Power fault diagnosis method, device and equipment and storage medium
By building a multi-level power fault diagnosis system, using deep learning models and time series prediction algorithms, high-precision, real-time and intelligent diagnosis of power faults is achieved, solving the problems of inefficiency and low accuracy of traditional methods, reducing the problem of low efficiency and low accuracy of traditional methods. Failure losses and repair costs.
Patent Information
- Application Number
- CN202411594193.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-08
- Publication Date
- 2025-02-14
AI Technical Summary
Traditional power fault monitoring and diagnosis methods are inefficient and have low accuracy, and cannot detect potential faults in a timely manner, making it difficult to meet the high-precision, real-time and intelligent needs of complex power systems.
Build a power fault diagnosis system including data acquisition layer, data transmission layer, data storage and processing layer and application layer. It collects various types of operating parameters through parameter sensors, transmits and stores them in real time, and uses deep learning-based fault diagnosis models or time The sequence prediction algorithm generates power fault diagnosis information and outputs fault location and early warning information through the application layer.
Accurate, real-time and intelligent diagnosis of power failures is achieved, fault loss and maintenance costs are reduced, and the safety and stability of the power system is improved.
Smart Images
Figure CN119438766A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of power system fault processing, and in particular to a power fault diagnosis method, device, equipment and storage medium. Background Art
[0002] As the scale and complexity of power systems continue to expand, power equipment failures pose a serious threat to the safe and stable operation of power grids. Traditional power fault monitoring and diagnosis methods mainly rely on manual inspections and regular maintenance, which have problems such as low efficiency, low accuracy, and failure to detect potential faults in a timely manner. In recent years, although some sensor-based and data analysis technologies have been applied to power fault monitoring, they still cannot meet the needs of high-precision, real-time, and intelligent fault warning and diagnosis for complex power systems.
[0003] Therefore, there is an urgent need for a power fault diagnosis method that can meet the high-precision, real-time and intelligent requirements of the power system. Summary of the invention
[0004] The embodiments of the present application provide a power fault diagnosis method, device, equipment and storage medium, which can meet the requirements of high-precision, real-time and intelligent power fault diagnosis of the power system.
[0005] In a first aspect, an embodiment of the present application provides a method for diagnosing electric power faults, which is applied to an electric power fault diagnosis system, wherein the electric power fault diagnosis system includes a data acquisition layer, a data transmission layer, a data storage and processing layer, and an application layer; the method includes: receiving, through the data acquisition layer, various types of operating parameters collected by various types of parameter sensors deployed in the electric power system; transmitting, through the data transmission layer, the various types of operating parameters in real time to the data storage and processing layer; storing, through the data storage and processing layer, the various types of operating parameters, and generating electric power fault diagnosis information based on the stored various types of operating parameters; and outputting the electric power fault diagnosis information through the application layer.
[0006] In an optional embodiment, generating power fault diagnosis information based on stored multiple types of operating parameters includes: inputting the multiple types of operating parameters into a deep learning-based fault diagnosis model to output warning-type power fault diagnosis information.
[0007] In an optional implementation, the training process of the deep learning-based fault diagnosis model is as follows:
[0008] inputting a preset number of operating parameter samples into the initial fault diagnosis model in sequence;
[0009] Iteratively training the initial fault diagnosis model through a target loss function to obtain a trained fault diagnosis model;
[0010] Determining the trained fault diagnosis model as the deep learning-based fault diagnosis model;
[0011] Among them, the formula of the objective loss function is:
[0012]
[0013] Among them, M is the number of running parameter samples; N is the number of elements in the network output layer, x and x' are the predicted value and the value corresponding to the labeled result of the model output respectively, LOSS is the mean square error, that is, the result of the loss function, and i and j are positive integers.
[0014] In an optional embodiment, the power fault diagnosis information is generated based on the stored multiple types of operating parameters, including: inputting the multiple types of operating parameters into a time series-based prediction algorithm to output a predicted value of the power operation; when the deviation between the predicted value of the power operation and the current actual value of the power operation exceeds a preset limit, outputting power fault diagnosis information of the warning type.
[0015] In an optional implementation, the outputting the power fault diagnosis information through the application layer includes: outputting the fault location of the power fault diagnosis information through the application layer based on the geographic information system GIS and the topological structure information of the power system.
[0016] In an optional embodiment, after outputting the fault location of the power fault diagnosis information through the application layer based on the geographic information system GIS and the topological structure information of the power system, it also includes: isolating the power system at the fault location according to the power fault diagnosis information.
[0017] In a second aspect, an embodiment of the present application provides an electric power fault diagnosis device, comprising: an acquisition module, used to receive, through a data acquisition layer, various types of operating parameters collected by various types of parameter sensors deployed in the electric power system; a transmission module, used to transmit the various types of operating parameters in real time to a data storage and processing layer through a data transmission layer; a storage and processing module, used to store the various types of operating parameters through the data storage and processing layer, and generate electric power fault diagnosis information based on the stored various types of operating parameters; an application module, used to output the electric power fault diagnosis information through an application layer.
[0018] In a third aspect, an embodiment of the present application provides a computer device, including: a memory, a processor;
[0019] The memory stores computer-executable instructions;
[0020] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various optional implementations of the first aspect.
[0021] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the above first aspect and / or various optional implementations of the first aspect.
[0022] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various optional implementations of the first aspect.
[0023] The power fault diagnosis method, device, equipment and storage medium provided in the embodiments of the present application, the embodiments of the present application, first, construct a diagnosis system including a data acquisition layer, a data transmission layer, a data storage and processing layer and an application layer; then, collect various types of operating parameters through the data acquisition layer; transmit the operating parameters in real time through the data transmission layer; store various types of operating parameters through the data storage and processing layer, and generate power fault diagnosis information based on the stored various types of operating parameters; output the power fault diagnosis information through the application layer, which can realize accurate, real-time and intelligent power fault diagnosis capabilities, and reduce fault losses and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS
[0024] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.
[0025] Figure 1 A schematic diagram of the structure of a power fault diagnosis system provided in an embodiment of the present application;
[0026] Figure 2 A schematic diagram of a flow chart of a power fault diagnosis method provided in an embodiment of the present application;
[0027] Figure 3 A schematic diagram of the structure of a power fault diagnosis device provided in an embodiment of the present application;
[0028] Figure 4 A schematic diagram of the structure of a computer device provided in an embodiment of the present application.
[0029] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION
[0030] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.
[0031] In order to design a method for diagnosing power faults that meets the requirements of high precision, real-time performance and intelligence of the power system, the present application provides a method, device, equipment and storage medium for diagnosing power faults. The technical concept is as follows: construct a power fault diagnosis system, which includes a data acquisition layer, a data transmission layer, a data storage and processing layer and an application layer; use the data acquisition layer to collect various types of operating parameters; use the data transmission layer to transmit the operating parameters in real time; use the data storage and processing layer to store various types of operating parameters, and generate power fault diagnosis information based on the stored various types of operating parameters; use the application layer to output the power fault diagnosis information, so as to realize accurate, real-time and intelligent power fault diagnosis analysis and processing.
[0032] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.
[0033] Figure 1 A schematic diagram of the structure of the power fault diagnosis system provided in the embodiment of the present application is shown in FIG. Figure 1 As shown, the specific power fault diagnosis system of the present application includes: a data acquisition layer 101, a data transmission layer 102, a data storage and processing layer 103 and an application layer 104.
[0034] Among them, the data collection layer 101 is used to collect data; the data transmission layer 102 is used to transmit data; the data storage and processing layer 103 is used to store and analyze data; and the application layer 104 is used to display the results of data analysis and processing.
[0035] The data collection layer 101, the data transmission layer 102, the data storage and processing layer 103 and the application layer 104 of the power fault diagnosis system can be deployed on the terminal device or on the server.
[0036] Figure 2 A flow chart of a power fault diagnosis method provided in an embodiment of the present application is shown in FIG. Figure 2As shown, the method includes:
[0037] S201: Receive various types of operating parameters collected by various types of parameter sensors deployed in the power system through the data collection layer.
[0038] In the embodiments of the present application, the parameter sensors include temperature acquisition sensors, humidity acquisition sensors, voltage acquisition sensors, vibration sensing sensors, and the like.
[0039] In the embodiments of the present application, the multiple types of operating parameters include: one or more of voltage parameters, current parameters, temperature parameters and vibration parameters.
[0040] S202: Transmit various types of operating parameters to the data storage and processing layer in real time through the data transmission layer.
[0041] In an embodiment of the present application, the data transmission layer transmits various types of operating parameters to the data storage and processing layer in real time through a mobile communication network.
[0042] Among them, the mobile communication network can be a 5G network or other communication networks.
[0043] Among them, high-speed communication technologies such as fiber-optic communication and 5G can be used to build a reliable data transmission network to ensure real-time transmission of data.
[0044] S203: storing various types of operating parameters through the data storage and processing layer, and generating power fault diagnosis information according to the stored various types of operating parameters.
[0045] In one embodiment of the present application, multiple types of operating parameters are input into a deep learning-based fault diagnosis model to output early warning type power fault diagnosis information.
[0046] Among them, the training process of the fault diagnosis model based on deep learning is:
[0047] inputting a preset number of operating parameter samples into the initial fault diagnosis model in sequence;
[0048] Iteratively training the initial fault diagnosis model through a target loss function to obtain a trained fault diagnosis model;
[0049] Determine the trained fault diagnosis model as the fault diagnosis model based on deep learning;
[0050] Among them, the formula of the objective loss function is:
[0051]
[0052] Among them, M is the number of running parameter samples; N is the number of elements in the network output layer, x and x' are the predicted value and the value corresponding to the labeled result of the model output respectively, LOSS is the mean square error, that is, the result of the loss function, and i and j are positive integers.
[0053] Specifically, when the deep learning-based fault diagnosis model outputs a prediction of an impending power failure based on multiple types of input operating parameters, early warning-type power fault diagnosis information is output through a display screen or voice playback.
[0054] Specifically, when the deep learning-based fault diagnosis model outputs a prediction of no power failure based on the input of multiple types of operating parameters, no information is output.
[0055] In another embodiment of the present application, multiple types of operating parameters are input into a time series-based prediction algorithm to output a predicted value of power operation; when the deviation between the predicted value of power operation and the current actual value of power operation exceeds a preset limit, early warning type power fault diagnosis information is output.
[0056] Among them, the prediction algorithms based on time series can be naive forecasting, simple average, moving average, weighted moving average and Holt linear trend method.
[0057] Optionally, the formula for the time series based prediction algorithm is as follows:
[0058]
[0059] In the formula, α is the smoothing parameter, and its value is 0≤α≤1; T is the current cycle, and y is the operating parameter; The operating parameters for the next week of the current cycle are predicted.
[0060] Among them, the early warning type of power fault diagnosis information is used to remind operation and maintenance personnel to maintain the power system to avoid the occurrence of faults.
[0061] S204: Outputting power fault diagnosis information through the application layer.
[0062] In an embodiment of the present application, the application layer displays the power fault diagnosis information through a display screen, and / or plays the power fault diagnosis information through voice playback to prompt the operation and maintenance personnel to maintain the power system.
[0063] In summary, the embodiments of the present application first construct a diagnostic system including a data acquisition layer, a data transmission layer, a data storage and processing layer, and an application layer; then, various types of operating parameters are collected through the data acquisition layer; the operating parameters are transmitted in real time through the data transmission layer; various types of operating parameters are stored through the data storage and processing layer, and power fault diagnosis information is generated according to the stored various types of operating parameters; and the power fault diagnosis information is output through the application layer, which can realize accurate, real-time and intelligent power fault diagnosis capabilities and reduce fault losses and maintenance costs.
[0064] Based on the above embodiment, in this embodiment, the process of outputting the power fault diagnosis information through the application layer is described in detail as follows:
[0065] S2041: Output the fault location of the power fault diagnosis information through the application layer based on the geographic information system GIS and the topological structure information of the power system.
[0066] Specifically, all electrical equipment in the topological structure of the power system is displayed on a display screen in the Geographic Information System (GIS); and the electrical equipment corresponding to the fault location of the power fault diagnosis information is further displayed as an alarm on the display screen.
[0067] Among them, the warning display includes a red marking display or a flashing display.
[0068] S2042: Isolate the power system at the fault location according to the power fault diagnosis information.
[0069] Specifically, the power fault diagnosis information is used to shut down the electrical equipment at the fault location and start the backup equipment.
[0070] In summary, by combining the geographic information system (GIS) and the power topology structure, the fault location can be quickly and accurately located, the maintenance efficiency can be improved, the workload of the operation and maintenance personnel can be reduced, and the work efficiency can be improved. In addition, by isolating the electrical equipment of the power system at the fault location, the fault can be avoided from expanding and the stability of the electrical system can be improved.
[0071] Figure 3 A schematic diagram of the structure of the power fault diagnosis device provided in the embodiment of the present application is shown in FIG. Figure 3 As shown, the power fault diagnosis device provided in this embodiment includes: a collection module 301 , a transmission module 302 , a storage and processing module 303 and an application module 304 .
[0072] The acquisition module 301 is used to receive various types of operating parameters acquired by various types of parameter sensors deployed in the power system through the data acquisition layer;
[0073] A transmission module 302, used to transmit the various types of operating parameters to the data storage and processing layer in real time through the data transmission layer;
[0074] A storage and processing module 303, used to store the multiple types of operating parameters through a data storage and processing layer, and generate power fault diagnosis information according to the stored multiple types of operating parameters;
[0075] The application module 304 is used to output the power fault diagnosis information through the application layer.
[0076] In one or more examples of the present application, the storage and processing module 303 is specifically used to:
[0077] The multiple types of operating parameters are input into a fault diagnosis model based on deep learning to output early warning type power fault diagnosis information.
[0078] In one or more examples of the present application, the training process of the fault diagnosis model based on deep learning is:
[0079] sequentially inputting a preset number of operating parameter samples into an initial fault diagnosis model;
[0080] Iteratively training the initial fault diagnosis model through a target loss function to obtain a trained fault diagnosis model;
[0081] Determining the trained fault diagnosis model as the deep learning-based fault diagnosis model;
[0082] Among them, the formula of the objective loss function is:
[0083]
[0084] Among them, M is the number of running parameter samples; N is the number of elements in the network output layer, x and x' are the predicted value and the value corresponding to the labeled result of the model output respectively, LOSS is the mean square error, that is, the result of the loss function, and i and j are positive integers.
[0085] In one or more instances of the present application, the storage and processing module 303 is specifically used to: input the multiple types of operating parameters into a time series-based prediction algorithm to output a predicted value of power operation; when the deviation between the predicted value of the power operation and the current actual value of the power operation exceeds a preset limit, output warning type power fault diagnosis information.
[0086] In one or more instances of the present application, the application module 304 is specifically used to: output the fault location of the power fault diagnosis information based on the geographic information system GIS and the topological structure information of the power system through the application layer; and isolate the power system at the fault location according to the power fault diagnosis information.
[0087] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. Figure 4 As shown, the computer device provided in this embodiment includes: at least one processor 401 and a memory 402. Optionally, the computer device 40 further includes a communication component 403. The processor 401, the memory 402 and the communication component 403 are connected via a bus 404.
[0088] In a specific implementation process, at least one processor 401 executes the computer-executable instructions stored in the memory 402, so that at least one processor 401 executes the above method.
[0089] The specific implementation process of the processor 401 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.
[0090] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.
[0091] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.
[0092] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.
[0093] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above-mentioned power fault diagnosis method is implemented.
[0094] The present application also provides a computer program product, including a computer program, which implements the above-mentioned power fault diagnosis method when executed by a processor.
[0095] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.
[0096] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.
[0097] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.
[0098] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0099] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
[0100] If the function is implemented in the form of a software functional unit 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 invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.
[0101] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.
[0102] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.
Claims
1. A method for diagnosing power faults, characterized in that: The method is applied to an electric power fault diagnosis system, which includes a data acquisition layer, a data transmission layer, a data storage and processing layer, and an application layer; The method comprises: Through the data collection layer, various types of operating parameters collected by various types of parameter sensors deployed in the power system are received; The various types of operating parameters are transmitted in real time to the data storage and processing layer through the data transmission layer; The plurality of types of operating parameters are stored through a data storage and processing layer, and power fault diagnosis information is generated according to the stored plurality of types of operating parameters; The power fault diagnosis information is output through the application layer.
2. The method according to claim 1, characterized in that: The generating of power fault diagnosis information according to the stored multiple types of operating parameters includes: The multiple types of operating parameters are input into a fault diagnosis model based on deep learning to output early warning type power fault diagnosis information.
3. The method according to claim 2, characterized in that The training process of the fault diagnosis model based on deep learning is as follows: inputting a preset number of operating parameter samples into the initial fault diagnosis model in sequence; Iteratively training the initial fault diagnosis model through a target loss function to obtain a trained fault diagnosis model; Determining the trained fault diagnosis model as the deep learning-based fault diagnosis model; Among them, the formula of the objective loss function is: Among them, M is the number of running parameter samples; N is the number of elements in the network output layer, x and x' are the predicted value and the value corresponding to the labeled result of the model output respectively, LOSS is the mean square error, that is, the result of the loss function, and i and j are positive integers.
4. The method according to claim 1, characterized in that The generating of power fault diagnosis information according to the stored multiple types of operating parameters includes: Inputting the plurality of types of operating parameters into a time series-based prediction algorithm to output a predicted value of power operation; When the deviation between the predicted value of the power operation and the actual value of the current power operation exceeds a preset limit, power fault diagnosis information of the early warning type is output.
5. The method according to claim 1, characterized in that: Outputting the power fault diagnosis information through the application layer includes: Through the application layer, based on the geographic information system GIS and the topological structure information of the power system, the fault location of the power fault diagnosis information is output.
6. The method according to claim 5, characterized in that After the fault location of the power fault diagnosis information is output through the application layer based on the geographic information system GIS and the topological structure information of the power system, the method further includes: The power system at the fault location is isolated according to the power fault diagnosis information.
7. A power fault diagnosis device, characterized in that: A collection module, used to receive various types of operating parameters collected by various types of parameter sensors deployed in the power system through a data collection layer; A transmission module, used for transmitting the various types of operating parameters to the data storage and processing layer in real time through the data transmission layer; A storage and processing module, used to store the multiple types of operating parameters through a data storage and processing layer, and generate power fault diagnosis information according to the stored multiple types of operating parameters; The application module is used to output the power fault diagnosis information through the application layer.
8. A computer device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.
10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.