Realizing the Improvement of the Reliability and Efficiency of Intelligent Electricity Inspection: IAOA-BP Algorithm for Anomaly Detection
Anomaly detection can improve the service level of the grid, effectively save human resources and reduce the operating cost of a power company. In this study, an improved arithmetic optimization-backpropagation (IAOA-BP) neural algorithm for an anomaly detection model was proposed for electricity in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/7/3021 |
| _version_ | 1797608016492625920 |
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| author | Yuping Zou Rui Wu Xuesong Tian Hua Li |
| author_facet | Yuping Zou Rui Wu Xuesong Tian Hua Li |
| author_sort | Yuping Zou |
| collection | DOAJ |
| description | Anomaly detection can improve the service level of the grid, effectively save human resources and reduce the operating cost of a power company. In this study, an improved arithmetic optimization-backpropagation (IAOA-BP) neural algorithm for an anomaly detection model was proposed for electricity inspection. The dynamic boundary strategy of the cosine control factor and the differential evolution operator are introduced into the arithmetic optimization algorithm (AOA) to obtain the improved arithmetic optimization algorithm (IAOA). The algorithm performance test proves that the IAOA has better solving ability and stability compared with the AOA, WOA, SCA, SOA and SSA. The IAOA was subsequently used to obtain the optimal weights and thresholds for BP. In the experimental phase, the proposed model is validated with electricity data provided by a power company. The results reveal that the overall determination accuracy using the IAOA-BP algorithm remains above 96%, and compared with other algorithms, the IAOA-BP has a higher accuracy and can meet the requirements grid supervision. The power load data anomaly detection model proposed in this study has some implications that might suggest how power companies can promote grid business model transformation, improve economic efficiency, enhance management and improve service quality. |
| first_indexed | 2024-03-11T05:38:41Z |
| format | Article |
| id | doaj.art-4d34f24b9d324df28aa9923c35ad7dc5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T05:38:41Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-4d34f24b9d324df28aa9923c35ad7dc52023-11-17T16:36:21ZengMDPI AGEnergies1996-10732023-03-01167302110.3390/en16073021Realizing the Improvement of the Reliability and Efficiency of Intelligent Electricity Inspection: IAOA-BP Algorithm for Anomaly DetectionYuping Zou0Rui Wu1Xuesong Tian2Hua Li3State Grid Tianjin Marketing Service Center, Tianjin 300200, ChinaState Grid Tianjin Marketing Service Center, Tianjin 300200, ChinaState Grid Tianjin Marketing Service Center, Tianjin 300200, ChinaState Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, ChinaAnomaly detection can improve the service level of the grid, effectively save human resources and reduce the operating cost of a power company. In this study, an improved arithmetic optimization-backpropagation (IAOA-BP) neural algorithm for an anomaly detection model was proposed for electricity inspection. The dynamic boundary strategy of the cosine control factor and the differential evolution operator are introduced into the arithmetic optimization algorithm (AOA) to obtain the improved arithmetic optimization algorithm (IAOA). The algorithm performance test proves that the IAOA has better solving ability and stability compared with the AOA, WOA, SCA, SOA and SSA. The IAOA was subsequently used to obtain the optimal weights and thresholds for BP. In the experimental phase, the proposed model is validated with electricity data provided by a power company. The results reveal that the overall determination accuracy using the IAOA-BP algorithm remains above 96%, and compared with other algorithms, the IAOA-BP has a higher accuracy and can meet the requirements grid supervision. The power load data anomaly detection model proposed in this study has some implications that might suggest how power companies can promote grid business model transformation, improve economic efficiency, enhance management and improve service quality.https://www.mdpi.com/1996-1073/16/7/3021electricity inspectionanomaly detectionimproved arithmetic optimization algorithmbackpropagation neural network |
| spellingShingle | Yuping Zou Rui Wu Xuesong Tian Hua Li Realizing the Improvement of the Reliability and Efficiency of Intelligent Electricity Inspection: IAOA-BP Algorithm for Anomaly Detection Energies electricity inspection anomaly detection improved arithmetic optimization algorithm backpropagation neural network |
| title | Realizing the Improvement of the Reliability and Efficiency of Intelligent Electricity Inspection: IAOA-BP Algorithm for Anomaly Detection |
| title_full | Realizing the Improvement of the Reliability and Efficiency of Intelligent Electricity Inspection: IAOA-BP Algorithm for Anomaly Detection |
| title_fullStr | Realizing the Improvement of the Reliability and Efficiency of Intelligent Electricity Inspection: IAOA-BP Algorithm for Anomaly Detection |
| title_full_unstemmed | Realizing the Improvement of the Reliability and Efficiency of Intelligent Electricity Inspection: IAOA-BP Algorithm for Anomaly Detection |
| title_short | Realizing the Improvement of the Reliability and Efficiency of Intelligent Electricity Inspection: IAOA-BP Algorithm for Anomaly Detection |
| title_sort | realizing the improvement of the reliability and efficiency of intelligent electricity inspection iaoa bp algorithm for anomaly detection |
| topic | electricity inspection anomaly detection improved arithmetic optimization algorithm backpropagation neural network |
| url | https://www.mdpi.com/1996-1073/16/7/3021 |
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