The Research and Design of Power Quality Monitoring System Based on ARM and DSP

Yang, Ling; Wei, Jia; Kang, Yuru; Lu, Xihong; Weifeng, Cai

doi:10.1109/imccc.2013.152

Cited by 2 publications

(1 citation statement)

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“…As distributed hardware devices based on edge computing carry multiple functional modules, the computing resources allocated to voltage sag application software are limited. [25][26][27] The existing algorithm is not suitable for most of edge devices, only the voltage monitoring function can be realized. Therefore, with the increasing number of edge devices deployed on the distribution network side, the study of a light-weight voltage sag source identification method suitable for edge devices has become an urgent problem to be solved.…”

Section: Introductionmentioning

confidence: 99%

Improved firefly algorithm–extended Kalman filter–least-square support-vector machine voltage sag monitoring and classification method based on edge computing

Liu

Qiu

Wang

et al. 2022

International Journal of Distributed Sensor Networks

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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.

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Section: Introductionmentioning

confidence: 99%