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- 저자 : Wu Zhongqiang (검색결과 2 건)
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Maximum Power Point Tracking for Photovoltaic System Based on IMVO Algorithm
Wu Zhongqiang,Cao Bilian,Hou Lincheng,Hu Xiaoyu,Ma Boyan 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.5
The power output curve of the photovoltaic (PV) array exhibits multi-peak characteristics under partial shading conditions, and the traditional control algorithm cannot track the maximum power point continuously and accurately, therefore, a global maximum power point tracking method is proposed based on the improved multi-verse optimization algorithm. Spiral update and adaptive compression factor are introduced to enhance the global search capability of algorithm; the travelling distance rate update method is changed, and the convergence speed of algorithm is accelerated, so the optimization ability of the algorithm is improved by the three aspects. The simulation results show that the improved multi-verse optimization algorithm can track the maximum power point continuously and stably under the three conditions that uniform irradiance, partial shading and variable irradiance, and the convergence time and convergence accuracy have been greatly improved, thus verifying the feasibility of the algorithm in the maximum power point tracking control.
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Microgrid Fault Diagnosis Based on Whale Algorithm Optimizing Extreme Learning Machine
Wu Zhongqiang,Lu Xueqin 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.3
A microgrid fault diagnosis method based on whale algorithm optimizing extreme learning machine (ELM) is proposed. Firstly, the three-phase fault voltage is analyzed by wavelet packet decomposition, and the feature vector composed of wavelet packet energy entropy is calculated as data samples. Then, a whale algorithm is used to optimize the extreme learning machine to establish a diagnostic model to identify and diagnose the fault type of microgrid. The whale algorithm has the characteristics of simple parameter setting, fast learning speed, and strong global optimization ability. The whale algorithm is used to optimize the input weights and hidden layer neuron thresholds of the extreme learning machine, which solves the problem that the random initialization of the input weights and hidden layer neuron thresholds easily afects the network performance, which can further improve the learning speed and generalization ability of the network, and beneft to global optimization. Simulation results show that compared with BP neural network, RBF neural network and ELM, the fault diagnosis model based on whale algorithm optimization extreme learning machine has faster learning speed, stronger generalization ability and higher recognition accuracy
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