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| Parameter Identification Model of Photovoltaic Module Based on Improved Manta Ray Optimization Algorithm |
| JIAN Xian-zhong1,WANG Peng1,WANG Ru-zhi2 |
1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
2. School of Materials Science and Engineering, Beijing University of Technology, Beijing 100020, China |
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Abstract In order to solve the problems of low parameter identification accuracy and poor stability in the current photovoltaic module model, a three-diode photovoltaic module parameter identification model (RLMRFO-TDM) based on manta ray foraging optimization algorithm and the refraction learning mechanism is proposed. The model integrates the differential evolution mechanism into the population updating link of MRFO algorithm, improves the local exploration ability of MRFO algorithm and speeds up the convergence speed of MRFO algorithm. The introduction of refraction learning mechanism improves the randomness of MRFO algorithm, the discreteness of population in the search area and the global search ability of MRFO algorithm. The benchmark function is used to verify the effectiveness of RLMRFO algorithm. The data sets of STP6-120/36 and STM6-40/36 photovoltaic modules are used to test the performance of parameter identification of RLMRFO-TDM model. Compared with other models, RLMRFO-TDM model has the best identification accuracy, stability and convergence speed.
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Received: 25 February 2022
Published: 13 January 2023
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2023, Vol. 44 
