Technical water supply system fault identification method and device of pumped storage unit under power generation phase modulation working condition, computer equipment, storage medium and computer program product

Abstract

The invention relates to a technical water supply system fault identification method and device of a pumped storage unit under a power generation phase modulation working condition, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring monitoring data of the pumped storage unit under a power generation phase modulation working condition; inputting the monitoring data into a pre-trained technical water supply system fault identification model, and outputting a fault identification result for the technical water supply system based on the monitoring data through the pre-trained technical water supply system fault identification model; the pre-trained technical water supply system fault identification model is obtained by training a historical monitoring data set of the pumped storage unit under a power generation phase modulation working condition; generating a fault processing text according to the fault identification result; the fault handling text includes fault handling operations required to be performed for the technical water supply system. By adopting the method, fault detection can be accurately carried out on the technical water supply system of the pumped storage unit under the power generation phase modulation working condition.

Description

Technical Field

[0001] The present application relates to the field of pumped storage technology, and in particular to a method, device, computer equipment, storage medium and computer program product for identifying faults in a technical water supply system of a pumped storage unit under power generation phase modulation conditions. Background Art

[0002] Pumped-storage hydroelectric generators are synchronous motors capable of both pumping and generating electricity, utilizing water energy for energy conversion. These units consist of pumps, turbines, generators, and control systems. As key electrical equipment in hydropower stations, pumped-storage hydroelectric generators offer rapid startup and shutdown, enabling quick and easy load adjustment. They perform a significant amount of peak load regulation, frequency regulation, voltage regulation, and emergency backup within the power grid.

[0003] Currently, general fault detection methods are often used to detect faults in the technical water supply system of a pumped-storage unit operating under power generation phase modulation. However, because the operation of the technical water supply system under power generation phase modulation differs from that under other operating conditions, general fault detection methods are unable to accurately detect faults in this specific operating condition.

[0004] Therefore, there is a problem in traditional technologies that it is impossible to accurately detect faults in the technical water supply system of the pumped storage unit under power generation phase adjustment conditions. Summary of the Invention

[0005] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for identifying faults in the technical water supply system of a pumped-storage unit under power generation phase modulation conditions, which can accurately detect faults in the technical water supply system of the pumped-storage unit under power generation phase modulation conditions.

[0006] A method for identifying faults in a technical water supply system of a pumped storage unit under power generation phase modulation conditions, the method comprising:

[0007] Obtain monitoring data of pumped storage units under power generation phase adjustment conditions;

[0008] The monitoring data is input into a pre-trained technical water supply system fault identification model, which outputs a fault identification result for the technical water supply system based on the monitoring data; the pre-trained technical water supply system fault identification model is trained using a historical monitoring data set of pumped storage units under power generation phase modulation conditions;

[0009] A fault handling text is generated based on the fault identification result; the fault handling text includes the fault handling operations that need to be performed on the technical water supply system.

[0010] In one embodiment, the monitoring data is input into a pre-trained technical water supply system fault identification model. The pre-trained technical water supply system fault identification model outputs a fault identification result for the technical water supply system based on the monitoring data, including:

[0011] Preprocessing the monitoring data to obtain processed monitoring data;

[0012] The processed monitoring data is input into the feature extraction network of the pre-trained technical water supply system fault identification model, and the state features of the technical water supply system are extracted through the feature extraction network;

[0013] Based on the status characteristics, the fault identification results are output.

[0014] In one embodiment, the feature extraction network includes a spatial feature extraction network and a temporal feature extraction network. The processed monitoring data is input into the feature extraction network of the pre-trained technical water supply system fault identification model. The feature extraction network extracts the status features of the technical water supply system, including:

[0015] The processed monitoring data are input into the spatial feature extraction network and the temporal feature extraction network respectively, the spatial features are extracted by the spatial feature extraction network, and the temporal features are extracted by the temporal feature extraction network;

[0016] The spatial features and temporal features are fused to obtain the state features.

[0017] In one embodiment, the method further comprises:

[0018] Obtain historical monitoring data sets;

[0019] Using the historical monitoring data set, the technical water supply system fault recognition model to be trained is trained to obtain a trained technical water supply system fault recognition model;

[0020] The performance of the trained technical water supply system fault identification model was verified using test data to obtain the model performance verification results.

[0021] When the model performance verification result meets the preset conditions, the trained technical water supply system fault identification model is used as the pre-trained technical water supply system fault identification model;

[0022] In the case that the model performance verification result does not meet the preset conditions, the step of using the historical monitoring data set to train the technical water supply system fault identification model to be trained is returned to, until the model performance verification result meets the preset conditions.

[0023] In one embodiment, a fault handling text is generated based on the fault identification result, including:

[0024] Determine the fault type based on the fault identification results;

[0025] Obtain at least one fault handling rule that matches the fault type from a pre-built fault handling rule library;

[0026] Generate fault handling text according to each fault handling rule.

[0027] In one embodiment, obtaining monitoring data of a pumped storage unit under a power generation phase adjustment condition includes:

[0028] Obtain valve status parameter data, water pump parameter data and hydraulic parameter data;

[0029] Monitoring data is generated based on valve status parameter data, water pump parameter data and hydraulic parameter data.

[0030] A device for identifying faults in a technical water supply system of a pumped storage unit under power generation phase modulation conditions, the device comprising:

[0031] An acquisition module is used to obtain monitoring data of the pumped storage unit under power generation phase adjustment conditions;

[0032] An input module is configured to input monitoring data into a pre-trained technical water supply system fault identification model, and output a fault identification result for the technical water supply system based on the monitoring data through the pre-trained technical water supply system fault identification model; the pre-trained technical water supply system fault identification model is trained using a historical monitoring data set of pumped storage units under power generation phase modulation conditions;

[0033] The generation module is used to generate a fault handling text based on the fault identification result; the fault handling text includes the fault handling operations required to be performed on the technical water supply system.

[0034] A computer device includes a memory and a processor, wherein the memory stores a computer program and the processor implements the steps of the above method when executing the computer program.

[0035] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0036] A computer program product comprises a computer program, which implements the steps of the above method when executed by a processor.

[0037] The above-mentioned method, device, computer equipment, storage medium and computer program product for identifying faults in the technical water supply system of the pumped-storage unit under the power generation phase modulation condition obtains the monitoring data of the pumped-storage unit under the power generation phase modulation condition; inputs the monitoring data into a pre-trained technical water supply system fault identification model, and outputs a fault identification result for the technical water supply system based on the monitoring data through the pre-trained technical water supply system fault identification model; the pre-trained technical water supply system fault identification model is trained using a historical monitoring data set of the pumped-storage unit under the power generation phase modulation condition; a fault handling text is generated based on the fault identification result; the fault handling text includes the fault handling operations required to be performed for the technical water supply system; in this way, the pre-trained technical water supply system fault identification model can be used to accurately parse the monitoring data of the pumped-storage unit under the power generation phase modulation condition to identify faults, and generate fault handling suggestions, which can improve the efficiency of fault location and the speed of response to faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 This is a diagram illustrating an application environment of a method for identifying faults in a technical water supply system of a pumped storage unit under a power generation phase modulation condition in one embodiment;

[0040] Figure 2 1 is a flow chart of a method for identifying faults in a technical water supply system of a pumped storage unit under power generation phase modulation conditions in one embodiment;

[0041] Figure 3 1 is a flow chart of a method for identifying faults in a technical water supply system of a pumped storage unit under power generation phase modulation conditions in another embodiment;

[0042] Figure 4 A structural block diagram of a fault identification device for a technical water supply system of a pumped storage unit under a power generation phase adjustment condition in one embodiment;

[0043] Figure 5 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0045] The method for identifying faults in the technical water supply system of a pumped storage unit under power generation phase adjustment conditions provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store data that server 104 needs to process. The data storage system can be integrated with server 104, or located in the cloud or on other network servers. Server 104 obtains monitoring data of the pumped-storage unit under power generation phase modulation conditions. Server 104 inputs the monitoring data into a pre-trained technical water supply system fault identification model. The pre-trained technical water supply system fault identification model outputs a fault identification result for the technical water supply system based on the monitoring data. The pre-trained technical water supply system fault identification model is trained using a historical monitoring data set of the pumped-storage unit under power generation phase modulation conditions. Server 104 generates a fault handling document based on the fault identification result. The fault handling document includes the required fault handling operations for the technical water supply system. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart car devices, etc. Portable wearable devices can include smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers.

[0046] In an exemplary embodiment, Figure 2 As shown in the figure, a method for identifying faults in the technical water supply system of a pumped storage unit under the phase modulation condition of power generation is provided. Figure 1 The server 104 in the example is used as an example to illustrate the process, including the following steps S202 to S206.

[0047] Step S202: Acquire monitoring data of the pumped storage unit under power generation phase adjustment conditions.

[0048] Among them, the power generation phase adjustment condition of the pumped storage unit refers to an operating state in which the pumped storage unit adjusts the excitation current in the power generation direction to transmit or absorb reactive power to the power grid, thereby achieving the purpose of regulating the grid voltage stability.

[0049] The monitoring data may be various state parameter data representing the pumped storage unit under power generation phase adjustment conditions.

[0050] Optionally, the server obtains monitoring data of the pumped storage unit under power generation phase adjustment conditions.

[0051] Step S204: input the monitoring data into a pre-trained technical water supply system fault identification model, and output a fault identification result for the technical water supply system based on the monitoring data through the pre-trained technical water supply system fault identification model; the pre-trained technical water supply system fault identification model is trained using a historical monitoring data set of the pumped storage unit under power generation phase modulation conditions.

[0052] Among them, the pre-trained technical water supply system fault identification model is trained using the historical monitoring data set of pumped storage units under power generation phase modulation conditions.

[0053] The fault identification result can characterize the fault type when a fault occurs in the technical water supply system.

[0054] Optionally, the server inputs the monitoring data into a pre-trained technical water supply system fault identification model, and the pre-trained technical water supply system fault identification model outputs a fault type identification result for the technical water supply system based on the monitoring data.

[0055] Step S206: Generate a fault handling text based on the fault identification result; the fault handling text includes the fault handling operations required to be performed on the technical water supply system.

[0056] Among them, the fault handling text may include fault handling suggestions, which may include multiple fault handling operations that need to be performed. In actual applications, maintenance personnel can perform standard operations according to the fault handling text to achieve fault handling.

[0057] Optionally, the server generates a fault handling text based on the fault identification result.

[0058] In the above-mentioned method for identifying faults in the technical water supply system of the pumped-storage unit under the power generation phase modulation condition, the monitoring data of the pumped-storage unit under the power generation phase modulation condition is obtained; the monitoring data is input into a pre-trained technical water supply system fault identification model, and the pre-trained technical water supply system fault identification model outputs a fault identification result for the technical water supply system based on the monitoring data; the pre-trained technical water supply system fault identification model is trained using a historical monitoring data set of the pumped-storage unit under the power generation phase modulation condition; a fault handling text is generated based on the fault identification result; the fault handling text includes the fault handling operations required to be performed on the technical water supply system; in this way, the pre-trained technical water supply system fault identification model can be used to accurately parse the monitoring data of the pumped-storage unit under the power generation phase modulation condition to identify the fault, and generate fault handling suggestions, which can improve the efficiency of fault location and the speed of response to the fault.

[0059] In an exemplary embodiment, the monitoring data is input into a pre-trained technical water supply system fault identification model, and the pre-trained technical water supply system fault identification model outputs a fault identification result for the technical water supply system based on the monitoring data, including: pre-processing the monitoring data to obtain processed monitoring data; inputting the processed monitoring data into a feature extraction network in the pre-trained technical water supply system fault identification model, extracting the state characteristics of the technical water supply system through the feature extraction network; and outputting the fault identification result based on the state characteristics.

[0060] The processed monitoring data may be standardized monitoring data that is convenient for feature extraction.

[0061] The feature extraction network may be a neural network for extracting spatiotemporal features.

[0062] Among them, the state characteristics can represent the current operating state of the technical water supply system.

[0063] Optionally, the server preprocesses the monitoring data to obtain processed monitoring data, and the server inputs the processed monitoring data into a feature extraction network in a pre-trained technical water supply system fault identification model, extracts the status characteristics of the technical water supply system through the feature extraction network, and outputs the fault identification result based on the status characteristics.

[0064] In this embodiment, the monitoring data is preprocessed to obtain processed monitoring data; the processed monitoring data is input into the feature extraction network in the pre-trained technical water supply system fault identification model, and the state characteristics of the technical water supply system are extracted through the feature extraction network; based on the state characteristics, the fault identification result is output; in this way, the fault can be accurately identified after data preprocessing and model feature extraction.

[0065] In an exemplary embodiment, the feature extraction network includes a spatial feature extraction network and a temporal feature extraction network. The processed monitoring data is input into the feature extraction network in a pre-trained technical water supply system fault identification model, and the state characteristics of the technical water supply system are extracted through the feature extraction network, including: inputting the processed monitoring data into the spatial feature extraction network and the temporal feature extraction network respectively, extracting spatial features through the spatial feature extraction network, and extracting temporal features through the temporal feature extraction network; fusing the spatial features and the temporal features to obtain state features.

[0066] The spatial feature extraction network may be a CNN (Convolutional Neural Network) network.

[0067] The time series feature extraction network may be an LSTM network (Long Short-Term Memory).

[0068] In the present application, the pre-trained technical water supply system fault identification model can be a CNN-LSTM model that combines a CNN network and an LSTM network.

[0069] Optionally, the server inputs the processed monitoring data into the CNN network and the LSTM network respectively, extracts spatial features through the CNN network, and extracts temporal features through the LSTM network; and fuses the spatial features and temporal features to obtain state features.

[0070] In this embodiment, the processed monitoring data are respectively input into the spatial feature extraction network and the temporal feature extraction network, and the spatial features are extracted by the spatial feature extraction network, and the temporal features are extracted by the temporal feature extraction network; the spatial features and the temporal features are fused to obtain the state features; in this way, the spatial features and the temporal features can be fused to obtain the state features that can characterize the operating state, which is conducive to improving the accuracy of fault identification.

[0071] In an exemplary embodiment, the method also includes: obtaining a historical monitoring data set; using the historical monitoring data set to train the technical water supply system fault identification model to be trained to obtain a trained technical water supply system fault identification model; using test data to verify the model performance of the trained technical water supply system fault identification model to obtain a model performance verification result; when the model performance verification result meets the preset conditions, the trained technical water supply system fault identification model is used as a pre-trained technical water supply system fault identification model; when the model performance verification result does not meet the preset conditions, returning to the step of using the historical monitoring data set to train the technical water supply system fault identification model to be trained until the model performance verification result meets the preset conditions.

[0072] The historical monitoring data set at least includes valve status parameter data, water pump parameter data and hydraulic parameter data collected at each historical time point. The historical monitoring data set may also store other operating status parameter data.

[0073] The trained technical water supply system fault identification model may be a technical water supply system fault identification model that has been trained but not tested.

[0074] The test data may be data separated from the historical monitoring data set and specifically used to test the performance of the model.

[0075] The model performance verification result may indicate whether the model performance meets the preset conditions, and the preset conditions may refer to whether the prediction accuracy of the model reaches the preset accuracy.

[0076] Optionally, the server obtains a historical monitoring data set and uses the historical monitoring data set to train the technical water supply system fault identification model to be trained to obtain the trained technical water supply system fault identification model. The server uses test data to verify the model performance of the trained technical water supply system fault identification model to obtain a model performance verification result. When the model performance verification result meets the preset conditions, the server uses the trained technical water supply system fault identification model as a pre-trained technical water supply system fault identification model. When the model performance verification result does not meet the preset conditions, the server returns to the step of using the historical monitoring data set to train the technical water supply system fault identification model to be trained until the model performance verification result meets the preset conditions.

[0077] In this embodiment, a historical monitoring data set is obtained; the historical monitoring data set is used to train the technical water supply system fault identification model to be trained to obtain the trained technical water supply system fault identification model; the test data is used to verify the model performance of the trained technical water supply system fault identification model to obtain a model performance verification result; when the model performance verification result meets the preset conditions, the trained technical water supply system fault identification model is used as the pre-trained technical water supply system fault identification model; when the model performance verification result does not meet the preset conditions, the step of using the historical monitoring data set to train the technical water supply system fault identification model to be trained is returned until the model performance verification result meets the preset conditions; in this way, the historical monitoring data set of the pumped-storage unit under the power generation phase modulation condition can be used to conduct targeted training on the technical water supply system fault identification model to be trained, thereby obtaining a pre-trained technical water supply system fault identification model that can efficiently and accurately determine the fault type of the pumped-storage unit under the power generation phase modulation condition.

[0078] In an exemplary embodiment, a fault handling text is generated based on the fault identification result, including: determining the fault type based on the fault identification result; obtaining at least one fault handling rule that matches the fault type from a pre-built fault handling rule library; and generating a fault handling text based on each fault handling rule.

[0079] The fault type may be a mechanical fault type, an electrical fault type, a hydraulic fault type or the like.

[0080] The pre-built fault handling rule library records multiple fault handling rules for different fault types.

[0081] The fault handling rule that matches the fault type may refer to a standardized processing rule that is applicable to solving the fault type.

[0082] Optionally, the server determines the fault type based on the fault identification result, obtains various fault handling rules that match the fault type from a pre-built fault handling rule library, and generates a fault handling text based on the various fault handling rules.

[0083] In this embodiment, the fault type is determined based on the fault identification result; at least one fault handling rule that matches the fault type is obtained from a pre-built fault handling rule library; and a fault handling text is generated based on each fault handling rule. The fault handling text can be generated quickly, which is conducive to accurately and efficiently responding to faults.

[0084] In an exemplary embodiment, obtaining monitoring data of a pumped storage unit under power generation phase adjustment conditions includes: obtaining valve state parameter data, water pump parameter data, and hydraulic parameter data; and generating monitoring data based on the valve state parameter data, water pump parameter data, and hydraulic parameter data.

[0085] The valve state parameter data may be state parameter data representing a switch.

[0086] Among them, the water pump parameter data may include current data, vibration data and temperature data. The current data when the water pump is running can intuitively reflect its working status. The vibration data when the water pump is running can reflect the operating status of its mechanical parts. The temperature data may include the temperature of the pump body and the motor. The temperature of the pump body can reflect the working condition of the pump body, and the temperature of the motor can reflect the operating status of the motor.

[0087] The hydraulic parameter data may include pressure data and flow data. The pressure data represents the pressure of the liquid in the pipeline or equipment, and the flow data represents the operating status of the pipeline.

[0088] Optionally, the server obtains valve state parameter data, water pump parameter data and hydraulic parameter data, and generates monitoring data based on the valve state parameter data, water pump parameter data and hydraulic parameter data.

[0089] In this embodiment, valve status parameter data, water pump parameter data and hydraulic parameter data are obtained; monitoring data is generated based on the valve status parameter data, water pump parameter data and hydraulic parameter data; in this way, multiple types of operating status parameter data can be obtained to generate monitoring data, which is conducive to the subsequent precise positioning of faults in the technical water supply system of the pumped storage unit under the power generation phase adjustment condition.

[0090] In another embodiment, Figure 3As shown in the figure, a method for identifying faults in the technical water supply system of a pumped storage unit under the phase modulation condition of power generation is provided. Figure 1 Taking the server 104 in the example as an example, the following steps are included:

[0091] Step S302: Acquire monitoring data of the pumped storage unit under power generation phase adjustment conditions.

[0092] Step S304: input the monitoring data into a pre-trained technical water supply system fault identification model, and output a fault identification result for the technical water supply system based on the monitoring data through the pre-trained technical water supply system fault identification model; the pre-trained technical water supply system fault identification model is trained using a historical monitoring data set of the pumped storage unit under power generation phase modulation conditions.

[0093] Step S306: Determine the fault type based on the fault identification result.

[0094] Step S308: Obtain at least one fault handling rule that matches the fault type from a pre-built fault handling rule library.

[0095] Step S310: Generate a fault handling text according to each fault handling rule; the fault handling text includes the fault handling operations required to be performed on the technical water supply system.

[0096] It should be noted that the specific limitations of the above steps can refer to the specific limitations of the technical water supply system fault identification method of a pumped storage unit under power generation phase adjustment conditions.

[0097] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0098] Based on the same inventive concept, an embodiment of the present application further provides a pumped-storage unit technical water supply system fault identification device under power generation phase adjustment conditions, which is used to implement the above-mentioned method for identifying technical water supply system faults of the pumped-storage unit under power generation phase adjustment conditions. The implementation solution provided by this device is similar to the implementation solution described in the above-mentioned method. Therefore, the specific limitations of the embodiments of the pumped-storage unit technical water supply system fault identification device under power generation phase adjustment conditions provided below can be referred to the above-mentioned limitations of the method for identifying technical water supply system faults of the pumped-storage unit under power generation phase adjustment conditions, and will not be repeated here.

[0099] In an exemplary embodiment, Figure 4 As shown, a device for identifying faults in a technical water supply system of a pumped storage unit under power generation phase adjustment conditions is provided, comprising: an acquisition module 402, an input module 404, and a generation module 406, wherein:

[0100] An acquisition module 402 is used to acquire monitoring data of the pumped storage unit under power generation phase adjustment conditions;

[0101] Input module 404 is configured to input the monitoring data into a pre-trained technical water supply system fault identification model, and output a fault identification result for the technical water supply system based on the monitoring data through the pre-trained technical water supply system fault identification model; the pre-trained technical water supply system fault identification model is trained using a historical monitoring data set of pumped storage units under power generation phase modulation conditions;

[0102] The generation module 406 is used to generate a fault handling text according to the fault identification result; the fault handling text includes the fault handling operations required to be performed on the technical water supply system.

[0103] In an exemplary embodiment, the input module 404 is used to preprocess the monitoring data to obtain processed monitoring data; input the processed monitoring data into the feature extraction network in the pre-trained technical water supply system fault identification model, and extract the state characteristics of the technical water supply system through the feature extraction network; based on the state characteristics, output the fault identification result.

[0104] In an exemplary embodiment, the feature extraction network includes a spatial feature extraction network and a temporal feature extraction network, and the input module 404 is used to input the processed monitoring data into the spatial feature extraction network and the temporal feature extraction network respectively, extract spatial features through the spatial feature extraction network, and extract temporal features through the temporal feature extraction network; the spatial features and the temporal features are fused to obtain state features.

[0105] In an exemplary embodiment, the device also includes: a training module for obtaining a historical monitoring data set; using the historical monitoring data set to train the technical water supply system fault identification model to be trained to obtain a trained technical water supply system fault identification model; using test data to verify the model performance of the trained technical water supply system fault identification model to obtain a model performance verification result; when the model performance verification result meets the preset conditions, the trained technical water supply system fault identification model is used as a pre-trained technical water supply system fault identification model; when the model performance verification result does not meet the preset conditions, return to the step of using the historical monitoring data set to train the technical water supply system fault identification model to be trained until the model performance verification result meets the preset conditions.

[0106] In an exemplary embodiment, the generation module 406 is specifically configured to determine the fault type based on the fault identification result; obtain at least one fault handling rule matching the fault type from a pre-built fault handling rule library; and generate a fault handling text based on each fault handling rule.

[0107] In an exemplary embodiment, the acquisition module 402 is specifically configured to acquire valve state parameter data, water pump parameter data, and hydraulic parameter data; and generate monitoring data based on the valve state parameter data, water pump parameter data, and hydraulic parameter data.

[0108] Each module in the aforementioned device for identifying faults in the technical water supply system of a pumped-storage unit under power generation phase modulation conditions can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in the form of software in a memory in the computer device, so that the processor can call and execute the corresponding operations of each module.

[0109] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 5As shown. The computer device includes a processor, a memory, an input / output (I / O) interface, and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store technical water supply system fault identification data of a pumped-storage unit under power generation phase modulation conditions. The I / O interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When executed by the processor, the computer program implements a method for identifying technical water supply system faults of a pumped-storage unit under power generation phase modulation conditions.

[0110] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0111] In one embodiment, a computer device is provided, comprising a memory and a processor. The memory stores a computer program. When executed by the processor, the computer program causes the processor to perform the steps of the aforementioned method for identifying a fault in the technical water supply system of a pumped-storage unit under power generation phase adjustment conditions. The steps of the method for identifying a fault in the technical water supply system of a pumped-storage unit under power generation phase adjustment conditions may be steps of the method for identifying a fault in the technical water supply system of a pumped-storage unit under power generation phase adjustment conditions in each of the aforementioned embodiments.

[0112] In one embodiment, a computer-readable storage medium is provided, storing a computer program. When executed by a processor, the computer program causes the processor to perform the steps of the aforementioned method for identifying a fault in the technical water supply system of a pumped-storage unit under power generation phase modulation conditions. The steps of the method for identifying a fault in the technical water supply system of a pumped-storage unit under power generation phase modulation conditions may be steps of the method for identifying a fault in the technical water supply system of a pumped-storage unit under power generation phase modulation conditions described in each of the aforementioned embodiments.

[0113] In one embodiment, a computer program product is provided, comprising a computer program. When executed by a processor, the computer program causes the processor to perform the steps of the aforementioned method for identifying a fault in the technical water supply system of a pumped-storage unit under power generation phase adjustment conditions. The steps of the method for identifying a fault in the technical water supply system of a pumped-storage unit under power generation phase adjustment conditions may be steps of the method for identifying a fault in the technical water supply system of a pumped-storage unit under power generation phase adjustment conditions described in each of the aforementioned embodiments.

[0114] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0115] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0116] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for identifying faults in the technical water supply system of a pumped storage unit under power generation phase modulation conditions, characterized in that: The method comprises: Obtain monitoring data of pumped storage units under power generation phase adjustment conditions; Inputting the monitoring data into a pre-trained technical water supply system fault identification model, and outputting a fault identification result for the technical water supply system based on the monitoring data by the pre-trained technical water supply system fault identification model; the pre-trained technical water supply system fault identification model is trained using a historical monitoring data set of the pumped storage unit under the power generation phase modulation condition; A fault handling text is generated based on the fault identification result; the fault handling text includes the fault handling operations that need to be performed on the technical water supply system.

2. The method according to claim 1, characterized in that Inputting the monitoring data into a pre-trained technical water supply system fault identification model, and outputting a fault identification result for the technical water supply system based on the monitoring data by the pre-trained technical water supply system fault identification model, includes: Preprocessing the monitoring data to obtain processed monitoring data; Inputting the processed monitoring data into a feature extraction network in a pre-trained technical water supply system fault identification model, and extracting state features of the technical water supply system through the feature extraction network; Based on the state characteristics, the fault identification result is output.

3. The method according to claim 2, characterized in that The feature extraction network includes a spatial feature extraction network and a temporal feature extraction network. The processed monitoring data is input into the feature extraction network of the pre-trained technical water supply system fault identification model, and the state features of the technical water supply system are extracted through the feature extraction network, including: Inputting the processed monitoring data into the spatial feature extraction network and the temporal feature extraction network respectively, extracting spatial features through the spatial feature extraction network, and extracting temporal features through the temporal feature extraction network; The spatial feature and the temporal feature are fused to obtain the state feature.

4. The method according to claim 1, wherein The method further comprises: Obtain historical monitoring data sets; Using the historical monitoring data set to train the technical water supply system fault identification model to be trained, to obtain a trained technical water supply system fault identification model; Using test data to perform model performance verification on the trained technical water supply system fault identification model to obtain a model performance verification result; In the case where the model performance verification result meets the preset conditions, the trained technical water supply system fault identification model is used as the pre-trained technical water supply system fault identification model; In the case that the model performance verification result does not meet the preset condition, return to the step of using the historical monitoring data set to train the technical water supply system fault identification model to be trained until the model performance verification result meets the preset condition.

5. The method according to claim 1, wherein Generating a fault handling text according to the fault identification result includes: determining the fault type according to the fault identification result; Obtaining at least one fault handling rule matching the fault type from a pre-built fault handling rule library; The fault handling text is generated according to each of the fault handling rules.

6. The method according to claim 1, characterized in that The obtaining of monitoring data of the pumped storage unit under the power generation phase adjustment condition includes: Obtain valve status parameter data, water pump parameter data and hydraulic parameter data; The monitoring data is generated based on the valve status parameter data, the water pump parameter data and the hydraulic parameter data.

7. A device for identifying faults in the technical water supply system of a pumped storage unit under power generation phase adjustment conditions, characterized in that: The device comprises: An acquisition module is used to obtain monitoring data of the pumped storage unit under power generation phase adjustment conditions; an input module, configured to input the monitoring data into a pre-trained technical water supply system fault identification model, and output a fault identification result for the technical water supply system based on the monitoring data by the pre-trained technical water supply system fault identification model; the pre-trained technical water supply system fault identification model is trained using a historical monitoring data set of the pumped-storage unit under the power generation phase modulation condition; A generation module is used to generate a fault handling text based on the fault identification result; the fault handling text includes the fault handling operations that need to be performed on the technical water supply system.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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