Oil-immersed transformer state evaluation method

Abstract

The invention discloses an oil-immersed transformer state evaluation method, and relates to the technical field of power transformers. The method comprises the following steps: inputting test data of a quick response item into a preset state factor calculation model for calculation to obtain a quick response item state factor of the oil-immersed transformer, and determining a first operation state of the oil-immersed transformer; performing weighted summation on the same type of test data in the test data of the low-speed reaction item and / or the test data of the maintenance item to obtain a low-speed reaction item state factor of the oil-immersed transformer, and determining a second operation state of the oil-immersed transformer; determining a fault diagnosis result of the oil-immersed transformer by using a preset fault detection network; and verifying the first operation state based on the second operation state and the fault diagnosis result, and determining a target operation state of the oil-immersed transformer. According to the invention, the operation state evaluation precision of the oil-immersed transformer can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of power transformers, and in particular to a method for evaluating the state of an oil-immersed transformer. Background Art

[0002] Oil-immersed power transformers can effectively transmit large amounts of electrical energy, providing a stable and reliable power supply for industrial production and commercial activities. The operating status of power transformers is very important for safe and efficient transmission of electrical energy, so it is necessary to obtain the operating status of transformers through operation and maintenance projects.

[0003] At present, when evaluating the operating status of an oil-immersed transformer, the evaluation personnel usually conduct the status evaluation based on the operating parameters of the oil-immersed transformer. However, this method is greatly influenced by the subjective factors of the evaluation personnel, and it is difficult to accurately evaluate the operating status of the oil-immersed transformer. Summary of the invention

[0004] In view of this, the present application provides a method for evaluating the state of an oil-immersed transformer, the main purpose of which is to improve the accuracy of evaluating the operating state of the oil-immersed transformer.

[0005] According to a first aspect of the present application, a method for evaluating the state of an oil-immersed transformer is provided, the method comprising: After testing the rapid response items involved in the operation and maintenance of oil-immersed transformers, Input the test data of the quick response project into a preset state factor calculation model for calculation to obtain the quick response project state factor of the oil-immersed transformer; Based on the quick response item state factor, determining a first operating state of the oil-immersed transformer; After testing the slow-response items and / or maintenance items involved in the operation and maintenance detection process of the oil-immersed transformer, weighted summing is performed on the test data of the slow-response items and / or the test data of the maintenance items of the same type to obtain the slow-response item state factor of the oil-immersed transformer; Determining a second operating state of the oil-immersed transformer based on the slow-reaction item state factor; At the same time, the test data of the slow-response items and / or the test data of the maintenance items are input into a preset fault detection network for fault detection, so as to obtain a fault diagnosis result of the oil-immersed transformer and fault location information when a fault exists, wherein when the fault diagnosis result indicates that a fault exists, the weights of the slow-response items and / or maintenance items involved in the fault diagnosis result are updated; Based on the second operating state and the fault diagnosis result, the first operating state is verified, and according to the verification result, a target operating state of the oil-immersed transformer is determined.

[0006] According to a second aspect of the present application, a device for evaluating the state of an oil-immersed transformer is provided, the device comprising: The first calculation unit is used to test the quick response items involved in the operation and maintenance of the oil-immersed transformer. Input the test data of the quick response project into a preset state factor calculation model for calculation to obtain the quick response project state factor of the oil-immersed transformer; A determination unit, configured to determine a first operating state of the oil-immersed transformer based on the quick response item state factor; The second calculation unit is used for, after testing the slow-response items and / or maintenance items involved in the operation and maintenance detection process of the oil-immersed transformer, performing weighted summation on the test data of the slow-response items and / or the test data of the maintenance items of the same type to obtain the slow-response item state factor of the oil-immersed transformer; The determining unit is further used to determine the second operating state of the oil-immersed transformer based on the slow-reaction item state factor; A detection unit, used for simultaneously inputting the test data of the slow-response items and / or the test data of the maintenance items into a preset fault detection network for fault detection, and obtaining a fault diagnosis result of the oil-immersed transformer, and fault location information when a fault exists, wherein when the fault diagnosis result is that a fault exists, the weights of the slow-response items and / or maintenance items involved in the fault diagnosis result are updated; A verification unit is used to verify the first operating state based on the second operating state and the fault diagnosis result, and determine the target operating state of the oil-immersed transformer according to the verification result.

[0007] According to a third aspect of the present application, a storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned oil-immersed transformer state assessment method is implemented.

[0008] According to a fourth aspect of the present application, an electronic device is provided, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the above-mentioned oil-immersed transformer state assessment method when executing the program.

[0009] By means of the above technical scheme, the present application provides a method for evaluating the state of an oil-immersed transformer, which can avoid human participation in the evaluation of the operating state of the oil-immersed transformer by using a preset state factor calculation model, calculating the state factor of the fast response item, and determining the first operating state of the oil-immersed transformer, thereby improving the accuracy of the evaluation of the operating state of the oil-immersed transformer. In addition, the present application can correct the first operating state of the oil-immersed transformer by calculating the state factor of the slow response item and determining the second operating state of the oil-immersed transformer, thereby further improving the accuracy of the evaluation of the operating state of the oil-immersed transformer. Furthermore, the present application can correct the first operating state of the oil-immersed transformer based on the fault diagnosis result by using a preset fault detection network to determine whether the oil-immersed transformer has a fault, and the fault location information when there is a fault, thereby ensuring the accuracy of the evaluation result of the operating state of the oil-immersed transformer. Furthermore, the present application can also update the weights of the slow response items and / or maintenance items involved in the fault diagnosis result when there is a fault, thereby improving the calculation accuracy of the state factor of the slow response item, and further ensuring the accuracy of the evaluation result of the operating state of the oil-immersed transformer.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A schematic diagram of a flow chart of a method for evaluating the state of an oil-immersed transformer provided in an embodiment of the present application is shown; Figure 2 A schematic flow chart of another oil-immersed transformer condition assessment method provided in an embodiment of the present application is shown; Figure 3 A schematic diagram of the model establishment process provided in an embodiment of the present application is shown; Figure 4 A schematic diagram of the overall evaluation process provided by an embodiment of the present application is shown; Figure 5 A schematic structural diagram of an oil-immersed transformer condition assessment device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0012] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0013] The existing technology is greatly influenced by the subjective factors of the evaluators, making it difficult to accurately evaluate the operating status of the oil-immersed transformer.

[0014] In order to solve the above problems, an embodiment of the present invention provides a method for evaluating the state of an oil-immersed transformer, such as Figure 1 As shown, the method includes: Step 101: After testing the quick response items involved in the operation and maintenance inspection process of the oil-immersed transformer, input the test data of the quick response items into a preset state factor calculation model for calculation to obtain the quick response item state factor of the oil-immersed transformer.

[0015] Among them, the operation and maintenance inspection items of oil-immersed transformers include dielectric loss of insulating oil, water content in oil, oil breakdown voltage, oil acid value detection, oil particle detection, furfural content in oil, dissolved gas detection in oil (including detection of H2, CH4, C2H6, C2H4, C2H2, CO, CO2 and O2 content), insulation resistance test, partial discharge test, dielectric loss test, core grounding current, core insulation resistance, winding DC resistance difference, oil-paper insulation polymerization degree, isothermal relaxation current detection, load rate, etc. According to the length of the test time interval of the operation and maintenance project, the project is divided into fast response project, slow response project and maintenance project. Among them, according to the implementation cycle of the power company's operation and maintenance inspection projects, the interval of dissolved gas detection in oil is short, which belongs to the rapid response project; the test interval of insulating oil dielectric loss, oil acid value detection, oil particulate matter detection, oil furfural content determination, insulation resistance test, partial discharge test, dielectric loss test, core grounding current, core insulation resistance, winding DC resistance difference and other inspection projects is long, which belongs to the slow response project; because the oil-paper insulation polymerization degree and isothermal relaxation current detection projects cannot be carried out when the transformer is in operation, they can only be carried out after the transformer is out of operation, which belongs to the maintenance project. The test data of the rapid response project includes the gas content of H2, CH4, C2H6, C2H4, C2H2, CO, CO2 and O2. In addition, the preset state factor calculation model can be specifically a neural network model.

[0016] The embodiment of the present invention is mainly applicable to the scenario of evaluating the operating status of an oil-immersed transformer. The execution subject of the embodiment of the present invention is a device or equipment capable of evaluating the operating status of an oil-immersed transformer, which can be specifically set on the server side.

[0017] Since the detection cycle of the quick response project is short, the detection frequency of the quick response project is high in the daily operation of the oil-immersed transformer, such as once every hour. In order to avoid human participation in the operation status evaluation of the oil-immersed transformer, thereby affecting the accuracy of the operation status evaluation of the oil-immersed transformer, the embodiment of the present invention calculates the quick response project state factor of the oil-immersed transformer based on the test data of the quick response project and the preset state factor calculation model, so as to determine the first operation state of the oil-immersed transformer based on the quick response project state factor. For this process, step 101 specifically includes: inputting the dissolved gas content in the oil into the preset neural network model for calculation to obtain the quick response project state factor of the oil-immersed transformer. Among them, the preset neural network model includes an input layer, a first hidden layer, a second hidden layer and an output layer.

[0018] When calculating the quick response item status factor of the oil-immersed transformer, the dissolved gas content in the oil is input into the first hidden layer through the input layer for calculation to obtain the output vector of the first hidden layer; the output vector of the first hidden layer is input into the second hidden layer for calculation to obtain the output vector of the second hidden layer; the output vector of the second hidden layer is input into the output layer for calculation to obtain the quick response item status factor of the oil-immersed transformer.

[0019] Specifically, the dissolved gas content in oil is taken as the input vector x = [x1, x2, x3, x4, …,x i ], where x i Represents the content of a certain gas, for example, the concentration of H2, CH4, C2H6, C2H4, etc. Taking the activation function sigmoid function as an example, the input vector is input to the first hidden layer through the input layer for calculation, and the output vector of the first hidden layer is: h 1 =sigmoid(W 1 x+b 1 ), where x is the input vector and h 1 is the output vector of the first hidden layer, W 1 is the weight matrix of the first hidden layer, b 1 is the bias vector of the first hidden layer, and sigmoid() is the activation function.

[0020] Then the output vector of the first hidden layer is input into the second hidden layer for calculation, and the output vector of the second hidden layer is obtained as: 2 =sigmoid(W 2 h 1 +b 2 ), where h 1 is the output vector of the first hidden layer, h2 is the output vector of the second hidden layer, W 2 is the weight matrix of the second hidden layer, b 2 is the bias vector of the second hidden layer, and sigmoid() is the activation function.

[0021] Then the output vector of the second hidden layer is calculated through the output layer, and the final output is y=sigmoid(W 3 h 2 +b 3 ), where h 2 is the output vector of the second hidden layer, W 3 is the weight matrix of the third hidden layer, y is the quick response item status factor, b 3 is the bias vector of the output layer, and sigmoid() is the activation function.

[0022] It should be noted that the sizes of the weight matrices and bias vectors of the first hidden layer, the second hidden layer, and the output layer depend on the number of elements in each layer.

[0023] Step 102: Determine a first operating state of the oil-immersed transformer based on the quick response item state factor.

[0024] The first operating state of the oil-immersed transformer includes a good operating state, aging, severe aging, etc.

[0025] For the embodiment of the present invention, in order to determine the first operating state of the oil-immersed transformer, step 102 specifically includes: determining the first evaluation level of the oil-immersed transformer based on the state interval of the quick response item state factor; and determining the first operating state of the oil-immersed transformer according to the first evaluation level.

[0026] Specifically, the value range of the quick response project status factor can be pre-divided into multiple state intervals, different state intervals correspond to different evaluation levels, and different evaluation levels correspond to different operating states. For example, the value range of the quick response project status factor is 0-100 points, and 0-100 points are divided into multiple state intervals, which are above 80 points, 40 points to 80 points, and below 40 points. If the calculated quick response project status factor is in the state interval above 80 points, then the corresponding evaluation level is determined to be A, indicating that the operating state of the oil-immersed transformer is good and there is no aging problem; if the calculated quick response project status factor is in the state interval of 40 points to 80 points, then the corresponding evaluation level is determined to be B, indicating that the oil-immersed transformer is aged and needs the attention of the operation and maintenance personnel; if the calculated quick response project status factor is in the state interval above 80 points, then the corresponding evaluation level is determined to be C, indicating that the oil-immersed transformer is seriously aged and needs to be stopped for maintenance immediately.

[0027] It should be noted that the settings of the status interval, evaluation level and operating status can be set according to actual business needs, and the embodiment of the present invention does not make specific limitations here.

[0028] Step 103: After testing the slow-response items and / or maintenance items involved in the operation and maintenance detection process of the oil-immersed transformer, weighted summation is performed on the test data of the slow-response items and / or the test data of the maintenance items of the same type to obtain a slow-response item status factor of the oil-immersed transformer.

[0029] For the embodiment of the present invention, since the detection cycle of the slow-reaction items and / or maintenance items is relatively long, the detection frequency of the slow-reaction items and / or maintenance items in the daily operation of the oil-immersed transformer is relatively low, such as once every 3 months, half a year or a year. In addition, each item in the slow-reaction items and / or maintenance items is usually not detected on the same day, so the measurement data of the same batch of tests, that is, the same type of measurement data, can be weighted and summed to obtain the slow-reaction item state factor.

[0030] Taking the insulation resistance test as an example, the weighted sum of each insulation resistance value tested is used to obtain the slow response item status factor. The specific formula is as follows:

[0031] in, is the slow-reaction item status factor, For the i The weight corresponding to each insulation resistance test item is: For the detection i The insulation resistance value.

[0032] Step 104: Determine a second operating state of the oil-immersed transformer based on the slow-reaction item state factor.

[0033] The first operating state of the oil-immersed transformer includes a good operating state, aging, severe aging, etc.

[0034] For the embodiment of the present invention, in order to determine the first operating state of the oil-immersed transformer, step 102 specifically includes: determining the second evaluation level of the oil-immersed transformer based on the state interval of the slow-response item state factor; and determining the second operating state of the oil-immersed transformer according to the second evaluation level.

[0035] Specifically, the value range of the slow-reaction project status factor can be divided into multiple state intervals in advance, and different state intervals correspond to different evaluation levels, and different evaluation levels correspond to different operating states. For example, the value range of the slow-reaction project status factor is 0-100 points, and 0-100 points are divided into multiple state intervals, which are above 80 points, 40 points to 80 points, and below 40 points. If the calculated slow-reaction project status factor is in the state interval above 80 points, then the corresponding evaluation level is determined to be A, indicating that the operating state of the oil-immersed transformer is good and there is no aging problem; if the calculated slow-reaction project status factor is in the state interval of 40 points to 80 points, then the corresponding evaluation level is determined to be B, indicating that the oil-immersed transformer is aged and needs the attention of the operation and maintenance personnel; if the calculated slow-reaction project status factor is in the state interval above 80 points, then the corresponding evaluation level is determined to be C, indicating that the oil-immersed transformer is seriously aged and needs to be stopped for maintenance immediately.

[0036] It should be noted that the settings of the status interval, evaluation level and operating status can be set according to actual business needs, and the embodiment of the present invention does not make specific limitations here.

[0037] Step 105: simultaneously input the test data of the slow response item and / or the test data of the maintenance item into a preset fault detection network for fault detection, and obtain the fault diagnosis result of the oil-immersed transformer and the fault location information when a fault exists.

[0038] Wherein, each node in the preset fault detection network is composed of determination conditions. In addition, when the fault diagnosis result is that a fault exists, the weights of the slow response items and / or maintenance items involved in the fault diagnosis result are updated.

[0039] For slow response items and / or maintenance items, the present invention can also be implemented to determine whether there is a fault in the oil-immersed transformer and where the fault occurs through test data of the slow response items and / or test data of the maintenance items and a preset fault detection network.

[0040] Specifically, the test data of the slow-reaction project and / or the test data of the maintenance project can be input into the preset fault detection network, and it is determined in turn whether the test data of the slow-reaction project and / or the test data of the maintenance project meet the determination conditions of each node in the network. If the determination condition of a certain node is not met, it means that there is a fault in the oil-immersed transformer. At the same time, since the manifestation of the oil-immersed transformer is different for different faults, when there is a fault in the oil-immersed transformer, the fault cause and fault location of the oil-immersed transformer can be determined according to the determination conditions that are not met in the preset fault detection network.

[0041] Step 106: Based on the second operating state and the fault diagnosis result, verify the first operating state, and determine the target operating state of the oil-immersed transformer according to the verification result.

[0042] For the embodiments of the present invention, since there may be errors in monitoring the content of each gas dissolved in the oil, resulting in inaccurate results of the first operating state, it is necessary to verify the first operating state based on the second operating state and fault diagnosis results obtained based on the test data of the slow response project and / or the test data of the maintenance project.

[0043] Specifically, if the first operating state is consistent with the second operating state or the fault diagnosis result, it means that the evaluated first operating state is accurate, and the first operating state is directly determined to be the target operating state of the oil-immersed transformer; if the first operating state is inconsistent with the second operating state and the fault diagnosis result, it means that the evaluated first operating state is inaccurate, and the second operating state is determined to be the target operating state of the oil-immersed transformer.

[0044] The embodiment of the present invention provides a method for evaluating the state of an oil-immersed transformer. By using a preset state factor calculation model, the state factor of a fast-response item is calculated, and the first operating state of the oil-immersed transformer is determined. This method can avoid human participation in the evaluation of the operating state of the oil-immersed transformer, thereby improving the accuracy of the evaluation of the operating state of the oil-immersed transformer. In addition, the embodiment of the present invention can correct the first operating state of the oil-immersed transformer by calculating the state factor of a slow-response item and determining the second operating state of the oil-immersed transformer, thereby further improving the accuracy of the evaluation of the operating state of the oil-immersed transformer. Furthermore, the embodiment of the present invention can correct the first operating state of the oil-immersed transformer based on the fault diagnosis result by using a preset fault detection network to determine whether the oil-immersed transformer has a fault and the fault location information when the fault exists, thereby ensuring the accuracy of the evaluation result of the operating state of the oil-immersed transformer. Furthermore, the embodiment of the present invention can also update the weights of the slow-response items and / or maintenance items involved in the fault diagnosis result when a fault exists, thereby improving the calculation accuracy of the state factor of the slow-response item and further ensuring the accuracy of the evaluation result of the operating state of the oil-immersed transformer.

[0045] Further, as a refinement and extension of the specific implementation of the above embodiment, in order to fully illustrate the implementation of this embodiment, this embodiment also provides another oil-immersed transformer state assessment method, such as Figure 2 As shown, the method includes: Step 201: After testing the quick response items involved in the operation and maintenance inspection process of the oil-immersed transformer, input the test data of the quick response items into a preset state factor calculation model for calculation to obtain the quick response item state factor of the oil-immersed transformer.

[0046] Among them, the preset state factor calculation model is specifically a neural network model.

[0047] According to the embodiment of the present invention, before using the preset state factor calculation model to calculate the rapid response item state factor of the oil-immersed transformer, it is necessary to train the preset state factor calculation model, such as Figure 3 Specifically, it is necessary to collect historical test data of the rapid response project, construct a sample training set, train the neural network model based on the sample training set, and obtain a preset state factor calculation model. Among them, the loss function in the training process is the mean square error function, which is as follows:

[0048] in, L is the mean square error, Representative i The rapid response project status factor of the model output corresponding to the sample data, Representative i The real quick response project status factor corresponding to the sample data.

[0049] When training a neural network model based on the mean square error loss function, the gradient descent method or its variants (such as the Adam optimizer) are used to update the weights and biases to minimize the loss function. The weight and bias update process can be expressed as: , ,in, η is the learning rate. The training process continues until the loss drops below the specified value, at which point and That is the structural parameter of the rapid response project status factor model.

[0050] In actual calculation, the test data of the rapid response project is first normalized, and then the normalized test data is input into the preset state factor calculation model for calculation to obtain the rapid response project state factor. ,in, and Calculate the model weights and bias parameters for the preset state factors, x It is the dissolved gas content in oil.

[0051] Step 202: Determine a first operating state of the oil-immersed transformer based on the quick response item state factor.

[0052] In the embodiment of the present invention, after the quick response item state factor is calculated, the first operating state of the oil-immersed transformer is determined based on the quick response item state factor. The specific process is exactly the same as step 102 and will not be repeated here.

[0053] Furthermore, the embodiment of the present invention can also set corresponding warning intervals and warning values ​​according to the rapid response project status factors obtained from historical operation and maintenance data. If the system enters the warning zone from the safe zone, it will send a warning message. If it stays in the warning zone for a long time, it can remind the operation and maintenance personnel to check possible faults or perform optimization operations. If the warning value is reached or exceeded, power outage is required for maintenance.

[0054] Step 203: After testing the slow-response items and / or maintenance items involved in the operation and maintenance detection process of the oil-immersed transformer, based on the weights corresponding to the slow-response items and / or the weights corresponding to the maintenance items, weighted summation of the same type of test data in the test data of the slow-response items and / or the test data of the maintenance items is performed to obtain a slow-response item status factor of the oil-immersed transformer.

[0055] Taking the insulation resistance test as an example, the weighted sum of each insulation resistance value tested is used to obtain the slow response item status factor. The specific formula is as follows:

[0056] in, is the slow-reaction item status factor, For the i The weight corresponding to each insulation resistance test item is: For the detection i The insulation resistance value.

[0057] Step 204: Determine a second operating state of the oil-immersed transformer based on the slow-reaction item state factor.

[0058] In the embodiment of the present invention, after the slow-reaction item state factor is calculated, the second operating state of the oil-immersed transformer is determined based on the slow-reaction item state factor. The specific process is exactly the same as step 104 and will not be repeated here.

[0059] Step 205: simultaneously input the test data of the slow response item and / or the test data of the maintenance item into a preset fault detection network for fault detection, and obtain the fault diagnosis result of the oil-immersed transformer and the fault location information when a fault exists.

[0060] Wherein, when the fault diagnosis result is that a fault exists, the weights of the slow response items and / or maintenance items involved in the fault diagnosis result are updated. In addition, the preset fault detection network includes a first detection network and a second detection network, the first detection network can be specifically an Alpha network, and the second detection network can be specifically a Beta network.

[0061] In the embodiment of the present invention, expert knowledge, experience and decision logic can be collected for slow response projects and / or maintenance projects and used as original rules. The original rules are then converted into a form that can be processed by a computer, such as rule representation, semantic network, framework representation or logic representation, to form a knowledge base, such as Figure 3 Then, an inference engine (preset fault detection network) and an interpreter are constructed based on the knowledge base, wherein the interpreter is used to parse and analyze the corresponding test data and judgment conditions when a fault occurs, so as to locate the fault.

[0062] When fault diagnosis is performed using a preset fault detection network, step 205 specifically includes: inputting the test data of the slow-response project and the test data of the maintenance project into the first detection network to obtain a first condition determination result corresponding to each node in the first detection network, wherein each node in the first detection network corresponds to a single determination condition; inputting the first condition determination result into the second detection network to obtain a second condition determination result corresponding to each node in the second detection network, wherein each node in the second detection network corresponds to at least two determination conditions; if the condition determination result corresponding to any one node in the second condition determination result is abnormal, it is determined that there is a fault in the oil-immersed transformer, and the test data of the slow-response project and / or the test data of the maintenance project involved in the abnormal condition determination result, as well as the determination condition are added to the conflict set; based on the test data of the slow-response project and / or the test data of the maintenance project in the conflict set, as well as the determination condition, the fault location information of the oil-immersed transformer is analyzed.

[0063] Specifically, the test data of the slow response project and the test data of the maintenance project are input into the Alpha network, and each node is traversed in turn to determine a single condition, and the first condition determination result of each node in the Alpha network is obtained and stored. Then, the first condition determination result is input into the Beta network, and each node is traversed in turn to determine the combination of multiple conditions. When determining the combination, the Beta network node can use the intermediate results passed down from the Alpha network to directly determine the condition combination based on each intermediate result, thereby reducing the amount of calculation and improving the efficiency of fault diagnosis.

[0064] If any node in the Beta network evaluates that the test data of the slow-response project and / or the test data of the maintenance project do not meet a certain judgment condition, it is determined that the oil-immersed transformer is faulty, and the judgment condition of the node and the test data of the related slow-response project and / or the test data of the maintenance project are added to the conflict set, such as Figure 4 As shown. Then, the interpreter is used to parse and analyze the test data of the slow response items and / or the test data of the maintenance items in the conflict set, as well as the judgment conditions, so as to determine the fault location information of the oil-immersed transformer, that is, what the specific fault is and where the fault occurs. Because the oil-immersed transformer manifests itself in different forms for different faults, such as for fault A, The test data of slow response item a and the test data of slow response item b do not meet the judgment condition; for fault B, the test data of slow response item b, the test data of slow response item c and the test data of slow response item d do not meet the judgment condition. Therefore, the interpreter can locate the specific location and fault type of the oil-immersed transformer according to the abnormal test data and judgment conditions in the conflict set, thereby playing a role in assisting and guiding decision-making.

[0065] In some embodiments, the weights of the slow-response items and / or maintenance items in the conflict set may also be updated. The method for this process includes: updating the weights of the slow-response items and / or maintenance items in the conflict set to obtain updated weights; based on the updated weights, weighted summing the test data of the slow-response items and / or the test data of the maintenance items of the same type to obtain a recalculated slow-response item state factor. Taking insulation resistance testing as an example, the specific formula is as follows:

[0066] in, is the recalculated slow-reacting item status factor, For the i The updated weight of each insulation resistance test item, For the detection i The insulation resistance value.

[0067] The embodiment of the present invention can improve the calculation accuracy of the state factor of the slow-response item by updating the weights of the slow-response item and / or the maintenance item in the conflict set, thereby improving the operating state assessment accuracy of the oil-immersed transformer.

[0068] Step 206: If the first operating state is different from the second operating state and the fault diagnosis result, it is determined that the first operating state is inaccurate, and based on the second operating state and the fault diagnosis result, the target operating state of the oil-immersed transformer is determined, and the preset state factor calculation model is updated.

[0069] For the embodiment of the present invention, if the first operating state is inconsistent with the second operating state and the fault diagnosis result, it means that the evaluated first operating state is inaccurate. At this time, the target operating state of the oil-immersed transformer can be comprehensively determined based on the second operating state and the fault diagnosis result. For example, the second operating state is that the oil-immersed transformer is seriously aged, and the fault detection result is that the oil-immersed transformer has a fault. At this time, the detection results of the two are consistent, and the second operating state can be determined as the target operating state of the oil-immersed transformer. In addition, the preset state factor calculation model can be continuously updated and trained to improve the calculation accuracy of the state factor of the rapid response project.

[0070] Another oil-immersed transformer state assessment method provided by an embodiment of the present invention can avoid human participation in the oil-immersed transformer operation state assessment by using a preset state factor calculation model to calculate the state factor of the fast response item and determine the first operation state of the oil-immersed transformer, thereby improving the accuracy of the oil-immersed transformer operation state assessment. In addition, by calculating the slow response item state factor and determining the second operation state of the oil-immersed transformer, the embodiment of the present invention can correct the first operation state of the oil-immersed transformer, thereby further improving the accuracy of the oil-immersed transformer operation state assessment. Furthermore, by using a preset fault detection network to determine whether the oil-immersed transformer has a fault and the fault location information when the fault exists, the embodiment of the present invention can correct the first operation state of the oil-immersed transformer based on the fault diagnosis result, thereby ensuring the accuracy of the oil-immersed transformer operation state assessment result. Further, the embodiment of the present invention can also update the weights of the slow response items and / or maintenance items involved in the fault diagnosis result when a fault exists, thereby improving the calculation accuracy of the slow response item state factor and further ensuring the accuracy of the oil-immersed transformer operation state assessment result.

[0071] Further, as Figure 1 and Figure 2 The specific implementation of the method shown in the embodiment provides an oil-immersed transformer state assessment device, such as Figure 5 As shown, the device includes: a first calculation unit 31, a determination unit 32, a second calculation unit 33, a detection unit 34 and a verification unit 35.

[0072] The first calculation unit 31 can be used to input the test data of the quick response items involved in the operation and maintenance inspection process of the oil-immersed transformer into a preset state factor calculation model for calculation after testing the quick response items involved in the operation and maintenance inspection of the oil-immersed transformer, so as to obtain the quick response item state factor of the oil-immersed transformer.

[0073] The determining unit 32 may be configured to determine the first operating state of the oil-immersed transformer based on the quick response item state factor.

[0074] The second calculation unit 33 can be used to perform weighted summation on the test data of the slow response items and / or the test data of the maintenance items after testing the slow response items and / or maintenance items involved in the operation and maintenance detection process of the oil-immersed transformer, so as to obtain the slow response item status factor of the oil-immersed transformer.

[0075] The determining unit 32 may also be configured to determine a second operating state of the oil-immersed transformer based on the slow-reaction item state factor.

[0076] The detection unit 34 can be used to simultaneously input the test data of the slow-response items and / or the test data of the maintenance items into a preset fault detection network for fault detection, thereby obtaining a fault diagnosis result of the oil-immersed transformer and fault location information when a fault exists, wherein when the fault diagnosis result is that a fault exists, the weights of the slow-response items and / or maintenance items involved in the fault diagnosis result are updated.

[0077] The verification unit 35 may be configured to verify the first operating state based on the second operating state and the fault diagnosis result, and determine a target operating state of the oil-immersed transformer according to the verification result.

[0078] In some embodiments, the first calculation unit 31 can be specifically used to input the dissolved gas content in the oil into the preset neural network model for calculation to obtain the fast response item state factor of the oil-immersed transformer.

[0079] In some embodiments, when the preset neural network model includes an input layer, a first hidden layer, a second hidden layer and an output layer, the first calculation unit 31 can also be specifically used to input the dissolved gas content in the oil through the input layer into the first hidden layer for calculation to obtain the output vector of the first hidden layer; input the output vector of the first hidden layer into the second hidden layer for calculation to obtain the output vector of the second hidden layer; input the output vector of the second hidden layer into the output layer for calculation to obtain the rapid response project state factor of the oil-immersed transformer.

[0080] In some embodiments, the determination unit 32 can be specifically used to determine a first evaluation level of the oil-immersed transformer based on the state interval of the quick response item state factor; and determine a first operating state of the oil-immersed transformer according to the first evaluation level.

[0081] In some embodiments, the determination unit 32 can also be specifically used to determine a second evaluation level of the oil-immersed transformer based on the state interval of the slow-reaction item state factor; and determine a second operating state of the oil-immersed transformer according to the second evaluation level.

[0082] In some embodiments, the second calculation unit 33 can be specifically used to weighted sum the test data of the slow response project and / or the test data of the maintenance project of the same type based on the weight corresponding to the slow response project and / or the weight corresponding to the maintenance project, so as to obtain the slow response project status factor of the oil-immersed transformer.

[0083] In some embodiments, when the preset fault detection network includes a first detection network and a second detection network, the detection unit 34 includes: a detection module, a determination module and an analysis module.

[0084] The detection module can be used to input the test data of the slow response project and the test data of the maintenance project into the first detection network to obtain the first condition judgment result corresponding to each node in the first detection network, wherein each node in the first detection network corresponds to a single judgment condition.

[0085] The detection module can also be used to input the first condition determination result into the second detection network to obtain the second condition determination result corresponding to each node in the second detection network, wherein each node in the second detection network corresponds to at least two determination conditions.

[0086] The determination module can be used to determine that there is a fault in the oil-immersed transformer if the condition determination result corresponding to any node in the second condition determination result is abnormal, and add the test data of the slow response item and / or the test data of the maintenance item involved in the abnormal condition determination result, as well as the determination condition to the conflict set.

[0087] The analysis module may be used to analyze the fault location information of the oil-immersed transformer based on the test data of the slow-response items and / or the test data of the maintenance items in the conflict set, and the determination conditions.

[0088] In some embodiments, the verification unit 35 can be specifically used to determine that the first operating state is inaccurate if the first operating state is different from the second operating state and the fault diagnosis result, determine the target operating state of the oil-immersed transformer based on the second operating state and the fault diagnosis result, and update the preset state factor calculation model.

[0089] In some embodiments, the detection unit 34 further includes: an update module.

[0090] The updating module can be used to update the weights of the slow-response items and / or maintenance items in the conflict set to obtain updated weights; based on the updated weights, weighted summation is performed on the test data of the slow-response items and / or the test data of the maintenance items of the same type to obtain a recalculated slow-response item status factor.

[0091] It should be noted that for other corresponding descriptions of the functional units involved in the oil-immersed transformer state assessment device provided in this embodiment, reference can be made to Figure 1 and Figure 2 The corresponding description in will not be repeated here.

[0092] Based on the above Figure 1 and Figure 2 The method shown in the embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned Figure 1 and Figure 2 The oil-immersed transformer condition assessment method shown.

[0093] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), including a number of instructions for enabling an electronic device (which can be a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of the present application.

[0094] Based on the above Figure 1 and Figure 2 The method shown, and Figure 5 In order to achieve the above-mentioned purpose, the embodiment of the present application also provides an electronic device, which can be a personal computer, a tablet computer, a server, or other network equipment, etc. The device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figure 1 and Figure 2 The oil-immersed transformer condition assessment method shown.

[0095] Optionally, the above-mentioned physical device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0096] Those skilled in the art will appreciate that the above-mentioned physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different arrangements of components.

[0097] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the above-mentioned physical device, and supports the operation of the information processing program and other software and / or programs. The network communication module is used to realize the communication between the components inside the storage medium, and the communication with other hardware and software in the information processing physical device.

[0098] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or by hardware.

[0099] The embodiment of the present invention calculates the state factor of the fast response item by using the preset state factor calculation model, and determines the first operating state of the oil-immersed transformer, which can avoid human participation in the operating state evaluation of the oil-immersed transformer, thereby improving the operating state evaluation accuracy of the oil-immersed transformer. In addition, the embodiment of the present invention can correct the first operating state of the oil-immersed transformer by calculating the state factor of the slow response item and determining the second operating state of the oil-immersed transformer, thereby further improving the operating state evaluation accuracy of the oil-immersed transformer. Furthermore, the embodiment of the present invention determines whether the oil-immersed transformer has a fault and the fault location information when the fault exists by using the preset fault detection network, and can correct the first operating state of the oil-immersed transformer based on the fault diagnosis result, thereby ensuring the accuracy of the operating state evaluation result of the oil-immersed transformer. Further, the embodiment of the present invention can also update the weights of the slow response items and / or maintenance items involved in the fault diagnosis result when a fault exists, thereby improving the calculation accuracy of the slow response item state factor, and further ensuring the accuracy of the operating state evaluation result of the oil-immersed transformer.

[0100] Those skilled in the art will appreciate that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily necessary for implementing the present application. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the description of the implementation scenario, or can be changed accordingly and located in one or more devices different from the present implementation scenario. The modules of the above-mentioned implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0101] The above serial numbers of this application are only for description and do not represent the advantages and disadvantages of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of this application, but this application is not limited to them, and any changes that can be thought of by technicians in this field should fall within the scope of protection of this application.

Claims

1. A method for evaluating the state of an oil-immersed transformer, characterized in that: include: After testing the rapid response items involved in the operation and maintenance of oil-immersed transformers, Input the test data of the quick response project into a preset state factor calculation model for calculation to obtain the quick response project state factor of the oil-immersed transformer; Determining a first operating state of the oil-immersed transformer based on the quick response item state factor; After testing the slow-response items and / or maintenance items involved in the operation and maintenance detection process of the oil-immersed transformer, weighted summing is performed on the test data of the slow-response items and / or the test data of the maintenance items of the same type to obtain the slow-response item state factor of the oil-immersed transformer; Determining a second operating state of the oil-immersed transformer based on the slow-reaction item state factor; At the same time, the test data of the slow-response items and / or the test data of the maintenance items are input into a preset fault detection network for fault detection, so as to obtain a fault diagnosis result of the oil-immersed transformer and fault location information when a fault exists, wherein when the fault diagnosis result indicates that a fault exists, the weights of the slow-response items and / or maintenance items involved in the fault diagnosis result are updated; Based on the second operating state and the fault diagnosis result, the first operating state is verified, and according to the verification result, the target operating state of the oil-immersed transformer is determined.

2. The method according to claim 1, characterized in that The test data of the quick response project includes the content of dissolved gas in oil. The preset state factor calculation model is a preset neural network model. The test data of the quick response project is input into the preset state factor calculation model for calculation to obtain the quick response project state factor of the oil-immersed transformer, including: The dissolved gas content in the oil is input into the preset neural network model for calculation to obtain the rapid response project state factor of the oil-immersed transformer.

3. The method according to claim 2, characterized in that When the preset neural network model includes an input layer, a first hidden layer, a second hidden layer and an output layer, the dissolved gas content in the oil is input into the preset neural network model for calculation to obtain the rapid response item state factor of the oil-immersed transformer, including: Inputting the dissolved gas content in the oil into the first hidden layer through the input layer for calculation, and obtaining an output vector of the first hidden layer; Inputting the output vector of the first hidden layer into the second hidden layer for calculation to obtain the output vector of the second hidden layer; The output vector of the second hidden layer is input into the output layer for calculation to obtain the fast response item state factor of the oil-immersed transformer.

4. The method according to claim 1, characterized in that: Determining a first operating state of the oil-immersed transformer based on the quick response item state factor includes: Determining a first evaluation level of the oil-immersed transformer based on the state interval of the quick response item state factor; determining a first operating state of the oil-immersed transformer according to the first evaluation level; and / or Determining a second operating state of the oil-immersed transformer based on the slow-reaction item state factor includes: Determining a second evaluation level of the oil-immersed transformer based on the state interval of the slow-reaction item state factor; A second operating state of the oil-immersed transformer is determined according to the second evaluation level.

5. The method according to claim 1, characterized in that The test data of the slow-reaction item and / or the test data of the maintenance item of the same type are weighted summed to obtain the slow-reaction item state factor of the oil-immersed transformer, including: Based on the weight corresponding to the slow response item and / or the weight corresponding to the maintenance item, the test data of the slow response item and / or the test data of the maintenance item are weighted and summed to obtain the slow response item status factor of the oil-immersed transformer.

6. The method according to claim 1, characterized in that When the preset fault detection network includes a first detection network and a second detection network, the test data of the slow response item and / or the test data of the maintenance item are input into the preset fault detection network for fault detection to obtain the fault diagnosis result of the oil-immersed transformer and the fault location information when a fault exists, including: Input the test data of the slow-reaction project and the test data of the maintenance project into the first detection network to obtain the first condition determination result corresponding to each node in the first detection network, wherein each node in the first detection network corresponds to a single determination condition; Inputting the first condition determination result into the second detection network to obtain the second condition determination result corresponding to each node in the second detection network, wherein each node in the second detection network corresponds to at least two determination conditions; If the condition determination result corresponding to any node in the second condition determination result is abnormal, it is determined that the oil-immersed transformer has a fault, and the test data of the slow response item and / or the test data of the maintenance item involved in the abnormal condition determination result and the determination condition are added to the conflict set; Analyzing the fault location information of the oil-immersed transformer based on the test data of the slow-response items and / or the test data of the maintenance items in the conflict set and the determination conditions; and / or Based on the second operating state and the fault diagnosis result, verifying the first operating state, and determining a target operating state of the oil-immersed transformer according to the verification result, including: If the first operating state is different from the second operating state and the fault diagnosis result, it is determined that the first operating state is inaccurate, and based on the second operating state and the fault diagnosis result, the target operating state of the oil-immersed transformer is determined, and the preset state factor calculation model is updated.

7. The method according to claim 6, characterized in that The method further comprises: Updating the weights of the slow-response items and / or maintenance items in the conflict set to obtain updated weights; Based on the updated weights, weighted summation is performed on the test data of the slow-response item and / or the test data of the maintenance item of the same type to obtain a recalculated slow-response item state factor.

8. An oil-immersed transformer condition assessment device, characterized in that: include: A first calculation unit is used for, after testing the quick response items involved in the operation and maintenance detection process of the oil-immersed transformer, inputting the test data of the quick response items into a preset state factor calculation model for calculation to obtain the quick response item state factor of the oil-immersed transformer; A determination unit, configured to determine a first operating state of the oil-immersed transformer based on the quick response item state factor; The second calculation unit is used for, after testing the slow-response items and / or maintenance items involved in the operation and maintenance detection process of the oil-immersed transformer, performing weighted summation on the test data of the slow-response items and / or the test data of the maintenance items of the same type to obtain the slow-response item state factor of the oil-immersed transformer; The determining unit is further used to determine the second operating state of the oil-immersed transformer based on the slow-reaction item state factor; A detection unit, used for simultaneously inputting the test data of the slow-response items and / or the test data of the maintenance items into a preset fault detection network for fault detection, and obtaining a fault diagnosis result of the oil-immersed transformer, and fault location information when a fault exists, wherein when the fault diagnosis result is that a fault exists, the weights of the slow-response items and / or maintenance items involved in the fault diagnosis result are updated; A verification unit is used to verify the first operating state based on the second operating state and the fault diagnosis result, and determine the target operating state of the oil-immersed transformer according to the verification result.

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

10. An electronic device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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