A method for evaluating the health status of GIS equipment

Abstract

The present invention relates to a method for evaluating the health status of GIS equipment, and belongs to the field of feature extraction and analysis technology. The method first sets the scoring intervals for the four health statuses of the GIS equipment, and then generates a historical mixed map of the four health statuses of each component based on the historical parameters in the historical detection report of the GIS equipment. Then, the historical scores of the four health statuses of each component are obtained, and input into the CKAN model together with all the historical mixed maps of all components for training. Finally, the real-time parameter information of the component is collected to obtain a real-time mixed map, which is input into the trained ckan model. At this time, the ckan model can extract key features that are strongly correlated with the health status, and reconstruct the key feature mixed map of each key component. Finally, the similarity between the key feature mixed map of each component and its historical health status of normal and serious is calculated to obtain a real-time score, which is compared with the scoring interval to determine the health status of each component.

Description

Technical Field

[0001] The present invention relates to a method for evaluating the health status of GIS equipment, and belongs to the technical field of feature extraction and analysis (G06F18 / 20). Background Art

[0002] With the development of the national economy and industry and commerce, the demand for electricity across all sectors of society is increasing, necessitating the continuous upgrading of power transmission and transformation equipment. Currently, GIS equipment features a compact structure and a small footprint, making it particularly well-suited to the needs of today's new power systems. However, GIS equipment contains a variety of key components, such as disconnectors, grounding switches, circuit breakers, current transformers, and voltage transformers, which play a vital role in the safe operation of the power system. The failure modes of different key components vary in the degree of impact on equipment operation and the frequency of failures. Therefore, accurately and rationally assessing the health of GIS equipment is crucial to improving the safety of power grid operations. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to achieve accurate evaluation of the health status of GIS equipment.

[0004] The present invention proposes a technical solution to solve the above technical problems: a method for evaluating the health status of GIS equipment, comprising the following steps:

[0005] Step 1: Set the score ranges corresponding to the four health states of the GIS equipment as a whole and its key components as follows:

[0006] Normal [0, 0.25],

[0007] Note that [0.25, 0.50]

[0008] Abnormal (0.50, 0.75],

[0009] severe (0.75, 1.00];

[0010] Step 2: Collect historical electrical quantities, mechanical quantities, environmental quantities, and appearance images of each key component in four historical health states from the historical inspection reports of the GIS equipment;

[0011] Convert the historical electrical quantities, historical mechanical quantities, and historical environmental quantities of each key component in the four historical health states into electrical characteristic maps, mechanical characteristic maps, and environmental characteristic maps of each key component in the four historical health states;

[0012] The historical electrical characteristic maps, historical mechanical characteristic maps, historical environmental characteristic maps, and historical appearance images of each key component in the four historical health states are fused to generate a historical mixed map of each key component in the four historical health states;

[0013] Step 3: Use word2vec to obtain the historical scores of the four health states of all key components;

[0014] Step 4: The historical mixed graphs of all key components in the four historical health states obtained in step 2 and the historical scores of all key components in the four historical health states obtained in step 3 are input into the CKAN model for training;

[0015] Step 4.1: Set the initial hyperparameters of the CKAN model;

[0016] Step 4.2: The CKAN model extracts the first feature point of the historical mixed graph of each key component under the four historical health states based on the initial hyperparameters, and generates a predicted score for each key component under the four historical health states based on the extracted first feature point;

[0017] Step 4.3: Calculate the loss function value of the predicted score and historical score of the four historical health states of all key components according to formulas (1), (2), and (3)

[0018] (1);

[0019] In formula (1), m is the number of key components of GIS equipment; i is the key component;

[0020] It is the key component i history of the four health status of the historical score and;

[0021] is the predicted score of the four historical health states of key component i;

[0022] (2);

[0023] In formula (2), is the mean of the historical scores of the four health states of all key components;

[0024] (3);

[0025] In formula (3), =2;

[0026] Step 4.4: Use the SWO algorithm to iteratively update the initial hyperparameters, and repeat steps 4.2-4.3 using the new hyperparameters generated in each iteration as the initial hyperparameters;

[0027] Step 4.5: Stop the iteration after reaching the number of iterations. The hyperparameter with the smallest value is input into the CKAN model to complete the training;

[0028] Step 5: Collect real-time electrical quantities, real-time mechanical quantities, real-time environmental quantities, and real-time appearance images of all key components in the GIS equipment;

[0029] Convert the real-time electrical quantity, real-time mechanical quantity and real-time environmental quantity of each key component into a real-time electrical characteristic map, a real-time mechanical characteristic map and a real-time environmental characteristic map respectively;

[0030] The real-time electrical characteristic map, real-time mechanical characteristic map, real-time environmental characteristic map, and real-time appearance image of each key component are fused separately to obtain a real-time hybrid map of each key component;

[0031] Step 6: Input the real-time mixed maps of all key components into the CKAN model trained in step 4. The CKAN model constructs the key feature mixed maps of the corresponding key components based on the second feature points extracted from the real-time mixed maps of each key component. ;

[0032] Step 7: Calculate the real-time health score of each key component in the GIS equipment according to formula (4)

[0033] (4);

[0034] In formula (1), j is the health status, j=1,2;

[0035] When j=1, It is the historical mixed spectrum when the historical health status of key component i is normal;

[0036] When j=2, It is the historical mixed spectrum when the historical health status of key component i is severe;

[0037] is the key feature mixture map of key component i;

[0038] Real-time scoring of the health status of each key component Compare with the four scoring value intervals in step 1 to determine which health status each key component belongs to.

[0039] Further, after step 7, the following steps are performed:

[0040] Step 8: Calculate the proportion of each key component in the same health state according to equations (5) and (6).

[0041] (5);

[0042] (6);

[0043] Step 9: Calculate the information entropy of the key components of GIS equipment in normal and serious health states according to formula (7)

[0044] (7);

[0045] Step 10: Calculate the weights of the key components of GIS equipment in normal and serious health states according to formula (8)

[0046] (8);

[0047] Step 11: Calculate the overall score of GIS equipment according to formula (9)

[0048] (9);

[0049] Compare the overall score Z of the GIS equipment with the four score value intervals in step 1 to determine the overall health status of the GIS equipment.

[0050] Furthermore, the hyperparameters are learning rate, batch size and regularization parameter.

[0051] The beneficial effects of the present invention are as follows: since the CKAN model is trained using the historical mixed maps and historical scores of all key components in the four historical health states, the trained CKAN model extracts important features that are strongly correlated with the health state in the mixed maps, thereby obtaining more detailed and valuable information about key components, effectively reducing the impact of extreme data on the prediction results, reducing the prediction error, and improving the robustness and accuracy of the model; in addition, by fusing the maps generated by electrical quantities, environmental quantities and mechanical quantities with their respective appearance shooting images, the formed mixed map can fully retain all information of key parts. This comprehensive perspective makes the prediction more accurate and can better reflect the actual operating conditions of the equipment. Using the fused mixed map as data input can significantly improve the accuracy of the prediction model for the health status of key components. By analyzing this comprehensive information, the prediction model can more effectively identify potential problems and provide more reliable prediction scores. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The following further illustrates the GIS equipment health status evaluation method of the present invention with reference to the accompanying drawings.

[0053] Figure 1 It is a real-time electrical quantity characteristic map of the pot-type insulator of the GIS equipment in the embodiment of the method for evaluating the health status of the GIS equipment.

[0054] Figure 2 It is a real-time mechanical quantity characteristic map of the pot-type insulator of the GIS equipment in the embodiment of the GIS equipment health status evaluation method.

[0055] Figure 3 It is a real-time environmental quantity characteristic map of the pot-type insulator of the GIS equipment in the embodiment of the GIS equipment health status evaluation method.

[0056] Figure 4 It is a real-time mixed map of the pot insulator of the GIS equipment in the embodiment of the method for evaluating the health status of the GIS equipment.

[0057] Figure 5 It is a mixed map of key features of the pot insulator of the GIS equipment in the embodiment of the method for evaluating the health status of the GIS equipment.

[0058] Figure 6 This is the ckan model structure in the embodiment of the GIS equipment health status evaluation method. DETAILED DESCRIPTION

[0059] Example 1

[0060] A method for evaluating the health status of a GIS device in this embodiment includes the following steps:

[0061] Step 1: Set the score ranges corresponding to the four health states of the GIS equipment as a whole and its key components as follows:

[0062] Normal [0, 0.25],

[0063] Note that [0.25, 0.50]

[0064] Abnormal (0.50, 0.75],

[0065] severe (0.75, 1.00].

[0066] Step 2: Collect historical electrical quantities, mechanical quantities, environmental quantities, and appearance images of each key component in four historical health states from the historical inspection reports of the GIS equipment;

[0067] Convert the historical electrical quantities, historical mechanical quantities, and historical environmental quantities of each key component in the four historical health states into electrical characteristic maps, mechanical characteristic maps, and environmental characteristic maps of each key component in the four historical health states;

[0068] This embodiment uses the existing technology Gram angle field to convert the extracted electrical quantity, mechanical quantity and environmental quantity into characteristic spectra respectively;

[0069] The historical electrical characteristic maps, historical mechanical characteristic maps, historical environmental characteristic maps, and historical appearance images of each key component in the four historical health states are fused to generate a historical mixed map of each key component in the four historical health states;

[0070] This embodiment adopts the existing technology of Co-attention of Multi-view (CAMV) to fuse the three historical feature maps with the historical appearance images to construct a historical hybrid map.

[0071] Step 3: Use word2vec to obtain the historical scores of the four health states of all key components;

[0072] Word2Vec is an existing technology that maps words into a vector space. It can capture the semantic relationship between words, map each health status name into a vector, and then map the vector of the health status name to a specific historical score.

[0073] Step 4: The historical mixed graphs of all key components in the four historical health states obtained in step 2 and the historical scores of all key components in the four historical health states obtained in step 3 are input into the CKAN model for training;

[0074] like Figure 6 As shown in Figure 1, the CKAN model is an existing convolutional neural network model for graph processing. It aims to improve feature extraction by aligning convolution kernels. It optimizes the alignment of convolution kernels by optimizing the CKAN model hyperparameters to better capture key features in the graph. It reduces spatial features to smaller dimensions, then flattens the feature map, converting it into a one-dimensional feature vector that is mapped to specific historical ratings.

[0075] Step 4.1: Set the initial hyperparameters of the CKAN model; the initial hyperparameters are learning rate, batch size, and regularization parameter;

[0076] Step 4.2: The CKAN model extracts the first feature point of the historical mixed graph of each key component under the four historical health states based on the initial hyperparameters, and generates a predicted score for each key component under the four historical health states based on the extracted first feature point;

[0077] Step 4.3: Calculate the loss function value of the predicted score and historical score of the four historical health states of all key components according to formulas (1), (2), and (3)

[0078] (1);

[0079] In formula (1), m is the number of key components of GIS equipment; i is the key component;

[0080] It is the key component i history of the four health status of the historical score and;

[0081] is the predicted score of the four historical health states of key component i;

[0082] (2);

[0083] In formula (2), is the mean of the historical scores of the four health states of all key components;

[0084] (3);

[0085] In formula (3), =2;

[0086] Step 4.4: Use the SWO algorithm to iteratively update the initial hyperparameters, and repeat steps 4.2-4.3 using the new hyperparameters generated in each iteration as the initial hyperparameters;

[0087] Step 4.5: Stop the iteration after reaching the number of iterations. The hyperparameters with the smallest value are input into the CKAN model to complete the training.

[0088] Step 5: Collect real-time electrical quantities, real-time mechanical quantities, real-time environmental quantities, and real-time appearance images of all key components in the GIS equipment;

[0089] Convert the real-time electrical quantity, real-time mechanical quantity and real-time environmental quantity of each key component into a real-time electrical characteristic map, a real-time mechanical characteristic map and a real-time environmental characteristic map respectively;

[0090] In this embodiment, the three real-time characteristic maps of one of the key components of the GIS equipment (pot insulator) are as follows: Figure 1 、 2 , 3 shown;

[0091] The real-time electrical characteristic map, real-time mechanical characteristic map, real-time environmental characteristic map, and real-time appearance image of each key component are fused separately to obtain a real-time hybrid map of each key component;

[0092] In this embodiment, the real-time mixed map of one of the key components of the GIS equipment (pot insulator) is as follows: Figure 4 shown.

[0093] Step 6: Input the real-time mixed maps of all key components into the CKAN model trained in step 4. The CKAN model constructs the key feature mixed maps of the corresponding key components based on the second feature points extracted from the real-time mixed maps of each key component. ;

[0094] In the embodiment, the key feature hybrid map of one of the key components of GIS equipment (pot insulator) is as follows: Figure 5 As shown;

[0095] Step 7: Calculate the real-time health score of each key component in the GIS equipment according to formula (4)

[0096] (4);

[0097] In formula (1), j is the health status, j=1,2;

[0098] When j=1, It is the historical mixed spectrum when the historical health status of key component i is normal;

[0099] When j=2, It is the historical mixed spectrum when the historical health status of key component i is severe;

[0100] is the key feature mixture map of key component i;

[0101] Real-time scoring of the health status of each key component Compare with the four scoring value intervals in step 1 to determine the health status of each key component;

[0102] Real-time scoring of the health status of each key component in this embodiment The health status is shown in Table 1 below.

[0103] Key components Real-time scoring Health status breaker 0.22 normal Closing resistance 0.21 normal Isolating switch 0.19 normal Grounding switch 0.18 normal Current transformer 0.17 normal lightning arrester 0.16 normal voltage transformer 0.15 normal Basin insulator 0.19 normal Mechanical structure 0.15 normal

[0104] Table 1

[0105] Example 2

[0106] The GIS equipment health status assessment method of this embodiment is basically the same as that of embodiment 1, except that after step 7, the following steps are performed:

[0107] Step 8: Calculate the proportion of each key component in the same health state according to equations (5) and (6).

[0108] (5);

[0109] (6).

[0110] Step 9: Calculate the information entropy of the key components of GIS equipment in normal and serious health states according to formula (7)

[0111] (7).

[0112] Step 10: Calculate the weights of the key components of GIS equipment in normal and serious health states according to formula (8)

[0113] (8).

[0114] Step 11: Calculate the overall score of GIS equipment according to formula (9)

[0115] (9);

[0116] Compare the overall score Z of the GIS equipment with the four score value intervals in step 1 to determine the overall health status of the GIS equipment;

[0117] The overall score of GIS equipment calculated in this example =0.19, the health status is healthy.

[0118] The above description is only a preferred embodiment of the present invention, but the present invention is not limited thereto. All equivalent replacements or equivalent changes of the concepts and technical solutions according to the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating the health status of GIS equipment, characterized in that The steps include: Step 1: Set the score ranges corresponding to the four health states of the GIS equipment as a whole and its key components as follows: Normal [0, 0.25], Note that [0.25, 0.50] Abnormal (0.50, 0.75], severe (0.75, 1.00]; Step 2: Collect historical electrical quantities, mechanical quantities, environmental quantities, and appearance images of each key component in four historical health states from the historical inspection reports of the GIS equipment; Convert the historical electrical quantities, historical mechanical quantities, and historical environmental quantities of each key component in the four historical health states into electrical characteristic maps, mechanical characteristic maps, and environmental characteristic maps of each key component in the four historical health states; The historical electrical characteristic maps, historical mechanical characteristic maps, historical environmental characteristic maps, and historical appearance images of each key component in the four historical health states are fused to generate a historical mixed map of each key component in the four historical health states; Step 3: Use word2vec to obtain the historical scores of the four health states of all key components; Step 4: The historical mixed graphs of all key components in the four historical health states obtained in step 2 and the historical scores of all key components in the four historical health states obtained in step 3 are input into the CKAN model for training; Step 4.1: Set the initial hyperparameters of the CKAN model; Step 4.2: The CKAN model extracts the first feature point of the historical mixed graph of each key component under the four historical health states based on the initial hyperparameters, and generates a predicted score for each key component under the four historical health states based on the extracted first feature point; Step 4.3: Calculate the loss function value fitness of the predicted scores and historical scores of the four historical health states of all key components according to equations (1), (2), and (3) In formula (1), m is the number of key components of GIS equipment; i is the key component; y i It is the key component i history of the four health status of the historical score and; is the predicted score of the four historical health states of key component i; In formula (2), is the mean of the historical scores of the four health states of all key components; fitness=(1-R 2 )+λMAPE (3); In formula (3), λ = 2; Step 4.4: Use the SWO algorithm to iteratively update the initial hyperparameters, and repeat steps 4.2-4.3 using the new hyperparameters generated in each iteration as the initial hyperparameters; Step 4.5: Stop the iteration after reaching the number of iterations, and input the hyperparameter with the minimum fitness value into the CKAN model to complete the training; Step 5: Collect real-time electrical quantities, real-time mechanical quantities, real-time environmental quantities, and real-time appearance images of all key components in the GIS equipment; Convert the real-time electrical quantity, real-time mechanical quantity and real-time environmental quantity of each key component into a real-time electrical characteristic map, a real-time mechanical characteristic map and a real-time environmental characteristic map respectively; The real-time electrical characteristic map, real-time mechanical characteristic map, real-time environmental characteristic map, and real-time appearance image of each key component are fused separately to obtain a real-time hybrid map of each key component; Step 6: Input the real-time mixed maps of all key components into the CKAN model trained in step 4. The CKAN model constructs the key feature mixed maps of the corresponding key components based on the second feature points extracted from the real-time mixed maps of each key component. Step 7: Calculate the real-time health score cs of each key component in the GIS equipment according to formula (4): i In formula (1), j is the health status, j = 1, 2; When j=1, It is the historical mixed graph when the historical health status of key component i is normal; When j=2, It is the historical mixed graph when the historical health status of key component i is severe; is the key feature mixture map of key component i; Real-time score of the health status of each key component i Compare with the four scoring value intervals in step 1 to determine which health status each key component belongs to.

2. The method for evaluating the health status of GIS equipment according to claim 1, characterized in that: After step 7, the following steps are performed: Step 8: Calculate the proportion p of each key component in the same health state according to equations (5) and (6): ij Step 9: Calculate the information entropy e of the key components of GIS equipment in normal and serious health states according to formula (7) j Step 10: Calculate the weights W of the key components of GIS equipment in normal and serious health states according to formula (8) j Step 11: Calculate the overall score Z of GIS equipment according to formula (9) Compare the overall score Z of the GIS equipment with the four score value intervals in step 1 to determine the overall health status of the GIS equipment.

3. The method for evaluating the health status of GIS equipment according to claim 1, characterized in that: The hyperparameters are learning rate, batch size and regularization parameter.

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