Operation and maintenance planning method and device of power equipment, equipment, storage medium and product

Abstract

The invention discloses an operation and maintenance planning method and device for power equipment, equipment, a storage medium and a product. The method comprises the following steps: acquiring operation parameters of power equipment in real time; according to the operation parameters, using a trained fault prediction model to identify a fault state of the power equipment and generating an early warning signal; collecting operation data before and after a fault according to the early warning signal, and determining a fault diagnosis result according to the operation data; and generating an operation and maintenance strategy and an operation and maintenance resource allocation scheme according to the fault diagnosis result and the current operation and maintenance resource state, and scheduling operation and maintenance resources to process faults according to the operation and maintenance strategy and the operation and maintenance resource allocation scheme. According to the technical scheme provided by the invention, the operation and maintenance efficiency and the operation and maintenance quality are improved through an automatic fault early warning mechanism and a fault response mechanism.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of power operation and maintenance technology, and in particular, to an operation and maintenance planning method, device, equipment, storage medium and product for power equipment. Background Art

[0002] As the hub of new energy power stations, the centralized control center faces problems such as multiple points and wide coverage, and difficult safety control. With the rapid development of new energy power stations, the scale of the power system continues to expand, and the traditional power equipment operation and maintenance management model can no longer meet the needs of efficient and accurate management.

[0003] Existing operation and maintenance methods mostly rely on manual experience. Fault diagnosis is usually performed based on manual experience when a fault occurs. The lack of intelligent and automated operation and maintenance planning leads to low operation and maintenance efficiency and insufficient risk control. In addition, human factors may lead to inaccurate or delayed fault diagnosis. Summary of the invention

[0004] The embodiments of the present invention provide a method, device, equipment, storage medium and product for operation and maintenance planning of electric power equipment, so as to improve the efficiency and quality of operation and maintenance through an automated fault warning mechanism and a fault response mechanism.

[0005] In a first aspect, an embodiment of the present invention provides an operation and maintenance planning method for electric power equipment, the method comprising:

[0006] Obtain the operating parameters of power equipment in real time;

[0007] According to the operating parameters, using a trained fault prediction model to identify the fault state of the power equipment and generate a warning signal;

[0008] Collecting operation data before and after the fault occurs according to the early warning signal, and determining the fault diagnosis result according to the operation data;

[0009] An operation and maintenance strategy and an operation and maintenance resource allocation plan are generated according to the fault diagnosis result and the current operation and maintenance resource status, and operation and maintenance resources are scheduled to handle the fault according to the operation and maintenance strategy and the operation and maintenance resource allocation plan.

[0010] Optionally, determining a fault diagnosis result according to the operating data includes:

[0011] extracting target features according to the operating data;

[0012] The target feature is compared with preset features of various fault types to determine the fault type diagnosis result.

[0013] Optionally, determining the fault diagnosis result according to the operating data further includes:

[0014] A fault location diagnosis result and a fault cause diagnosis result are determined according to the fault type diagnosis result, the operating data and the topological structure of the power equipment.

[0015] Optionally, generating an operation and maintenance strategy according to the fault diagnosis result and the current operation and maintenance resource status includes:

[0016] Determine the impact of the fault on the operation of the power system according to the fault diagnosis result;

[0017] Generate an operation and maintenance plan based on the impact content;

[0018] Conduct risk assessment on the operation and maintenance plan, and optimize the operation and maintenance plan based on identified risk points;

[0019] The operation and maintenance strategy is generated according to the optimized operation and maintenance plan.

[0020] Optionally, after scheduling the operation and maintenance resources to handle the fault according to the operation and maintenance strategy and the operation and maintenance resource allocation scheme, the method further includes:

[0021] Real-time monitoring of operation and maintenance resource status and fault handling progress;

[0022] The operation and maintenance resource allocation scheme is dynamically adjusted in real time according to the current operation and maintenance resource status and fault handling progress, and scheduling is performed based on the adjusted operation and maintenance resource allocation scheme.

[0023] Optionally, after acquiring the operating parameters of the power equipment in real time, the method further includes:

[0024] Storing the operating parameters;

[0025] Anomaly detection is performed based on the stored operating parameters to identify target parameters that deviate from a normal operating range.

[0026] In a second aspect, an embodiment of the present invention further provides an operation and maintenance planning device for electric power equipment, the device comprising:

[0027] An operating parameter acquisition module is used to obtain the operating parameters of the power equipment in real time;

[0028] A warning signal generating module, used to identify the fault state of the power equipment and generate a warning signal using a trained fault prediction model according to the operating parameters;

[0029] A fault diagnosis result determination module collects operation data before and after the fault occurs according to the warning signal, and determines the fault diagnosis result according to the operation data;

[0030] The operation and maintenance scheduling processing module is used to generate an operation and maintenance strategy and an operation and maintenance resource allocation plan according to the fault diagnosis result and the current operation and maintenance resource status, and schedule operation and maintenance resources to handle the fault according to the operation and maintenance strategy and the operation and maintenance resource allocation plan.

[0031] In a third aspect, an embodiment of the present invention further provides a computer device, the computer device comprising:

[0032] one or more processors;

[0033] A memory for storing one or more programs;

[0034] When the one or more programs are executed by the one or more processors, the one or more processors implement the operation and maintenance planning method of the power equipment provided by any embodiment of the present invention.

[0035] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the operation and maintenance planning method for power equipment provided by any embodiment of the present invention.

[0036] In a fifth aspect, an embodiment of the present invention further provides a computer program product, which includes a computer program, and when the program is executed by a processor, the operation and maintenance planning method of the power equipment provided by any embodiment of the present invention is implemented.

[0037] The embodiment of the present invention provides an operation and maintenance planning method for electric power equipment, firstly, the operation parameters of the electric power equipment are obtained in real time, then the fault state of the electric power equipment is identified by using a trained fault prediction model according to the real-time operation parameters and a corresponding warning signal is generated, then the operation data before and after the fault occurs are collected according to the warning signal, and the fault diagnosis result is determined according to the collected operation data, and finally the operation and maintenance strategy and the operation and maintenance resource allocation scheme are generated according to the obtained fault diagnosis result and the current operation and maintenance resource state, and the operation and maintenance resources are dispatched to handle the fault according to the operation and maintenance strategy and the operation and maintenance resource allocation scheme. The operation and maintenance planning method for electric power equipment provided by the embodiment of the present invention improves the operation and maintenance efficiency and the operation and maintenance quality through an automated fault warning mechanism and a fault response mechanism. Thus, the substation operation and maintenance mode is improved, the problem of frequent flow of operation and maintenance personnel is solved, the equipment inspection is refined, the state is grasped in real time, and the digital team construction is improved, and it is convenient to realize the centralized monitoring, control and fault warning of the wind, light and storage multi-energy complementary station with large-scale centralized access to the Internet, so as to meet the unmanned and less-staffed operation mode, and at the same time, it is convenient to study the technical solution design and application effect evaluation of the access of new energy power generation to the power centralized control center, so as to realize the effective management and control of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1A flowchart of the operation and maintenance planning method for power equipment provided in the first embodiment of the present invention;

[0039] Figure 2 A schematic diagram of the structure of an operation and maintenance planning device for electric power equipment provided in Embodiment 2 of the present invention;

[0040] Figure 3 This is a schematic diagram of the structure of a computer device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0041] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0042] It should be mentioned before discussing the exemplary embodiments in more detail that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the steps as sequential processes, many of the steps therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of the steps can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0043] Embodiment 1

[0044] Figure 1 This is a flowchart of the operation and maintenance planning method for power equipment provided in the first embodiment of the present invention. This embodiment can be applied to the situation of fault warning and response processing of power equipment, and is particularly applicable to the operation and maintenance management of large-scale new energy power stations. The method can be executed by the operation and maintenance planning device for power equipment provided in the embodiment of the present invention. The device can be implemented by hardware and / or software, and can generally be integrated into computer equipment, and can be specifically applied in a centralized control center. Figure 1 As shown, the specific steps include:

[0045] S11. Obtaining operating parameters of power equipment in real time.

[0046] Specifically, the existing sensors and data acquisition systems deployed on various power equipment can be used to collect the operating parameters of power equipment in real time, such as the voltage, current, power, temperature, etc. of key equipment such as transformers and circuit breakers. At the same time, a stable and reliable communication network can be established to transmit the collected operating parameters to the centralized control center in real time, which can be transmitted using wireless communication technology. A Web-based real-time monitoring interface can be developed in the centralized control center, so that operation and maintenance personnel can access it through a browser and view the operating parameters, equipment status, etc. of power equipment in real time, which adds user-friendly features.

[0047] Optionally, after acquiring the operating parameters of the power equipment in real time, the method further includes: storing the operating parameters; and performing anomaly detection based on the stored operating parameters to identify target parameters that deviate from the normal operating range.

[0048] Specifically, after receiving the operating parameters transmitted by the substation, the centralized control center can perform preliminary processing on the operating parameters and store them using the database management system, and ensure the security and integrity of the data for subsequent analysis and monitoring. Then, based on the stored operating parameters, the data can be analyzed through an integrated anomaly detection algorithm to quickly identify target parameters that deviate from the normal operating range, and further prompt the target parameters through the real-time monitoring interface.

[0049] Among them, the anomaly detection algorithm can adopt a statistical method, a distance method, a clustering method, or a nearest neighbor method, etc. The statistical method uses a standard score (Z-score) to determine whether a data point is an outlier. The formula is Where X represents the data point, μ represents the mean value, and σ represents the standard deviation. Distance-based methods, such as calculating the average distance from a data point to all other points, consider the data point to be abnormal if the distance exceeds a preset threshold. The formula is:

[0050]

[0051] Among them, d(X i ,X j ) represents the data point X i With X j The Euclidean distance between them. The nearest neighbor-based method calculates the average distance between each point and its k nearest neighbors. If the distance is much smaller than other points, the data point may be abnormal. The formula is:

[0052]

[0053] Among them, N k (X i ) represents the data point X iThe k nearest neighbors of the above methods can also be combined, and a voting method or a preset weight can be used to determine the final anomaly score or label of each data point to determine whether it is an outlier, that is, the target parameter. The formula of the weighted scheme is:

[0054] Anomaly Score=ω1·score1+ω2·score2+…+ω n score n

[0055] Among them, ω i Represents the weight of the i-th anomaly detection algorithm, score i represents the anomaly score assigned to the data point by the i-th anomaly detection algorithm.

[0056] Real-time data collection and monitoring of power equipment through the centralized control center improves the monitoring efficiency of power equipment, reduces the omissions of manual monitoring, and realizes comprehensive monitoring of the status of power equipment. Further, through the automated abnormal detection mechanism, potential problems can be discovered and handled in a timely manner, which improves the fault response speed, reduces maintenance costs, and enhances the stability and reliability of the power system.

[0057] S12. According to the operating parameters, use a trained fault prediction model to identify the fault state of the power equipment and generate a warning signal.

[0058] Specifically, the centralized control center can integrate machine learning algorithms to conduct in-depth analysis of the operating parameters of power equipment and predict potential equipment failures, which can be achieved through the equipment monitoring system. Machine learning algorithms can be used to train historical fault data to establish a fault prediction model, where machine learning algorithms can include but are not limited to decision trees, random forests, support vector machines, neural networks, etc. Taking the Random Forest (RF) algorithm as an example, random forest is an integrated learning method that constructs multiple decision trees for classification or regression prediction, and improves the overall prediction accuracy and robustness by combining the prediction results of multiple models.

[0059] After obtaining the operating parameters of the power equipment, the big data analysis tools can be used to perform preprocessing processes such as cleaning, standardization and normalization on the operating parameters to improve the accuracy of data analysis. The preprocessed operating parameters are then input into the trained fault prediction model in real time, so that the fault prediction model can analyze the data to predict the equipment status, which can be a normal working state or a fault state, that is, it can identify situations where the power equipment may have a fault. When a fault state is predicted, the early warning mechanism can be automatically triggered to generate an early warning signal, and the early warning signal can be notified to the operation and maintenance personnel through sound and light alarms, emails, text messages, application push, etc.

[0060] Through the automated fault warning mechanism, possible faults are predicted and early warnings are given, which enables real-time monitoring and predictive maintenance of power equipment, reduces unexpected downtime, improves fault response speed, reduces maintenance costs, and also improves the accuracy of fault analysis, enhancing the stability and reliability of the power system.

[0061] S13. Collecting operating data before and after the fault occurs according to the early warning signal, and determining a fault diagnosis result according to the operating data.

[0062] Specifically, after generating the warning signal, the centralized control center can receive the warning signal from the equipment monitoring system in real time through the fault diagnosis system, and generate the fault diagnosis result based on the warning signal. Specifically, after obtaining the warning signal, the operation data within a period of time before and after the fault time point corresponding to the warning signal can be collected, which may include but not be limited to voltage, current, frequency, temperature, etc., and the collected operation data can be firstly cleaned, standardized and normalized to adapt to the subsequent comparison and analysis. Then, the collected operation data can be quickly analyzed by using real-time data processing technology to identify the fault characteristics therein, so as to determine the fault diagnosis results based on the identified fault characteristics, so as to provide a basis for subsequent operation and maintenance scheduling.

[0063] Optionally, determining a fault diagnosis result according to the operating data includes: extracting a target feature according to the operating data; and comparing the target feature with preset features of various fault types to determine a fault type diagnosis result.

[0064] Specifically, a fault feature database can be built into the fault diagnosis system to store preset features of various fault types, each of which has its own specific parameter change pattern and identification features. After collecting the required operating data, target features, i.e., features related to various fault types, can be extracted from the operating data, specifically the pre-processed operating data. Among them, target features may include statistical features, frequency domain features, time domain features, machine learning features, etc. Statistical features may include mean (mean, average value of a data set), standard deviation (Standard Deviation, degree of dispersion of a data set), skewness (skewness, asymmetry of data distribution), kurtosis (kurtosis, sharpness or flatness of data distribution), range (range, difference between maximum and minimum values ​​of data), etc. Frequency domain features may include fast Fourier transform (Fast Fourier Transform, FFT, converting time domain signals into frequency domain signals), power spectral density (Power Spectral Density, PSD, estimation of signal power spectrum), etc. Time domain features may include autocorrelation (Autocorrelation, correlation between a data point and itself at different time delays), first-order difference (First-orderDifference, change between consecutive data points), etc. Machine learning features may include principal component analysis (PCA, dimensionality reduction technology, extracting the main change direction of data), random projection (Random Projection, used for dimensionality reduction of high-dimensional data), etc. Then, the extracted target features can be compared with the preset features of each fault type entry in the fault feature database, so as to automatically identify and classify the fault type diagnosis results according to the comparison results. The comparison process can use a similarity measurement method to determine the best matching fault type. Among them, the similarity measurement method can include Euclidean distance, Manhattan distance, cosine similarity, Pearson correlation coefficient, etc. Furthermore, the fault diagnosis system has the ability to learn and update, and can continuously enrich and improve the fault feature database based on new fault cases and expert knowledge.

[0065] Further optionally, the determination of the fault diagnosis result according to the operating data further includes: determining the fault location diagnosis result and the fault cause diagnosis result according to the fault type diagnosis result, the operating data and the topological structure of the power equipment. Specifically, an advanced positioning algorithm can be applied, combined with the topological structure of the power equipment and the real-time operating data, to accurately determine the location of the fault and obtain the fault location diagnosis result. Then, based on the fault type diagnosis result and the historical operating data of the power equipment, the possible fault causes are analyzed to obtain the fault cause diagnosis result.

[0066] When an early warning occurs, the fault diagnosis system can quickly locate the cause and location of the fault and respond to the fault warning quickly, significantly shortening the fault diagnosis time, improving the accuracy of fault location and cause analysis, and reducing the risk of misoperation. At the same time, the automated fault diagnosis process reduces dependence on manual experience and improves operation and maintenance efficiency. The fault diagnosis results can provide clear guidance for operation and maintenance personnel and speed up fault handling.

[0067] S14: Generate an operation and maintenance strategy and an operation and maintenance resource allocation plan according to the fault diagnosis result and the current operation and maintenance resource status, and dispatch operation and maintenance resources to handle the fault according to the operation and maintenance strategy and the operation and maintenance resource allocation plan.

[0068] Specifically, after obtaining the fault diagnosis results, the centralized control center can generate an operation and maintenance strategy and an operation and maintenance resource allocation plan based on the current operation and maintenance resource status, so as to automatically dispatch the operation and maintenance resources to handle the fault according to the operation and maintenance strategy and the operation and maintenance resource allocation plan. Among them, the operation and maintenance resources may include personnel, tools, spare parts, etc., and the operation and maintenance resource status may include the availability and location of the operation and maintenance resources, etc. The intelligent scheduling process can be specifically implemented through an intelligent scheduling system. The intelligent scheduling system can receive the fault diagnosis results provided by the fault diagnosis system, and can monitor the operation and maintenance resource status in real time, such as evaluating the currently available operation and maintenance resources, and determining the priority of resource allocation, etc. The resource optimization allocation algorithm can be integrated into the intelligent scheduling system to automatically calculate the optimal operation and maintenance resource allocation plan based on the fault diagnosis results and the operation and maintenance resource status to achieve rapid response and processing. Among them, the constraints used by the resource optimization allocation algorithm may include the availability of operation and maintenance resources, the type of fault, the geographical location, the skill requirements of maintenance personnel, etc., such as resource quantity constraints to ensure that the allocated resources do not exceed the available resources, that is, Among them, a ij represents the number of j-th resources allocated to fault point i, A j represents the total amount of the jth resource, or the fault demand constraint to ensure that each fault has enough resources to repair it, that is, Among them, D iIt represents the minimum amount of resources required to handle fault point i. After the operation and maintenance resource allocation plan is generated, the rapid response mechanism can be automatically triggered to dispatch the nearest resources to the fault site. In addition, the task assignment list can be automatically generated in combination with the operation and maintenance strategy and automatically issued to the corresponding operation and maintenance personnel for execution, thereby clarifying the responsibilities and operation steps of the operation and maintenance personnel.

[0069] Optionally, generating an operation and maintenance strategy based on the fault diagnosis result and the current operation and maintenance resource status includes: determining the impact of the fault on the operation of the power system based on the fault diagnosis result; generating an operation and maintenance plan based on the impact; performing a risk assessment on the operation and maintenance plan, and optimizing the operation and maintenance plan based on identified risk points; and generating the operation and maintenance strategy based on the optimized operation and maintenance plan.

[0070] Specifically, after obtaining the fault diagnosis results, the impact of the fault on the operation of the power system can be analyzed, such as the scope and duration of the power outage, and the impact content can be obtained. Then, a preliminary operation and maintenance plan can be formulated based on the impact content, such as the steps, timetable, resource requirements, etc. of fault repair. Then, a risk assessment is conducted on the preliminary operation and maintenance plan to identify possible risk points, and the operation and maintenance plan is optimized based on the identified risk points to reduce the risk. Subsequently, a detailed operation and maintenance strategy can be formulated based on the optimized operation and maintenance plan, such as specific operations such as personnel division of labor, spare parts replacement, and system testing. Furthermore, a standardized response process can be provided for similar faults based on the fault diagnosis results and the generated operation and maintenance plan and operation and maintenance strategy.

[0071] By quickly formulating operation and maintenance plans and strategies based on the fault diagnosis results, the fault response speed is improved. At the same time, through risk assessment and optimization, the uncertainty and risk in the operation and maintenance process are reduced. Detailed operation and maintenance strategies ensure the systematic and standardized fault handling and improve the quality of operation and maintenance. The formulation and updating of plans provide a guarantee for rapid response to similar faults in the future and enhance the resilience of the power system.

[0072] Optionally, after scheduling the operation and maintenance resources to handle the fault according to the operation and maintenance strategy and the operation and maintenance resource allocation plan, it also includes: real-time monitoring of the operation and maintenance resource status and fault handling progress; dynamically adjusting the operation and maintenance resource allocation plan according to the current operation and maintenance resource status and fault handling progress in real time, and scheduling based on the adjusted operation and maintenance resource allocation plan.

[0073] The intelligent dispatching system optimizes the allocation of operation and maintenance resources, realizes rapid fault response and processing, and improves fault response speed and processing efficiency through automated resource allocation. The dynamic adjustment mechanism ensures the flexibility and adaptability of resource allocation, the task assignment and information feedback mechanism improves the transparency and traceability of operation and maintenance work, and data analysis and learning improve the intelligence level of the dispatching system and optimize long-term operation and maintenance efficiency.

[0074] The technical solution provided by the embodiment of the present invention first obtains the operating parameters of the power equipment in real time, then uses the trained fault prediction model to identify the fault state of the power equipment according to the real-time operating parameters and generates a corresponding warning signal, then collects the operating data before and after the fault occurs according to the warning signal, and determines the fault diagnosis result according to the collected operating data, and finally generates the operation and maintenance strategy and the operation and maintenance resource allocation plan according to the obtained fault diagnosis result and the current operation and maintenance resource state, and dispatches the operation and maintenance resources to handle the fault according to the operation and maintenance strategy and the operation and maintenance resource allocation plan. Through the automated fault warning mechanism and fault response mechanism, the operation and maintenance efficiency and quality are improved. Thereby, the substation operation and maintenance mode is improved, the problem of frequent flow of operation and maintenance personnel is solved, the equipment inspection is refined, the status is grasped in real time, and the digital team construction is improved, and it is convenient to realize the centralized monitoring, control and fault warning of the large-scale centralized wind, solar and storage multi-energy complementary stations to meet the unmanned and less-staffed operation mode, and it is convenient to study the technical solution design and application effect evaluation of the access of new energy power generation to the power centralized control center, so as to realize the effective management and control of new energy.

[0075] Embodiment 2

[0076] Figure 2 This is a schematic diagram of the structure of the operation and maintenance planning device for power equipment provided in the second embodiment of the present invention. The device can be implemented by hardware and / or software, and can generally be integrated into a computer device to execute the operation and maintenance planning method for power equipment provided in any embodiment of the present invention. Figure 2 As shown, the device comprises:

[0077] An operating parameter acquisition module 21 is used to acquire operating parameters of power equipment in real time;

[0078] A warning signal generating module 22, configured to identify the fault state of the power equipment and generate a warning signal using a trained fault prediction model according to the operating parameters;

[0079] A fault diagnosis result determination module 23 collects operation data before and after the fault occurs according to the warning signal, and determines a fault diagnosis result according to the operation data;

[0080] The operation and maintenance scheduling processing module 24 is used to generate an operation and maintenance strategy and an operation and maintenance resource allocation plan according to the fault diagnosis result and the current operation and maintenance resource status, and schedule operation and maintenance resources to handle the fault according to the operation and maintenance strategy and the operation and maintenance resource allocation plan.

[0081] The technical solution provided by the embodiment of the present invention first obtains the operating parameters of the power equipment in real time, then uses the trained fault prediction model to identify the fault state of the power equipment according to the real-time operating parameters and generates a corresponding warning signal, then collects the operating data before and after the fault occurs according to the warning signal, and determines the fault diagnosis result according to the collected operating data, and finally generates the operation and maintenance strategy and the operation and maintenance resource allocation plan according to the obtained fault diagnosis result and the current operation and maintenance resource state, and dispatches the operation and maintenance resources to handle the fault according to the operation and maintenance strategy and the operation and maintenance resource allocation plan. Through the automated fault warning mechanism and fault response mechanism, the operation and maintenance efficiency and quality are improved. Thereby, the substation operation and maintenance mode is improved, the problem of frequent flow of operation and maintenance personnel is solved, the equipment inspection is refined, the status is grasped in real time, and the digital team construction is improved, and it is convenient to realize the centralized monitoring, control and fault warning of the large-scale centralized wind, solar and storage multi-energy complementary stations to meet the unmanned and less-staffed operation mode, and it is convenient to study the technical solution design and application effect evaluation of the access of new energy power generation to the power centralized control center, so as to realize the effective management and control of new energy.

[0082] On the basis of the above technical solution, optionally, the fault diagnosis result determination module 23 includes:

[0083] A target feature extraction unit, used to extract target features according to the operation data;

[0084] The fault type determination unit is used to compare the target feature with preset features of various fault types to determine a fault type diagnosis result.

[0085] On the basis of the above technical solution, optionally, the fault diagnosis result determination module 23 further includes:

[0086] The fault location and fault cause determination unit is used to determine the fault location diagnosis result and the fault cause diagnosis result according to the fault type diagnosis result, the operation data and the topological structure of the power equipment.

[0087] On the basis of the above technical solution, optionally, the operation and maintenance scheduling processing module 24 includes:

[0088] An impact content determination unit, used to determine the impact content of the fault on the operation of the power system according to the fault diagnosis result;

[0089] An operation and maintenance plan generating unit, used to generate an operation and maintenance plan according to the impact content;

[0090] An operation and maintenance plan optimization unit, used to perform risk assessment on the operation and maintenance plan and optimize the operation and maintenance plan according to the identified risk points;

[0091] An operation and maintenance strategy generating unit is used to generate the operation and maintenance strategy according to the optimized operation and maintenance plan.

[0092] On the basis of the above technical solution, optionally, the operation and maintenance planning device of the power equipment further includes:

[0093] A processing progress monitoring module, used for real-time monitoring of the operation and maintenance resource status and the fault processing progress after the operation and maintenance resources are scheduled to process the fault according to the operation and maintenance strategy and the operation and maintenance resource allocation plan;

[0094] The allocation scheme adjustment module is used to dynamically adjust the operation and maintenance resource allocation scheme according to the current operation and maintenance resource status and fault handling progress in real time, and perform scheduling based on the adjusted operation and maintenance resource allocation scheme.

[0095] On the basis of the above technical solution, optionally, the operation and maintenance planning device of the power equipment further includes:

[0096] An operating parameter storage module, used for storing the operating parameters after the operating parameters of the electric power equipment are acquired in real time;

[0097] The data anomaly detection module is used to perform anomaly detection based on the stored operating parameters to identify target parameters that deviate from the normal operating range.

[0098] The operation and maintenance planning device for electric power equipment provided in the embodiment of the present invention can execute the operation and maintenance planning method for electric power equipment provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0099] It is worth noting that in the embodiment of the operation and maintenance planning device of the above-mentioned power equipment, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0100] Embodiment 3

[0101] Figure 3 The schematic diagram of the structure of the computer device provided for the third embodiment of the present invention shows a block diagram of an exemplary computer device suitable for implementing the implementation mode of the present invention. Figure 3 The computer device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention. Figure 3 As shown, the computer device includes a processor 31, a memory 32, an input device 33 and an output device 34; the number of processors 31 in the computer device can be one or more. Figure 3Taking a processor 31 as an example, the processor 31, memory 32, input device 33 and output device 34 in the computer device can be connected through a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0102] The memory 32, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the operation and maintenance planning method of the power equipment in the embodiment of the present invention (for example, the operation parameter acquisition module 21, the early warning signal generation module 22, the fault diagnosis result determination module 23 and the operation and maintenance scheduling processing module 24 in the operation and maintenance planning device of the power equipment). The processor 31 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 32, that is, realizes the above-mentioned operation and maintenance planning method of the power equipment.

[0103] The memory 32 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 32 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 32 may further include a memory remotely arranged relative to the processor 31, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0104] The input device 33 can be used to obtain the operating parameters of the power equipment in real time, and to generate key signal inputs related to user settings and function control of the computer equipment, etc. The output device 34 can be used to send scheduling information to operation and maintenance personnel, etc.

[0105] Embodiment 4

[0106] Embodiment 4 of the present invention further provides a storage medium containing computer executable instructions, which, when executed by a computer processor, is used to execute an operation and maintenance planning method for power equipment, the method comprising:

[0107] Obtain the operating parameters of power equipment in real time;

[0108] According to the operating parameters, using a trained fault prediction model to identify the fault state of the power equipment and generate a warning signal;

[0109] Collecting operation data before and after the fault occurs according to the early warning signal, and determining the fault diagnosis result according to the operation data;

[0110] An operation and maintenance strategy and an operation and maintenance resource allocation plan are generated according to the fault diagnosis result and the current operation and maintenance resource status, and operation and maintenance resources are scheduled to handle the fault according to the operation and maintenance strategy and the operation and maintenance resource allocation plan.

[0111] The storage medium may be any of various types of memory devices or storage devices. The term "storage medium" is intended to include: installation media, such as CD-ROM, floppy disk or tape device; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (such as hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in the computer system in which the program is executed, or may be located in a different second computer system, which is connected to the computer system via a network (such as the Internet). The second computer system may provide program instructions to the computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations (e.g., in different computer systems connected via a network). The storage medium may store program instructions (e.g., embodied as a computer program) that may be executed by one or more processors.

[0112] Of course, the storage medium containing computer executable instructions provided in an embodiment of the present invention is not limited to the method operations described above, and can also execute related operations in the operation and maintenance planning method of the power equipment provided in any embodiment of the present invention.

[0113] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0114] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0115] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0116] Embodiment 5

[0117] Embodiment 5 of the present invention also provides a computer program product, which includes a computer program (also referred to as code, instruction), which can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it is used to execute the operation and maintenance planning method of the power equipment provided in any of the above embodiments, and has the corresponding beneficial effects of the execution method.

[0118] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for operation and maintenance planning of electric power equipment, characterized in that: include: Obtain the operating parameters of power equipment in real time; According to the operating parameters, using a trained fault prediction model to identify the fault state of the power equipment and generate a warning signal; Collecting operation data before and after the fault occurs according to the early warning signal, and determining the fault diagnosis result according to the operation data; An operation and maintenance strategy and an operation and maintenance resource allocation plan are generated according to the fault diagnosis result and the current operation and maintenance resource status, and operation and maintenance resources are scheduled to handle the fault according to the operation and maintenance strategy and the operation and maintenance resource allocation plan.

2. The operation and maintenance planning method of power equipment according to claim 1, characterized in that: Determining the fault diagnosis result according to the operating data includes: extracting target features according to the operating data; The target feature is compared with preset features of various fault types to determine the fault type diagnosis result.

3. The operation and maintenance planning method of electric power equipment according to claim 2, characterized in that: Determining the fault diagnosis result according to the operating data further includes: A fault location diagnosis result and a fault cause diagnosis result are determined according to the fault type diagnosis result, the operating data and the topological structure of the power equipment.

4. The operation and maintenance planning method of power equipment according to claim 1, characterized in that: Generating an operation and maintenance strategy according to the fault diagnosis result and the current operation and maintenance resource status includes: Determine the impact of the fault on the operation of the power system according to the fault diagnosis result; Generate an operation and maintenance plan based on the impact content; Conduct risk assessment on the operation and maintenance plan, and optimize the operation and maintenance plan based on identified risk points; The operation and maintenance strategy is generated according to the optimized operation and maintenance plan.

5. The operation and maintenance planning method of power equipment according to claim 1, characterized in that: After scheduling the operation and maintenance resources to handle the fault according to the operation and maintenance strategy and the operation and maintenance resource allocation scheme, the method further includes: Real-time monitoring of operation and maintenance resource status and fault handling progress; The operation and maintenance resource allocation scheme is dynamically adjusted in real time according to the current operation and maintenance resource status and fault handling progress, and scheduling is performed based on the adjusted operation and maintenance resource allocation scheme.

6. The operation and maintenance planning method of electric power equipment according to claim 1, characterized in that: After the real-time acquisition of the operating parameters of the power equipment, the method further includes: Storing the operating parameters; Anomaly detection is performed based on the stored operating parameters to identify target parameters that deviate from a normal operating range.

7. An operation and maintenance planning device for electric power equipment, characterized in that: include: An operating parameter acquisition module is used to obtain the operating parameters of the power equipment in real time; A warning signal generating module, used to identify the fault state of the power equipment and generate a warning signal using a trained fault prediction model according to the operating parameters; A fault diagnosis result determination module collects operation data before and after the fault occurs according to the warning signal, and determines the fault diagnosis result according to the operation data; The operation and maintenance scheduling processing module is used to generate an operation and maintenance strategy and an operation and maintenance resource allocation plan according to the fault diagnosis result and the current operation and maintenance resource status, and schedule operation and maintenance resources to handle the fault according to the operation and maintenance strategy and the operation and maintenance resource allocation plan.

8. A computer device, characterized in that: include: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the operation and maintenance planning method for power equipment as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the operation and maintenance planning method of the power equipment as described in any one of claims 1-6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the operation and maintenance planning method of the power equipment as described in any one of claims 1 to 6.

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