Substation operation state monitoring method and device, electronic equipment and storage medium

Abstract

The invention provides a transformer substation operation state monitoring method and device, electronic equipment and a storage medium, and relates to the technical field of transformer substation data monitoring. Firstly, multi-source data of a transformer is acquired; inputting the multi-source data into a preset multi-physical field mechanism model, and obtaining a first operation state and a first change trend corresponding to the multi-source data; inputting the multi-source data into a preset artificial intelligence model, and obtaining a second operation state and a second change trend corresponding to the multi-source data; the first operation state and the second operation state are fused, the first change trend and the second change trend are fused, and the corresponding final operation state and the final change trend of the transformer are determined; and finally, monitoring the operation state of the transformer substation according to the final operation state and the final change trend. The method has the advantages that the monitoring result of the operation state of the transformer substation is more accurate, and complex working conditions can be met.

Description

Technical Field

[0001] The present application relates to the technical field of substation data monitoring, and in particular to a substation operation status monitoring method, device, electronic equipment and storage medium. Background Art

[0002] Comprehensive, timely and accurate analysis and prediction of the operating status of transformers in substations is an important safeguard for the overall safety of substations and the basis for the reliable operation of power grids. With the access of a high proportion of new energy sources, the strong uncertainty, volatility and large amount of harmonics in the new power system will cause the main transformers of substations to be subjected to more extreme and drastically changing operating conditions. It is necessary to improve the practical level of digital technologies such as intelligent perception, data analysis and situation prediction to support the lean management and efficient optimization operation of transformers.

[0003] At present, the operation status monitoring of substations generally adopts the method of mechanism model simulation analysis. However, the prediction results of the state prediction using a single mechanism model are sometimes not accurate, and at the same time, they cannot meet the performance requirements in complex environments.

[0004] In summary, the current solutions for substation operation monitoring have low accuracy and cannot meet the requirements of complex working conditions. Summary of the invention

[0005] The purpose of the present application is to provide a substation operation status monitoring method, device, electronic equipment and storage medium to solve the problem that the substation operation monitoring in the prior art has low accuracy and cannot meet the requirements of complex working conditions.

[0006] In order to achieve the above purpose, the technical solution adopted in the embodiment of the present application is as follows:

[0007] In a first aspect, an embodiment of the present application provides a method for monitoring the operation status of a substation, the method comprising:

[0008] Obtain multi-source data of the transformer; wherein the multi-source data includes online electric energy detection data, historical electric energy detection data, preset direction experimental data, transformer size data, operating environment data and performance index data;

[0009] Inputting the multi-source data into a preset multi-physics field mechanism model, and obtaining a first operating state and a first change trend corresponding to the multi-source data;

[0010] Inputting the multi-source data into a preset artificial intelligence model, and obtaining a second operating state and a second change trend corresponding to the multi-source data;

[0011] Merging the first operating state with the second operating state, and merging the first change trend with the second change trend, and determining a final operating state and a final change trend corresponding to the transformer;

[0012] The operating state of the substation is monitored according to the final operating state and the final change trend.

[0013] Optionally, the final operating state satisfies the formula:

[0014] X j =a1X 1j +b1X 2j

[0015] The final change trend satisfies the formula:

[0016] Y j =a2Y 1j +b2Y 2j

[0017] Among them, X j represents the final running state of the jth data, X 1j represents the first running state of the jth data, X 2j Indicates the second running state of the jth data; Y j Indicates the final change trend of the jth data, Y 1j Indicates the first change trend of the jth data, Y 2j represents the second change trend of the j-th data; a1, a2, b1 and b2 represent weights respectively, and a1+b1=1, a2+b2=1, a1>b1, a2<b2.

[0018] Optionally, a1=b2 and b1=a2.

[0019] Optionally, before the step of fusing the first operating state with the second operating state and fusing the first change trend with the second change trend, the method further includes:

[0020] Determine the amount of data from multiple sources;

[0021] The values ​​of a1, a2, b1 and b2 are adjusted according to the data amount.

[0022] Optionally, after the step of monitoring the operating state of the substation according to the final operating state and the final change trend, the method further includes:

[0023] Determining error data of the multi-physics field mechanism model based on historical and real-time final operating states and final change trends;

[0024] Obtaining optimization parameters of the multi-physics field mechanism model according to the error data;

[0025] The multi-physics field mechanism model is updated according to the optimization parameters.

[0026] Optionally, the step of monitoring the operating state of the substation according to the final operating state and the final change trend includes:

[0027] The value of the final operating state is compared with a threshold, and when the value of the final operating state is greater than the threshold, the final operating state is determined to be a fault state.

[0028] Optionally, after the step of acquiring multi-source data of the transformer, the method further comprises:

[0029] The multi-source data are cleaned and denoised.

[0030] In a second aspect, an embodiment of the present application further provides a substation operation status monitoring device, the device comprising:

[0031] A data acquisition unit, used to acquire multi-source data of the transformer; wherein the multi-source data includes online detection data of electric energy, historical detection data of electric energy, experimental data of preset directions, transformer size data, operating environment data and performance index data;

[0032] A data processing unit, used for inputting the multi-source data into a preset multi-physics field mechanism model, and obtaining a first operating state and a first change trend corresponding to the multi-source data;

[0033] A data processing unit, used to input the multi-source data into a preset artificial intelligence model, and obtain a second operating state and a second change trend corresponding to the multi-source data;

[0034] A data fusion unit, used to fuse the first operating state with the second operating state, and fuse the first change trend with the second change trend, and determine the final operating state and final change trend corresponding to the transformer;

[0035] The data processing unit is also used to monitor the operating status of the substation according to the final operating status and the final change trend.

[0036] In a third aspect, an embodiment of the present application further provides an electronic device, including:

[0037] A memory for storing one or more programs;

[0038] processor;

[0039] When the one or more programs are executed by the processor, the above-mentioned substation operation status monitoring method is implemented.

[0040] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned substation operation status monitoring method is implemented.

[0041] Compared with the prior art, this application has the following beneficial effects:

[0042] The present application provides a substation operation status monitoring method, device, electronic device and storage medium, firstly obtain multi-source data of transformer; wherein, the multi-source data includes online detection data of electric energy, historical detection data of electric energy, experimental data of preset direction, transformer size data, operation environment data and performance index data; then input the multi-source data into the preset multi-physics field mechanism model, and obtain the first operation state and the first change trend corresponding to the multi-source data; and input the multi-source data into the preset artificial intelligence model, and obtain the second operation state and the second change trend corresponding to the multi-source data; then fuse the first operation state with the second operation state, and fuse the first change trend with the second change trend, and determine the final operation state and the final change trend corresponding to the transformer; finally, monitor the operation state of the substation according to the final operation state and the final change trend. Since the multi-physics field mechanism model and the artificial intelligence model are integrated in the present application, the motion state and the change trend can be obtained by the two models, and the data obtained by the two models can be integrated, and then the integrated data can be used to monitor the operation state of the substation; therefore, the data used for the operation state of the present application integrates the advantages of the two models, which can make the monitoring results more accurate and can meet complex working conditions.

[0043] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 A schematic diagram of a module of an electronic device provided in an embodiment of the present application.

[0046] Figure 2A first exemplary flow chart of a method for monitoring substation operating status provided in an embodiment of the present application.

[0047] Figure 3 A second exemplary flowchart of the substation operation status monitoring method provided in an embodiment of the present application.

[0048] Figure 4 A third exemplary flow chart of the substation operation status monitoring method provided in an embodiment of the present application.

[0049] Figure 5 A module diagram of a substation operation status monitoring device provided in an embodiment of the present application.

[0050] In the figure: 100 - electronic device; 101 - processor; 102 - memory; 103 - communication bus; 200 - substation operation status monitoring device; 210 - data acquisition unit; 220 - data processing unit; 230 - data fusion unit. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0052] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0053] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0054] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0055] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0056] As described in the background technology, currently, the monitoring of the operating status of a substation is generally implemented using a mechanism model. However, a single model has the problem of low accuracy in monitoring the operating status and cannot be applied to complex working conditions.

[0057] In view of this, in order to solve the above-mentioned technical problems, an embodiment of the present application provides a method for monitoring the operating status of a substation, which realizes the monitoring of the operating status of the substation by fusing the results determined by the two models.

[0058] The substation operation status monitoring method provided in this application is exemplarily described below.

[0059] It should be noted that the substation operation status monitoring method provided in this application can be applied to electronic equipment. Figure 1 A schematic structural block diagram of an electronic device 100 provided in an embodiment of the present application is shown, and the electronic device 100 includes a memory 102, a processor 101, and a communication bus 103. The memory 102, the processor 101, and the communication bus 103 are electrically connected to each other directly or indirectly to achieve data transmission or interaction. For example, these elements can be electrically connected to each other through one or more communication buses or signal lines.

[0060] The memory 102 can be used to store software programs and modules, such as program instructions or modules corresponding to the substation operation status monitoring device provided in the embodiment of the present application. The processor 101 executes various functional applications and data processing by executing the software programs and modules stored in the memory 102, thereby executing the steps of the substation operation status monitoring method provided in the embodiment of the present application. The communication bus 103 can be used to communicate signaling or data with other node devices.

[0061] Among them, the memory 102 can be, but is not limited to, random access memory (Random Access Memory, RAM), read only memory (Read Only Memory, ROM), programmable read-only memory (Programmable Read-Only Memory, PROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable programmable read-only memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.

[0062] The processor 101 may be an integrated circuit chip with signal processing capability. The processor 101 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0063] Understandably, Figure 1 The structure shown is for illustration only. The electronic device 100 may also include Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown. Figure 1 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0064] The following uses the electronic device 100 as an illustrative execution subject to exemplify the substation operation status monitoring method provided in the embodiment of the present application.

[0065] See also Figure 2 The substation operation status monitoring method provided in this application includes:

[0066] S102, obtaining multi-source data of the transformer; wherein the multi-source data includes online detection data of electric energy, historical detection data of electric energy, experimental data of preset directions, transformer size data, operating environment data and performance index data;

[0067] S104, inputting the multi-source data into a preset multi-physics field mechanism model, and obtaining a first operating state and a first change trend corresponding to the multi-source data;

[0068] S106, inputting the multi-source data into a preset artificial intelligence model, and obtaining a second operating state and a second change trend corresponding to the multi-source data;

[0069] S108, integrating the first operating state with the second operating state, integrating the first change trend with the second change trend, and determining a final operating state and a final change trend corresponding to the transformer;

[0070] S110, monitoring the operation status of the substation according to the final operation status and the final change trend.

[0071] At present, the analysis of data generally includes mechanism models and non-mechanism models. Among them, the mechanism model refers to an accurate mathematical model established according to the internal mechanism of the object, production process or the transfer mechanism of material flow. It is a mathematical model of the object or process obtained based on the mass balance equation, energy balance equation, momentum balance equation, phase balance equation, and certain physical property equations, chemical reaction laws, basic circuit laws, etc. The advantage of the mechanism model is that the parameters have very clear physical meanings. This type of model exists in all walks of life and requires sufficient input conditions. The output is obtained through the model and the entire process can be simulated.

[0072] Non-mechanism models refer to artificial intelligence, represented by neural networks, and of course there are also decision trees, genetic algorithms, support vector machines and other models. This type of model has incomplete input, collects massive amounts of data through mobile Internet or other related software, organizes the data into information, and then integrates and refines the relevant information. After training and fitting based on the data, an automated decision model is formed.

[0073] Based on this, in this application, a mechanism model and an artificial intelligence model are used to process the data simultaneously, and the processed data are fused to realize the operating status monitoring of the substation, achieving higher accuracy and wider applicable scenarios.

[0074] Among them, the multi-source data of the transformer includes online electric energy detection data, historical electric energy detection data, preset direction experimental data, transformer size data, operating environment data and performance index data; the electric energy can be transformer sound, light, electricity, magnetism, etc.

[0075] After obtaining multi-source data of the transformer, the multi-source data can be processed through the multi-physical field mechanism model and the artificial intelligence model respectively to obtain the processed data.

[0076] Specifically, the multi-physics field mechanism model provided by the present application includes: a three-dimensional model that can reflect the structure and function of the transformer, and also constructs a physical field model that exists inside the transformer when it is running, such as vibration field, temperature field, electromagnetic field, etc. Then, according to several preset formulas for calculating the operating status and change trend, a transformer multi-physics field mechanism model is established. After inputting multi-source data into the preset multi-physics field mechanism model, the multi-physics field mechanism model can simulate and analyze the multi-physics field inside the transformer, characterize the behavior and rules of the transformer, and obtain the operating status and change trend of the transformer.

[0077] The artificial intelligence model provided in this application is a time series prediction data model. The main operation mode is: multi-source data is used as model input, and the model outputs the transformer status, including normal, local overheating, partial discharge, system short circuit, and core grounding fault types, to achieve transformer status analysis and fault discrimination; artificial intelligence prediction models based on time series convolutional neural networks (time series prediction networks) such as LSTNet use multi-source data as model input, and the model outputs transformer vibration, temperature, sound pattern, core grounding current and other state prediction data in the future period of time to achieve transformer status trend prediction.

[0078] It should be noted that the first operating state obtained by the multi-physical field mechanism model and the second operating state obtained by the artificial intelligence model in this application are parameters that characterize the current operating status of the transformer, and the parameter types of the two are the same; the first change trend and the second change trend are both predicted data that characterize the transformer in the future, and the parameter types of the two are also exactly the same.

[0079] After obtaining relevant data, in order to monitor the operating status of the substation more accurately, this application needs to fuse the data of the two, that is, to fuse the first operating status and the second operating status into one data, and to fuse the first change trend and the second change trend into one data. In this way, in determining the final operating status and the final change trend, the advantages of the two models are taken into account, and the disadvantages of the two models are complemented to achieve a higher precision effect; at the same time, it can better meet the requirements of complex working conditions.

[0080] It should be noted that in order to improve data accuracy, after step S102, please refer to Figure 3 , the method further comprises:

[0081] S103, performing data cleaning and denoising processing on multi-source data.

[0082] Specifically, the transformer historical and real-time detectable operation data can be preprocessed, including data cleaning, denoising and other processing (including missing value filling and abnormal point processing) of the collected transformer historical and real-time operation data to ensure high accuracy and reliability of data quality. At the same time, by extracting features from the preprocessed data, the features reflecting the transformer operating status, such as current, temperature, humidity, etc., are extracted, and the extracted features are optimized and screened using feature selection technology to improve the accuracy and generalization ability of the model.

[0083] As an implementation method, the final running state satisfies the formula:

[0084] X j =a1X 1j +b1X 2j

[0085] The final change trend satisfies the formula:

[0086] Y j =a2Y 1j +b2Y 2j

[0087] Among them, X j represents the final running state of the jth data, X 1j represents the first running state of the jth data, X 2j Indicates the second running state of the jth data; Y j Indicates the final change trend of the jth data, Y 1j Indicates the first change trend of the jth data, Y 2j represents the second change trend of the j-th data; a1, a2, b1 and b2 represent weights respectively, and a1+b1=1, a2+b2=1, a1>b1, a2<b2.

[0088] That is, in this application, the complementarity between the two models is achieved by weight distribution. Since the parameter types determined by the mechanism model and the artificial intelligence model are the same, each parameter in the final operating state and the final change trend is obtained through the two models, taking into account the advantages of the two models.

[0089] The applicant has found that the mechanism model performs well in the operation status confirmation stage, while the artificial intelligence model performs better in the operation status prediction. Therefore, in this application, when determining the final operation status, the weight of the mechanism model will be greater than the weight of the artificial intelligence model; and when determining the final change trend, the weight of the artificial intelligence model will be greater than the weight of the mechanism model.

[0090] Preferably, a1=b2 and b1=a2.

[0091] That is, when the final operating state satisfies the formula:

[0092] X j =aX 1j +bX 2j

[0093] Then the final change trend satisfies the formula:

[0094] Y j =bY 1j +aY 2j

[0095] That is, by setting the weights of the final operating state and the final change trend in opposite directions, the complementarity between the mechanism model and the artificial intelligence model can be achieved.

[0096] It should be noted that the weights of the mechanism model and the artificial intelligence model provided in this application are not constant, but can be adjusted in real time according to the actual working conditions, so that the final operating state and the final change trend determined are more accurate, thereby improving the monitoring accuracy of the substation's operating state. By using the opposite weights of the final operating state and the final change trend, when the final operating state is determined, if any weight changes, its weight also changes synchronously when the final change trend is determined, ensuring the consistency of the change, achieving complementarity between the final operating state and the final change trend, and the final operating state and the final change trend always remain corresponding, and can eventually be applied to more complex working conditions with higher accuracy.

[0097] As an implementation manner, before S108, the method further includes:

[0098] S1071, determining the data volume of multi-source data;

[0099] S1072, adjust the values ​​of a1, a2, b1 and b2 according to the data volume.

[0100] The applicant has found that, for the mechanism model and the artificial intelligence model, the mechanism model is more suitable for processing scenarios with relatively small amounts of data, while the artificial intelligence model is more suitable for scenarios with relatively large amounts of data. Therefore, when determining the weights of the mechanism model and the artificial intelligence model, the weights of the two models can be adjusted according to the amount of data. For example, as the amount of data increases, the values ​​of a1 and a2 gradually decrease, while the values ​​of b1 and b2 gradually increase.

[0101] That is, in this application, the weight is related to the type of final data and the current working condition, and the weight is more related to the type of final data. That is, when a1 decreases and b1 increases, a1 is always greater than b1 in the end.

[0102] Of course, the weight value can also be adjusted according to other working condition data, for example, the weight can be adjusted by the temperature of the current environment. In actual application, the applicant found that when the current ambient temperature changes, the accuracy of the final determination will change. The fundamental reason is that when the temperature of the sensor changes, the accuracy of its data collection changes. Therefore, the present application can also achieve temperature compensation by adjusting the weights of the two models, so that the final data after fusion is more accurate.

[0103] Specifically, when the temperature rises, the data acquisition accuracy of the sensor will decrease. When determining the final operating state, a1 is set to decrease and b1 is set to increase so as to compensate the mechanism model through the artificial intelligence model. When determining the final change trend, a2 is set to increase and b2 to be smaller so as to compensate the artificial intelligence model through the mechanism model and improve the final data accuracy.

[0104] As an implementation method, the step of S110 includes:

[0105] The value of the final operating state is compared with a threshold, and when the value of the final operating state is greater than the threshold, the final operating state is determined to be a fault state.

[0106] That is, in this application, when determining whether a substation fails, after determining the value of the final operating state, the value can be compared with the threshold. If the value of the final operating state is greater than the threshold, the final operating state is determined to be a fault state; if the value of the final operating state is less than the threshold, the final operating state is determined to be a non-fault state. At the same time, the final change trend is determined.

[0107] When in a fault state, an alarm message is generated and displayed or sent to the user end to remind the staff of a fault. When in a non-fault state, it will continue to determine whether the value of the final change trend is less than the threshold. If the final change trend shows a fault state, an alarm message will also be generated and displayed or sent to the user end. I will not go into details here.

[0108] Moreover, in this application, the artificial intelligence model is obtained by training a convolutional neural network, and the model is relatively fixed. However, for the mechanism model, its parameters can be updated in real time during operation to improve the model accuracy. Therefore, after step S110, please refer to Figure 4 , the method further comprises:

[0109] S112, determining error data of the multi-physics field mechanism model based on the historical and real-time final operating states and final change trends;

[0110] S114, obtaining optimization parameters of the multi-physics field mechanism model according to the error data;

[0111] S116, updating the multi-physics field mechanism model according to the optimization parameters.

[0112] That is, in this application, the multi-physics field mechanism model can be updated in real time according to historical data and real-time data. Specifically, the real-time winding current data of the transformer can be input into the transformer mechanism model, and the winding fault diagnosis results and winding internal state data can be obtained by analyzing the winding deformation degree and spectrum characteristics under the time series; then the fault data and normal operation data are screened out, and the measured value of the winding deformation under the fault state is obtained. The error calculation and comparison are performed with the calculated value obtained based on the mechanism model to obtain the winding deformation error data under the fault state; then the model winding deformation error data is input into the mechanism model, and the transformer data is driven, and the proportional coefficient of the radial / winding axial leakage flux and the current in the mechanism model, the damping coefficient of the insulating oil relative to the winding, and the sliding friction coefficient of the i-th winding component are optimized; finally, according to the optimized parameters, the transformer mechanism model is updated.

[0113] Based on the above implementation, please refer to Figure 5 The embodiment of the present application further provides a substation operation status monitoring device 200, the device comprising:

[0114] The data acquisition unit 210 is used to acquire multi-source data of the transformer; wherein the multi-source data includes online detection data of electric energy, historical detection data of electric energy, preset direction experimental data, transformer size data, operating environment data and performance index data.

[0115] It can be understood that the above S102 can be executed by the data acquisition unit 210 .

[0116] The data processing unit 220 is used to input the multi-source data into a preset multi-physical field mechanism model, and obtain a first operating state and a first change trend corresponding to the multi-source data.

[0117] It can be understood that the above S104 can be executed by the data processing unit 220 .

[0118] The data processing unit 220 is used to input the multi-source data into a preset artificial intelligence model and obtain the second operating state and the second change trend corresponding to the multi-source data.

[0119] It can be understood that the above S106 can be executed by the data processing unit 220 .

[0120] The data fusion unit 230 is used to fuse the first operating state with the second operating state, and fuse the first change trend with the second change trend, and determine the final operating state and the final change trend corresponding to the transformer.

[0121] It can be understood that the above S108 can be executed by the data fusion unit 230 .

[0122] The data processing unit 220 is also used to monitor the operation status of the substation according to the final operation status and the final change trend.

[0123] It can be understood that the above S110 can be executed by the data processing unit 220 .

[0124] In summary, the present application provides a substation operation status monitoring method, device, electronic device and storage medium, firstly, obtaining multi-source data of the transformer; wherein the multi-source data includes online electric energy detection data, historical electric energy detection data, preset direction experimental data, transformer size data, operating environment data and performance index data; then the multi-source data is input into a preset multi-physical field mechanism model, and a first operating state and a first change trend corresponding to the multi-source data are obtained; and the multi-source data is input into a preset artificial intelligence model, and a second operating state and a second change trend corresponding to the multi-source data are obtained; then the first operating state is merged with the second operating state, and the first change trend is merged with the second change trend, and the final operating state and the final change trend corresponding to the transformer are determined; finally, the operation state of the substation is monitored according to the final operating state and the final change trend. Since the multi-physics field mechanism model and the artificial intelligence model are integrated in the present application, the motion state and change trend can be obtained through the two models, and then the data obtained by the two models are integrated, and then the integrated data is used to monitor the operation status of the substation; therefore, the data used for the operation status in the present application integrates the advantages of the two models, which can make the monitoring results more accurate and can meet complex working conditions.

[0125] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods may also be implemented in other ways. The device embodiments described above are merely schematic, for example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to the embodiments of the present application. In this regard, each box in the flowchart or block diagram may represent a module, a program segment or a part of a code, and the module, program segment or a part of a code includes one or more executable instructions for implementing a specified logical function.

[0126] It should also be noted that in some alternative implementations, the functions marked in the blocks may also occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the opposite order, depending on the functions involved.

[0127] It should also be noted that each box in the block diagram and / or flowchart, and combinations of boxes in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0128] In addition, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0129] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product in essence or in other words, the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk. Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, so the scope of protection of the present invention should be based on the scope defined by the claims.

Claims

1. A method for monitoring the operation status of a substation, characterized in that: The method comprises: Obtain multi-source data of the transformer; wherein the multi-source data includes online detection data of electric energy, historical detection data of electric energy, experimental data of preset directions, transformer size data, operating environment data and performance index data; Inputting the multi-source data into a preset multi-physics field mechanism model, and obtaining a first operating state and a first change trend corresponding to the multi-source data; Inputting the multi-source data into a preset artificial intelligence model, and obtaining a second operating state and a second change trend corresponding to the multi-source data; Merging the first operating state with the second operating state, and merging the first change trend with the second change trend, and determining a final operating state and a final change trend corresponding to the transformer; The operating state of the substation is monitored according to the final operating state and the final change trend.

2. The substation operation status monitoring method according to claim 1, characterized in that: The final operating state satisfies the formula: X j =a1X 1j +b1X 2j The final change trend satisfies the formula: <h2 style=";text-align:left;direction:ltr">Y<h2 style=";text-align:left;direction:ltr"> j <h2 style=";text-align:left;direction:ltr"> =a2Y<h2 style=";text-align:left;direction:ltr"> 1j <h2 style=";text-align:left;direction:ltr"> +b2Y<h2 style=";text-align:left;direction:ltr"> 2j Among them, X j represents the final running state of the jth data, X 1j represents the first running state of the jth data, X 2j Indicates the second running state of the jth data; Y j Indicates the final change trend of the jth data, Y 1j Indicates the first change trend of the jth data, Y 2j represents the second change trend of the j-th data; a1, a2, b1 and b2 represent weights respectively, and a1+b1=1, a2+b2=1, a1>b1, a2<b2.

3. The substation operation status monitoring method according to claim 2, characterized in that: a1=b2 and b1=a2.

4. The substation operation status monitoring method according to claim 2, characterized in that: Before the step of fusing the first operating state with the second operating state and fusing the first change trend with the second change trend, the method further includes: Determine the amount of data from multiple sources; The values ​​of a1, a2, b1 and b2 are adjusted according to the data amount.

5. The substation operation status monitoring method according to claim 1, characterized in that: After the step of monitoring the operating state of the substation according to the final operating state and the final change trend, the method further includes: Determining error data of the multi-physics field mechanism model based on historical and real-time final operating states and final change trends; Obtaining optimization parameters of the multi-physics field mechanism model according to the error data; The multi-physics field mechanism model is updated according to the optimization parameters.

6. The substation operation status monitoring method according to claim 1, characterized in that: The step of monitoring the operating state of the substation according to the final operating state and the final change trend includes: The value of the final operating state is compared with a threshold, and when the value of the final operating state is greater than the threshold, the final operating state is determined to be a fault state.

7. The substation operation status monitoring method according to claim 1, characterized in that: After the step of acquiring multi-source data of the transformer, the method further comprises: The multi-source data are cleaned and denoised.

8. A substation operation status monitoring device, characterized in that: The device comprises: A data acquisition unit, used to acquire multi-source data of the transformer; wherein the multi-source data includes online detection data of electric energy, historical detection data of electric energy, experimental data of preset directions, transformer size data, operating environment data and performance index data; A data processing unit, used for inputting the multi-source data into a preset multi-physics field mechanism model, and obtaining a first operating state and a first change trend corresponding to the multi-source data; A data processing unit, used to input the multi-source data into a preset artificial intelligence model, and obtain a second operating state and a second change trend corresponding to the multi-source data; A data fusion unit, used to fuse the first operating state with the second operating state, and fuse the first change trend with the second change trend, and determine the final operating state and final change trend corresponding to the transformer; The data processing unit is also used to monitor the operating status of the substation according to the final operating status and the final change trend.

9. An electronic device, characterized in that: include: A memory for storing one or more programs; processor; When the one or more programs are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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