Improved firefly algorithm–extended Kalman filter–least-square support-vector machine voltage sag monitoring and classification method based on edge computing
Aiming at the hardware reusability, multi-service carrying capacity, and computing resource limitations of edge devices, a light-weight voltage sag monitoring and classification method based on improved firefly algorithm optimization, extended Kalman filter, and least-square support-vector machine i...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/15501329221087055 |
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| _version_ | 1850174969034571776 |
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| author | Zhu Liu Xuesong Qiu Yonggui Wang Shuai Zhang Zhi Li |
| author_facet | Zhu Liu Xuesong Qiu Yonggui Wang Shuai Zhang Zhi Li |
| author_sort | Zhu Liu |
| collection | DOAJ |
| description | Aiming at the hardware reusability, multi-service carrying capacity, and computing resource limitations of edge devices, a light-weight voltage sag monitoring and classification method based on improved firefly algorithm optimization, extended Kalman filter, and least-square support-vector machine is proposed. The strategy of linearly decreasing inertia weight is introduced to optimize the state error of the extended Kalman filter algorithm and the measurement noise covariance matrix to achieve accurate monitoring of voltage sags. Extract characteristic quantities such as average value, duration of sag, minimum sag dispersion characteristics, number of sag phases, and flow direction of disturbance energy. As a model training data set, the least-square support-vector machine method optimized based on the improved firefly algorithm is used to create a multi-level classification model of voltage sag source to realize the classification of voltage sag sources. This method fully considers the influence of the limited resources of edge computing equipment on the algorithm, and effectively improves the use of computing resources by improving the optimization algorithm. Simulation and experimental results show that this method is suitable for edge computing equipment to monitor and distinguish voltage sags. |
| format | Article |
| id | doaj-art-d68491abb6ee487fb4e365cd1c133c23 |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2022-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-d68491abb6ee487fb4e365cd1c133c232025-08-20T02:19:33ZengWileyInternational Journal of Distributed Sensor Networks1550-14772022-03-011810.1177/15501329221087055Improved firefly algorithm–extended Kalman filter–least-square support-vector machine voltage sag monitoring and classification method based on edge computingZhu Liu0Xuesong Qiu1Yonggui Wang2Shuai Zhang3Zhi Li4State Grid Information & Telecommunication Group Co., Ltd., Beijing, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaState Grid Information & Telecommunication Group Co., Ltd., Beijing, ChinaState Grid Information & Telecommunication Group Co., Ltd., Beijing, ChinaState Grid Information & Telecommunication Group Co., Ltd., Beijing, ChinaAiming at the hardware reusability, multi-service carrying capacity, and computing resource limitations of edge devices, a light-weight voltage sag monitoring and classification method based on improved firefly algorithm optimization, extended Kalman filter, and least-square support-vector machine is proposed. The strategy of linearly decreasing inertia weight is introduced to optimize the state error of the extended Kalman filter algorithm and the measurement noise covariance matrix to achieve accurate monitoring of voltage sags. Extract characteristic quantities such as average value, duration of sag, minimum sag dispersion characteristics, number of sag phases, and flow direction of disturbance energy. As a model training data set, the least-square support-vector machine method optimized based on the improved firefly algorithm is used to create a multi-level classification model of voltage sag source to realize the classification of voltage sag sources. This method fully considers the influence of the limited resources of edge computing equipment on the algorithm, and effectively improves the use of computing resources by improving the optimization algorithm. Simulation and experimental results show that this method is suitable for edge computing equipment to monitor and distinguish voltage sags.https://doi.org/10.1177/15501329221087055 |
| spellingShingle | Zhu Liu Xuesong Qiu Yonggui Wang Shuai Zhang Zhi Li Improved firefly algorithm–extended Kalman filter–least-square support-vector machine voltage sag monitoring and classification method based on edge computing International Journal of Distributed Sensor Networks |
| title | Improved firefly algorithm–extended Kalman filter–least-square support-vector machine voltage sag monitoring and classification method based on edge computing |
| title_full | Improved firefly algorithm–extended Kalman filter–least-square support-vector machine voltage sag monitoring and classification method based on edge computing |
| title_fullStr | Improved firefly algorithm–extended Kalman filter–least-square support-vector machine voltage sag monitoring and classification method based on edge computing |
| title_full_unstemmed | Improved firefly algorithm–extended Kalman filter–least-square support-vector machine voltage sag monitoring and classification method based on edge computing |
| title_short | Improved firefly algorithm–extended Kalman filter–least-square support-vector machine voltage sag monitoring and classification method based on edge computing |
| title_sort | improved firefly algorithm extended kalman filter least square support vector machine voltage sag monitoring and classification method based on edge computing |
| url | https://doi.org/10.1177/15501329221087055 |
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