A comprehensive performance evaluation algorithm for substation secondary equipment: An improved analytic hierarchy process entropy weight and learning vector quantization neural network approach
Abstract This paper introduces a comprehensive performance evaluation algorithm explicitly designed for secondary equipment in substations, specifically targeting the relay protection system. In contrast to the current evaluation systems, this novel method navigates the complex internal interconnect...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | The Journal of Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/tje2.12347 |
| _version_ | 1797347544179671040 |
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| author | Wei Wang Jianfei Zhang Sai Wang Xuewei Chen |
| author_facet | Wei Wang Jianfei Zhang Sai Wang Xuewei Chen |
| author_sort | Wei Wang |
| collection | DOAJ |
| description | Abstract This paper introduces a comprehensive performance evaluation algorithm explicitly designed for secondary equipment in substations, specifically targeting the relay protection system. In contrast to the current evaluation systems, this novel method navigates the complex internal interconnections and mechanisms inherent within secondary system equipment. Such complications have previously impeded the accuracy and breadth of evaluations, thereby limiting the degree of precision and innovation attainable within substations. The proposed approach effectively integrates the improved Analytic Hierarchy Process entropy weight (IAHP‐EW) method with the Learning Vector Quantization (LVQ) neural network. Initially, the IAHP‐EW method identified the comprehensive evaluation indicators and their corresponding weights for relay protection devices. Following weight allocation, these evaluation indicators are scrutinized and computed utilizing the multivariate regression analysis algorithm, resulting in performance evaluation outcomes for the relay protection system. These outcomes are subsequently classified and utilized in training the LVQ neural network, promoting the network's capacity to autonomously evaluate the performance status of the relay protection system. To corroborate the viability and effectiveness of this proposed performance evaluation and prediction algorithm, empirical operating data from a local substation is used. The results suggest a significant improvement in the evaluation accuracy of secondary equipment performance, indicating potential for practical application and a valuable contribution to the field through the introduction of a novel approach to performance assessment of substation relay protection systems. |
| first_indexed | 2024-03-08T11:50:18Z |
| format | Article |
| id | doaj.art-5273cb91860d48d69b09adfd4a1e31e4 |
| institution | Directory Open Access Journal |
| issn | 2051-3305 |
| language | English |
| last_indexed | 2024-03-08T11:50:18Z |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Journal of Engineering |
| spelling | doaj.art-5273cb91860d48d69b09adfd4a1e31e42024-01-24T13:52:30ZengWileyThe Journal of Engineering2051-33052024-01-0120241n/an/a10.1049/tje2.12347A comprehensive performance evaluation algorithm for substation secondary equipment: An improved analytic hierarchy process entropy weight and learning vector quantization neural network approachWei Wang0Jianfei Zhang1Sai Wang2Xuewei Chen3Tangshan Power Supply Company State Grid Jibei Electric Power Co., Ltd Tangshan ChinaTangshan Power Supply Company State Grid Jibei Electric Power Co., Ltd Tangshan ChinaTangshan Power Supply Company State Grid Jibei Electric Power Co., Ltd Tangshan ChinaTangshan Power Supply Company State Grid Jibei Electric Power Co., Ltd Tangshan ChinaAbstract This paper introduces a comprehensive performance evaluation algorithm explicitly designed for secondary equipment in substations, specifically targeting the relay protection system. In contrast to the current evaluation systems, this novel method navigates the complex internal interconnections and mechanisms inherent within secondary system equipment. Such complications have previously impeded the accuracy and breadth of evaluations, thereby limiting the degree of precision and innovation attainable within substations. The proposed approach effectively integrates the improved Analytic Hierarchy Process entropy weight (IAHP‐EW) method with the Learning Vector Quantization (LVQ) neural network. Initially, the IAHP‐EW method identified the comprehensive evaluation indicators and their corresponding weights for relay protection devices. Following weight allocation, these evaluation indicators are scrutinized and computed utilizing the multivariate regression analysis algorithm, resulting in performance evaluation outcomes for the relay protection system. These outcomes are subsequently classified and utilized in training the LVQ neural network, promoting the network's capacity to autonomously evaluate the performance status of the relay protection system. To corroborate the viability and effectiveness of this proposed performance evaluation and prediction algorithm, empirical operating data from a local substation is used. The results suggest a significant improvement in the evaluation accuracy of secondary equipment performance, indicating potential for practical application and a valuable contribution to the field through the introduction of a novel approach to performance assessment of substation relay protection systems.https://doi.org/10.1049/tje2.12347distribution networksneural network |
| spellingShingle | Wei Wang Jianfei Zhang Sai Wang Xuewei Chen A comprehensive performance evaluation algorithm for substation secondary equipment: An improved analytic hierarchy process entropy weight and learning vector quantization neural network approach The Journal of Engineering distribution networks neural network |
| title | A comprehensive performance evaluation algorithm for substation secondary equipment: An improved analytic hierarchy process entropy weight and learning vector quantization neural network approach |
| title_full | A comprehensive performance evaluation algorithm for substation secondary equipment: An improved analytic hierarchy process entropy weight and learning vector quantization neural network approach |
| title_fullStr | A comprehensive performance evaluation algorithm for substation secondary equipment: An improved analytic hierarchy process entropy weight and learning vector quantization neural network approach |
| title_full_unstemmed | A comprehensive performance evaluation algorithm for substation secondary equipment: An improved analytic hierarchy process entropy weight and learning vector quantization neural network approach |
| title_short | A comprehensive performance evaluation algorithm for substation secondary equipment: An improved analytic hierarchy process entropy weight and learning vector quantization neural network approach |
| title_sort | comprehensive performance evaluation algorithm for substation secondary equipment an improved analytic hierarchy process entropy weight and learning vector quantization neural network approach |
| topic | distribution networks neural network |
| url | https://doi.org/10.1049/tje2.12347 |
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