring and analysis is improved, the state early warning timeliness is ensured, and the technical effect of safe running of the power grid is further ensured.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
Fig. 1 is a schematic flow chart of an intelligent monitoring and early warning method for a power grid running state;
fig. 2 is a schematic flow chart of acquiring fault event attribute information in an intelligent monitoring and early warning method for a power grid running state;
fig. 3 is a schematic structural diagram of an intelligent monitoring and early warning system for power grid running state;
fig. 4 is a schematic structural diagram of an exemplary electronic device of the present application.
Reference numerals illustrate: the system comprises a database acquisition module 11, an attribute classification module 12, a database integration module 13, a traceability analysis module 14, a risk assessment training module 15, a power grid risk prediction module 16, a power grid operation early warning module 17, a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, an operating system 1151, an application 1152 and a user interface 1160.
Detailed Description
In the description of the present application, those skilled in the art will appreciate that the present application may be embodied as methods, apparatuses, electronic devices, and computer-readable storage media. Accordingly, the present application may be embodied in the following forms: complete hardware, complete software (including firmware, resident software, micro-code, etc.), a combination of hardware and software. Furthermore, in some embodiments, the present application may also be embodied in the form of a computer program product in one or more computer-readable storage media, which contain computer program code.
Any combination of one or more computer-readable storage media may be employed by the computer-readable storage media described above. The computer-readable storage medium includes: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer readable storage medium include the following: portable computer magnetic disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disk read-only memories, optical storage devices, magnetic storage devices, or any combination thereof. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, device.
The technical scheme of the application is that the acquisition, storage, use, processing and the like of the data meet the relevant regulations of national laws.
The present application describes methods, apparatus, and electronic devices provided by the flowchart and/or block diagram.
It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose 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 means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in a computer readable storage medium that can cause a computer or other programmable data processing apparatus to function in a particular manner. Thus, instructions stored in a computer-readable storage medium produce an instruction means which implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The present application is described below with reference to the drawings in the present application.
Example 1
As shown in fig. 1, the present application provides an intelligent monitoring and early warning method for an operation state of a power grid, where the method includes:
step S1: acquiring a power grid operation fault event database through big data;
specifically, in order to ensure the safe operation of the power grid system, the operation state of the power grid system needs to be monitored in real time, and the purpose of monitoring is to timely and accurately master the operation state of the power grid by adopting an effective monitoring means and an analysis and diagnosis technology, so that the safe, reliable and economic operation of the power grid system is ensured, the faults occurring in the power grid system are timely operated and maintained, and the safety-endangered accidents are avoided.
In order to realize intelligent monitoring of the running state of the power grid, a power grid running fault event database is acquired through searching and collecting big data, wherein the power grid running fault event database is historical power grid running fault data information and comprises relevant fault information such as fault occurrence time, fault type, influence, position and the like of a fault event. And a data basis is provided for subsequent operation fault analysis and prediction by collecting massive related power grid data, so that the data analysis accuracy is improved.
Step S2: acquiring a power grid attribute classification rule, classifying the power grid operation fault event database based on the power grid attribute classification rule, and acquiring fault event attribute information;
step S3: integrating the power grid operation fault event database according to the fault event attribute information to generate a power grid operation fault attribute event set;
as shown in fig. 2, further, the step of obtaining the fault event attribute information further includes:
performing index extraction on the power grid attribute classification rule to obtain a power grid fault attribute classification index, wherein the power grid fault attribute classification index comprises a fault type, a fault grade and a fault cause;
extracting knowledge content according to the power grid fault attribute classification indexes to obtain a fault attribute knowledge node set;
Constructing a power grid attribute classifier based on the fault attribute knowledge node set;
and carrying out attribute classification on the power grid operation fault event database based on the power grid attribute classifier to acquire the fault event attribute information.
Specifically, a power grid attribute classification rule is formulated and acquired, wherein the power grid attribute classification rule is a power grid fault data attribute classification basis, and the power grid fault data attribute classification basis can be set by self-supplement through experience setting. Classifying the power grid operation fault event database based on the power grid attribute classification rule, firstly extracting indexes of the power grid attribute classification rule to obtain power grid fault attribute classification indexes corresponding to the classification rule, wherein the power grid fault attribute classification indexes are operation fault specific classification basis indexes including fault types, fault grades, fault causes and the like. And extracting knowledge content according to the grid fault attribute classification indexes, namely determining specific content of each attribute index, wherein the fault type indexes comprise short-circuit faults, disconnection faults, equipment faults, overload faults and the like by way of example, taking each type content of each attribute index as a knowledge node, and extracting to obtain a fault attribute knowledge node set.
And taking the fault attribute knowledge node set as a tree structure attribute classification model to construct a power grid attribute classifier, wherein the power grid attribute classifier is used for classifying the attributes of the power grid operation fault event data. And classifying the attribute of each fault event in the power grid operation fault event database based on the power grid attribute classifier, and acquiring fault event attribute information after each fault event is classified. And integrating the power grid operation fault event database according to the fault event attribute information, integrating the fault events with the same attribute type into one type, and generating a power grid operation fault attribute event set with integrated attributes. The method and the system realize quick and accurate classification of the power grid operation fault event attribute, and further improve classification, integration and standardization of a power grid operation fault event database.
Step S4: performing traceability analysis on the power grid operation fault attribute event set to obtain a power grid abnormal interference signal set;
further, the step of obtaining the abnormal interference signal set of the power grid further includes:
performing data tracing based on the power grid operation fault attribute event set to obtain a power grid operation tracing data stream;
constructing a power grid operation fault tag library according to the fault event attribute information;
Performing sample labeling division on the power grid operation traceability data stream based on the power grid operation fault tag library to generate a power grid operation positive sample and an operation fault negative sample;
training the power grid operation positive sample and the operation fault negative sample by using a neural network structure to generate a power grid operation fault identification network;
and identifying and analyzing the power grid operation fault attribute event set based on the power grid operation fault identification network, and outputting the power grid abnormal interference signal set.
Specifically, the power grid operation fault attribute event set is subjected to traceability analysis, and firstly, data traceability is performed based on the power grid operation fault attribute event set, namely, operation data within a preset range of fault event occurrence time, such as traceability and extraction of operation data on the current day of fault event occurrence, is performed, so that a power grid operation traceability data stream is obtained. And constructing a power grid operation fault tag library according to the fault type index in the fault event attribute information, wherein the power grid operation fault tag library comprises normal operation tags and operation fault tags corresponding to the fault type index. And carrying out sample labeling and dividing on preset proportion data in the power grid operation traceability data stream as training sample data based on the power grid operation fault tag library, and generating a power grid operation positive sample corresponding to a normal operation tag and an operation fault negative sample corresponding to an operation fault tag.
Training the power grid operation positive sample and the power grid operation fault negative sample by using a neural network structure to generate a power grid operation fault identification network, wherein the power grid operation fault identification network is a neural network model and is used for quickly identifying power grid operation fault data. And identifying and analyzing the power grid operation fault attribute event set based on the power grid operation fault identification network, and outputting a power grid abnormal interference signal set, namely a power grid operation fault data stream. The intelligent recognition of the abnormal interference signals of the power grid is realized by generating the power grid operation fault recognition network, and the recognition accuracy and recognition efficiency of the abnormal signals are improved.
Step S5: respectively performing risk assessment training based on the information of a plurality of power grid operation scenes and the power grid abnormal interference signal set, and constructing a power grid operation abnormal prediction network library;
furthermore, the step of building the network library for predicting the abnormal operation of the power grid further comprises the following steps:
formulating power grid abnormality evaluation assignment factors, wherein the power grid abnormality evaluation assignment factors comprise an influence range, a loss value and an abnormality depth;
performing assignment marking on the power grid abnormal interference signal set based on the power grid abnormal evaluation assignment factors to generate a power grid abnormal signal sample set;
Respectively carrying out network model training according to the power grid abnormal signal sample set to obtain a power grid operation abnormal prediction network set;
and forming the power grid operation abnormality prediction network library based on the power grid operation abnormality prediction network set.
Further, the step of obtaining the network set for predicting abnormal operation of the power grid further includes:
extracting elements from the plurality of power grid operation scene information to obtain power grid operation scene parameter information;
determining operation scene calibration parameters according to the power grid operation scene parameter information;
clustering and dividing the power grid abnormal signal sample set according to the operation scene calibration parameters to obtain a power grid abnormal clustering training data set;
training is carried out based on the power grid abnormal cluster training data set respectively, and a power grid operation abnormal prediction network set is obtained.
Specifically, the power grid operation scene comprises a resident power supply scene, a school power supply scene, an application scene combining different power consumption and different power demands of photovoltaic power generation and the like, and the risk assessment training is respectively carried out based on the information of a plurality of power grid operation scenes and the abnormal interference signal set of the power grid. Firstly, formulating a power grid abnormality evaluation assignment factor, wherein the power grid abnormality evaluation assignment factor is an influence index for evaluating a power grid abnormality interference signal and comprises an influence range, namely an abnormality influence scene range; loss value, i.e., the economic loss value of electricity caused by abnormality; the depth of abnormality, i.e., the severity of the abnormality, etc. And carrying out assignment marking on the power grid abnormal interference signal set based on the power grid abnormal evaluation assignment factors, carrying out grade refinement on the abnormal assignment factors through abnormal influence data of the power grid abnormal interference signals, formulating a factor grade assignment table, carrying out sample factor type assignment on the power grid abnormal interference signal set according to the factor grade assignment table, and weighting factor assignment results to obtain the power grid risk coefficient. And carrying out assignment marking according to the power grid risk coefficient, and respectively taking a power grid abnormal interference signal set corresponding to the coefficient assignment marking as a power grid abnormal signal sample set, wherein the power grid abnormal signal sample set comprises power grid abnormal signals and power grid risk coefficient evaluation assignment results, so as to be used for training a power grid operation abnormal prediction model.
And respectively performing network model training according to the power grid abnormal signal sample set, and firstly extracting elements of the plurality of power grid operation scene information, namely determining operation scene parameters of the power grid operation scene information to obtain power grid operation scene parameter information, wherein the power grid operation scene parameter information comprises related power parameters such as scene required power, equipment operation power consumption and the like. According to the power grid operation scene parameter information, a scene parameter grade table can be divided by an expert, so that parameter grade is matched with the power grid operation scene parameter information, and a matching result is determined to be an operation scene calibration parameter. And carrying out clustering division on the power grid abnormal signal sample set according to the operation scene calibration parameters, namely carrying out signal sample set partitioning integration according to the operation scene parameter types to obtain a power grid abnormal clustering training data set which is clustered according to the operation scene calibration parameters. And training based on the power grid abnormal cluster training data sets respectively to obtain a power grid operation abnormal prediction network set corresponding to the operation scene calibration parameters. Based on the power grid operation abnormity prediction network set, a power grid operation abnormity prediction network library is formed, the scene coverage comprehensiveness of the power grid operation abnormity prediction network is improved, personalized scene matching prediction is further realized, and the operation state abnormity prediction analysis accuracy is improved.
Step S6: monitoring, identifying and acquiring a real-time interference signal in a target application scene, and carrying out matching prediction based on the target application scene, the real-time interference signal and the power grid operation abnormality prediction network library to output a power grid danger prediction coefficient;
step S7: and when the power grid danger prediction coefficient reaches a preset danger alarm threshold value, generating a danger early warning signal to perform operation early warning.
Specifically, under the condition that the power system sensor group comprises power sensors such as current, voltage and power consumption, real-time monitoring and identification are used for obtaining a target application scene, namely, a real-time monitoring data stream under the operation scene of a power grid to be monitored, further, fault data identification is carried out on the real-time monitoring data stream through the operation fault identification network of the power grid, and a real-time interference signal of the current application scene is output. And based on the matching of the operation scene calibration parameters of the target application scene and the power grid operation abnormity prediction network library, calling and obtaining a power grid operation abnormity prediction network corresponding to the scene calibration parameters, further predicting according to the real-time interference signals of the prediction network, outputting a power grid risk prediction coefficient, and representing the current power grid real-time operation risk degree.
When the power grid risk prediction coefficient reaches a preset risk alarm threshold, the current operation risk degree of the power grid is indicated to reach an early warning standard, wherein the preset risk alarm threshold can be set according to actual operation experience, and a corresponding level of risk early warning signal is generated according to the power grid risk prediction coefficient to perform power grid operation early warning, so that power grid personnel can perform risk safety operation and maintenance in time. The intelligent monitoring of the running state of the power grid is realized, the early warning timeliness of the dangerous state of the power grid is ensured, and the safe running of the power grid is further ensured.
Further, the training is performed on the power grid anomaly cluster training data set based on the power grid anomaly evaluation assignment factors, and the steps of the application further include:
dividing the power grid abnormal cluster training data set according to the power grid abnormal evaluation assignment factors to obtain a cluster training abnormal factor data set;
respectively carrying out network structure training based on the clustering training abnormal factor data set, and respectively generating an influence range prediction network, a loss value prediction network and an abnormal depth prediction network;
and fusing the influence range prediction network, the loss value prediction network and the abnormal depth prediction network to generate a power grid operation abnormal prediction network.
Further, the generating a network for predicting abnormal operation of the power grid further includes:
carrying out criticality analysis on the power grid abnormity evaluation assignment factors by using an entropy weight method, and determining abnormity factor criticality distribution information;
the abnormal factor criticality distribution information is used as a prediction network fusion coefficient;
and carrying out weighted fusion on the influence range prediction network, the loss value prediction network and the abnormal depth prediction network based on the prediction network fusion coefficient to generate the power grid operation abnormal prediction network.
Specifically, the training process for the power grid anomaly cluster training data set based on the power grid anomaly evaluation assignment factors is as follows: firstly, dividing the power grid abnormal cluster training data set according to the power grid abnormal evaluation assignment factors to obtain a cluster training abnormal factor data set which comprises power grid abnormal signals and factor assignment results. And respectively carrying out network structure training based on the clustering training abnormal factor data set, and respectively generating a corresponding influence range prediction network, a loss value prediction network and an abnormal depth prediction network for factor abnormal coefficient prediction. And in order to improve the output accuracy of the anomaly prediction network, the influence range prediction network, the loss value prediction network and the anomaly depth prediction network are fused.
In order to improve the prediction accuracy of the fusion model, the objective weighting method, namely an entropy weighting method, is utilized to carry out key analysis on the power grid abnormal evaluation assignment factors, namely the weight of each evaluation factor is calculated by utilizing the information entropy, and abnormal factor criticality assignment information, namely abnormal factor weight assignment information, is determined according to the weight assignment result. And taking the abnormal factor criticality distribution information as a prediction network fusion coefficient, wherein the larger the criticality weight is, the larger the corresponding fusion coefficient is, and the larger the voting weight in the fusion model is. And carrying out weighted fusion on the influence range prediction network, the loss value prediction network and the abnormal depth prediction network based on the prediction network fusion coefficient to generate a power grid operation abnormal prediction network with higher output accuracy. The prediction accuracy of the abnormal operation of the power grid is improved, the state early warning timeliness is guaranteed, and the safe operation of the power grid is further guaranteed.
In summary, the intelligent monitoring and early warning method for the running state of the power grid has the following technical effects:
because the power grid operation fault event database is acquired through big data, and is classified based on the power grid attribute classification rule, fault event attribute information is acquired, and the power grid operation fault event database is integrated to generate a power grid operation fault attribute event set; performing traceability analysis on the power grid operation fault attribute event set to obtain a power grid abnormal interference signal set, performing risk assessment training on the basis of the power grid operation scene information and the power grid abnormal interference signal set, building a power grid operation abnormal prediction network library, performing matching prediction on the basis of the target application scene obtained by monitoring and identification, the real-time interference signal and the power grid operation abnormal prediction network library, and outputting a power grid risk prediction coefficient; and when the power grid danger prediction coefficient reaches a preset danger alarm threshold value, generating a danger early warning signal to perform operation early warning. The intelligent monitoring of the running state of the power grid is realized through the abnormal running prediction network of the power grid, the accuracy of monitoring and analysis is improved, the state early warning timeliness is ensured, and the technical effect of safe running of the power grid is further ensured.
Example two
Based on the same inventive concept as the intelligent monitoring and early warning method for the running state of the power grid in the foregoing embodiment, the invention also provides an intelligent monitoring and early warning system for the running state of the power grid, as shown in fig. 3, the system comprises:
the database acquisition module 11 is used for acquiring a power grid operation fault event database through big data;
the attribute classification module 12 is configured to obtain a power grid attribute classification rule, classify the power grid operation fault event database based on the power grid attribute classification rule, and obtain fault event attribute information;
the database integration module 13 is configured to integrate the power grid operation fault event database according to the fault event attribute information, so as to generate a power grid operation fault attribute event set;
the tracing analysis module 14 is configured to perform tracing analysis on the power grid operation fault attribute event set to obtain a power grid abnormal interference signal set;
the risk assessment training module 15 is used for respectively carrying out risk assessment training based on the information of a plurality of power grid operation scenes and the power grid abnormal interference signal set, and constructing a power grid operation abnormal prediction network library;
the power grid risk prediction module 16 is used for monitoring, identifying and acquiring a real-time interference signal under a target application scene, performing matching prediction based on the target application scene, the real-time interference signal and the power grid operation abnormality prediction network library, and outputting a power grid risk prediction coefficient;
And the power grid operation early warning module 17 is used for generating a danger early warning signal to perform operation early warning when the power grid danger prediction coefficient reaches a preset danger alarm threshold value.
Further, the system further comprises:
the index extraction unit is used for extracting indexes of the power grid attribute classification rules to obtain power grid fault attribute classification indexes, wherein the power grid fault attribute classification indexes comprise fault types, fault grades and fault causes;
the knowledge content extraction unit is used for extracting knowledge content according to the power grid fault attribute classification indexes to obtain a fault attribute knowledge node set;
the classifier construction unit is used for constructing a power grid attribute classifier based on the fault attribute knowledge node set;
and the attribute classification unit is used for classifying the attributes of the power grid operation fault event database based on the power grid attribute classifier, and acquiring the fault event attribute information.
Further, the system further comprises:
the data tracing unit is used for tracing data based on the power grid operation fault attribute event set to obtain a power grid operation tracing data stream;
the label library construction unit is used for constructing a power grid operation fault label library according to the fault event attribute information;
The sample labeling and dividing unit is used for carrying out sample labeling and dividing on the power grid operation traceability data stream based on the power grid operation fault tag library to generate a power grid operation positive sample and an operation fault negative sample;
the fault identification network generation unit is used for training the power grid operation positive sample and the operation fault negative sample by using a neural network structure to generate a power grid operation fault identification network;
and the anomaly identification and analysis unit is used for carrying out identification and analysis on the power grid operation fault attribute event set based on the power grid operation fault identification network and outputting the power grid anomaly interference signal set.
Further, the system further comprises:
the evaluation and assignment factor making unit is used for making power grid abnormality evaluation and assignment factors, wherein the power grid abnormality evaluation and assignment factors comprise an influence range, a loss value and an abnormality depth;
the signal set dividing unit is used for carrying out assignment marking on the power grid abnormal interference signal set based on the power grid abnormal evaluation assignment factors to generate a power grid abnormal signal sample set;
the network model training unit is used for respectively carrying out network model training according to the power grid abnormal signal sample set to obtain a power grid operation abnormal prediction network set;
The prediction network library composition unit is used for forming the power grid operation abnormality prediction network library based on the power grid operation abnormality prediction network set.
Further, the system further comprises:
the element extraction unit is used for extracting elements from the plurality of power grid operation scene information to obtain power grid operation scene parameter information;
the calibration parameter determining unit is used for determining the calibration parameters of the operation scene according to the operation scene parameter information of the power grid;
the clustering unit is used for carrying out clustering on the power grid abnormal signal sample set according to the operation scene calibration parameters to obtain a power grid abnormal clustering training data set;
and the data set training unit is used for respectively training based on the power grid abnormal cluster training data sets to obtain a power grid operation abnormal prediction network set.
Further, the system further comprises:
the data set dividing unit is used for dividing the power grid abnormal cluster training data set according to the power grid abnormal evaluation assignment factors to obtain a cluster training abnormal factor data set;
the network structure training unit is used for respectively carrying out network structure training based on the clustering training abnormal factor data set and respectively generating an influence range prediction network, a loss value prediction network and an abnormal depth prediction network;
And the prediction network fusion unit is used for fusing the influence range prediction network, the loss value prediction network and the abnormal depth prediction network to generate a power grid operation abnormal prediction network.
Further, the system further comprises:
the criticality analysis unit is used for carrying out criticality analysis on the power grid abnormity evaluation assignment factors by utilizing an entropy weight method and determining abnormity factor criticality distribution information;
the fusion coefficient obtaining unit is used for taking the abnormal factor criticality distribution information as a prediction network fusion coefficient;
and the weighted fusion unit is used for carrying out weighted fusion on the influence range prediction network, the loss value prediction network and the abnormal depth prediction network based on the prediction network fusion coefficient to generate the power grid operation abnormal prediction network.
The various variations and specific examples of the intelligent monitoring and early warning method for the power grid operation state in the first embodiment of fig. 1 are also applicable to the intelligent monitoring and early warning system for the power grid operation state in this embodiment, and by the foregoing detailed description of the intelligent monitoring and early warning method for the power grid operation state, those skilled in the art can clearly know the implementation method for the intelligent monitoring and early warning system for the power grid operation state in this embodiment, so that the description is omitted herein for brevity.
In addition, the application further provides an electronic device, which comprises a bus, a transceiver, a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the transceiver, the memory and the processor are respectively connected through the bus, and when the computer program is executed by the processor, the processes of the method embodiment for controlling output data are realized, and the same technical effects can be achieved, so that repetition is avoided and redundant description is omitted.
Exemplary electronic device
In particular, referring to FIG. 4, the present application also provides an electronic device comprising a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.
In this application, the electronic device further includes: computer programs stored on the memory 1150 and executable on the processor 1120, which when executed by the processor 1120, implement the various processes of the method embodiments described above for controlling output data.
A transceiver 1130 for receiving and transmitting data under the control of the processor 1120.
In this application, a bus architecture (represented by bus 1110), the bus 1110 may include any number of interconnected buses and bridges, with the bus 1110 connecting various circuits, including one or more processors, represented by the processor 1120, and memory, represented by the memory 1150.
Bus 1110 represents one or more of any of several types of bus structures, including a memory bus and memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such an architecture includes: industry standard architecture buses, micro-channel architecture buses, expansion buses, video electronics standards association, and peripheral component interconnect buses.
Processor 1120 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments may be implemented by instructions in the form of integrated logic circuits in hardware or software in a processor. The processor includes: general purpose processors, central processing units, network processors, digital signal processors, application specific integrated circuits, field programmable gate arrays, complex programmable logic devices, programmable logic arrays, micro control units or other programmable logic devices, discrete gates, transistor logic devices, discrete hardware components. The methods, steps and logic blocks disclosed in the present application may be implemented or performed. For example, the processor may be a single-core processor or a multi-core processor, and the processor may be integrated on a single chip or located on multiple different chips.
The processor 1120 may be a microprocessor or any conventional processor. The method steps disclosed in connection with the present application may be performed directly by a hardware decoding processor or by a combination of hardware and software modules in a decoding processor. The software modules may be located in random access memory, flash memory, read only memory, programmable read only memory, erasable programmable read only memory, registers, and the like, as known in the art. The readable storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
Bus 1110 may also connect together various other circuits such as peripheral devices, voltage regulators, or power management circuits, bus interface 1140 providing an interface between bus 1110 and transceiver 1130, all of which are well known in the art. Therefore, this application will not be further described.
The transceiver 1130 may be one element or a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 is configured to transmit the data processed by the processor 1120 to the other devices. Depending on the nature of the computer device, a user interface 1160 may also be provided, for example: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.
It should be appreciated that in this application, the memory 1150 may further include memory located remotely from the processor 1120, which may be connected to a server through a network. One or more portions of the above-described networks may be an ad hoc network, an intranet, an extranet, a virtual private network, a local area network, a wireless local area network, a wide area network, a wireless wide area network, a metropolitan area network, an internet, a public switched telephone network, a plain old telephone service network, a cellular telephone network, a wireless fidelity network, and combinations of two or more of the foregoing. For example, the cellular telephone network and wireless network may be global system for mobile communications devices, code division multiple access devices, worldwide interoperability for microwave access devices, general packet radio service devices, wideband code division multiple access devices, long term evolution devices, LTE frequency division duplex devices, LTE time division duplex devices, advanced long term evolution devices, general mobile communications devices, enhanced mobile broadband devices, mass machine class communications devices, ultra-reliable low-latency communications devices, and the like.
It should be appreciated that the memory 1150 in this application may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. Wherein the nonvolatile memory includes: read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, or flash memory.
The volatile memory includes: random access memory, which serves as an external cache. By way of example, and not limitation, many forms of RAM are available, such as: static random access memory, dynamic random access memory, synchronous dynamic random access memory, double data rate synchronous dynamic random access memory, enhanced synchronous dynamic random access memory, synchronous link dynamic random access memory, and direct memory bus random access memory. The memory 1150 of the electronic device described herein includes, but is not limited to, the memory described above and any other suitable type of memory.
In this application, memory 1150 stores the following elements of operating system 1151 and application programs 1152: an executable module, a data structure, or a subset thereof, or an extended set thereof.
Specifically, the operating system 1151 includes various device programs, such as: a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The applications 1152 include various applications such as: and the media player and the browser are used for realizing various application services. A program for implementing the method of the present application may be included in the application 1152. The application 1152 includes: applets, objects, components, logic, data structures, and other computer apparatus-executable instructions that perform particular tasks or implement particular abstract data types.
In addition, the application further provides a computer readable storage medium, on which a computer program is stored, where the computer program when executed by a processor implements each process of the above-mentioned method embodiment for controlling output data, and the same technical effects can be achieved, and for avoiding repetition, a detailed description is omitted herein.
The foregoing is merely a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. An intelligent monitoring and early warning method for the running state of a power grid is characterized by comprising the following steps:
acquiring a power grid operation fault event database through big data;
acquiring a power grid attribute classification rule, classifying the power grid operation fault event database based on the power grid attribute classification rule, and acquiring fault event attribute information;
integrating the power grid operation fault event database according to the fault event attribute information to generate a power grid operation fault attribute event set;
Performing traceability analysis on the power grid operation fault attribute event set to obtain a power grid abnormal interference signal set;
respectively performing risk assessment training based on the information of a plurality of power grid operation scenes and the power grid abnormal interference signal set, and constructing a power grid operation abnormal prediction network library;
monitoring, identifying and acquiring a real-time interference signal in a target application scene, and carrying out matching prediction based on the target application scene, the real-time interference signal and the power grid operation abnormality prediction network library to output a power grid danger prediction coefficient;
and when the power grid danger prediction coefficient reaches a preset danger alarm threshold value, generating a danger early warning signal to perform operation early warning.
2. The method of claim 1, wherein the obtaining fault event attribute information comprises:
performing index extraction on the power grid attribute classification rule to obtain a power grid fault attribute classification index, wherein the power grid fault attribute classification index comprises a fault type, a fault grade and a fault cause;
extracting knowledge content according to the power grid fault attribute classification indexes to obtain a fault attribute knowledge node set;
constructing a power grid attribute classifier based on the fault attribute knowledge node set;
And carrying out attribute classification on the power grid operation fault event database based on the power grid attribute classifier to acquire the fault event attribute information.
3. The method of claim 1, wherein the obtaining the set of grid anomaly interference signals comprises:
performing data tracing based on the power grid operation fault attribute event set to obtain a power grid operation tracing data stream;
constructing a power grid operation fault tag library according to the fault event attribute information;
performing sample labeling division on the power grid operation traceability data stream based on the power grid operation fault tag library to generate a power grid operation positive sample and an operation fault negative sample;
training the power grid operation positive sample and the operation fault negative sample by using a neural network structure to generate a power grid operation fault identification network;
and identifying and analyzing the power grid operation fault attribute event set based on the power grid operation fault identification network, and outputting the power grid abnormal interference signal set.
4. The method of claim 1, wherein the grid-operation anomaly prediction network library comprises:
formulating power grid abnormality evaluation assignment factors, wherein the power grid abnormality evaluation assignment factors comprise an influence range, a loss value and an abnormality depth;
Performing assignment marking on the power grid abnormal interference signal set based on the power grid abnormal evaluation assignment factors to generate a power grid abnormal signal sample set;
respectively carrying out network model training according to the power grid abnormal signal sample set to obtain a power grid operation abnormal prediction network set;
and forming the power grid operation abnormality prediction network library based on the power grid operation abnormality prediction network set.
5. The method of claim 4, wherein the deriving a set of grid-operation anomaly prediction networks comprises:
extracting elements from the plurality of power grid operation scene information to obtain power grid operation scene parameter information;
determining operation scene calibration parameters according to the power grid operation scene parameter information;
clustering and dividing the power grid abnormal signal sample set according to the operation scene calibration parameters to obtain a power grid abnormal clustering training data set;
training is carried out based on the power grid abnormal cluster training data set respectively, and a power grid operation abnormal prediction network set is obtained.
6. The method of claim 5, wherein the training the grid anomaly cluster training data sets based on the grid anomaly evaluation valuation factors, respectively, comprises:
Dividing the power grid abnormal cluster training data set according to the power grid abnormal evaluation assignment factors to obtain a cluster training abnormal factor data set;
respectively carrying out network structure training based on the clustering training abnormal factor data set, and respectively generating an influence range prediction network, a loss value prediction network and an abnormal depth prediction network;
and fusing the influence range prediction network, the loss value prediction network and the abnormal depth prediction network to generate a power grid operation abnormal prediction network.
7. The method of claim 6, wherein the generating a grid-operation anomaly prediction network comprises:
carrying out criticality analysis on the power grid abnormity evaluation assignment factors by using an entropy weight method, and determining abnormity factor criticality distribution information;
the abnormal factor criticality distribution information is used as a prediction network fusion coefficient;
and carrying out weighted fusion on the influence range prediction network, the loss value prediction network and the abnormal depth prediction network based on the prediction network fusion coefficient to generate the power grid operation abnormal prediction network.
8. An intelligent monitoring and early warning system for an operation state of a power grid, which is characterized by comprising:
The database acquisition module is used for acquiring a power grid operation fault event database through big data;
the attribute classification module is used for acquiring a power grid attribute classification rule, classifying the power grid operation fault event database based on the power grid attribute classification rule, and acquiring fault event attribute information;
the database integration module is used for integrating the power grid operation fault event database according to the fault event attribute information to generate a power grid operation fault attribute event set;
the tracing analysis module is used for tracing analysis on the power grid operation fault attribute event set to obtain a power grid abnormal interference signal set;
the risk assessment training module is used for respectively carrying out risk assessment training based on the information of a plurality of power grid operation scenes and the power grid abnormal interference signal set and constructing a power grid operation abnormal prediction network library;
the power grid risk prediction module is used for monitoring, identifying and acquiring a real-time interference signal under a target application scene, carrying out matching prediction on the basis of the target application scene, the real-time interference signal and the power grid operation abnormality prediction network library, and outputting a power grid risk prediction coefficient;
and the power grid operation early warning module is used for generating a danger early warning signal to perform operation early warning when the power grid danger prediction coefficient reaches a preset danger alarm threshold value.
9. An electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and operable on the processor, the transceiver, the memory and the processor being connected by the bus, characterized in that the computer program when executed by the processor implements the steps of a method for intelligent monitoring and pre-warning of an operation state of an electrical network according to any one of claims 1-7.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of a method for intelligent monitoring and pre-warning of an operation state of an electrical network according to any one of claims 1-7.
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| CN117955245B (en) * | 2024-03-25 | 2024-06-04 | 广东电网有限责任公司佛山供电局 | Method and device for determining running state of power grid, storage medium and electronic equipment |
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