Photovoltaic Maximum Power Point Tracking Algorithm Based on LSTM-FLC
Photovoltaic array features nonlinear performance,and its maximum power point(MPP)will shift with environmental changes.Although the maximum power point tracking(MPPT)algorithm is widely used to track and predict the MPP of photovoltaic systems,it still faces challenges such as low dynamic quality and poor control accuracy of fuzzy logic control(FLC).To solve the problems men-tioned,a photovoltaic maximum power point tracking algorithm based on long-short term memory-flC(LSTM-FLC)is proposed.Firstly,the LSTM network predicts the MPP voltage by a time series method based on the light intensity and temperature datasets.Secondly,the deviation between the predicted voltage and the photovoltaic array voltage,as well as its derivative,are used as the input of FLC,and thus FLC is used to direct adjust the duty cycle of the boost converter.At the same time,the maximum and minimum duty ratios are pre-set to prevent the switch from being normally turned on.Simulation verification is carried out using MATLAB/Simulink under four varia-ble atmospheric conditions.Experimental results show that compared with LSTM,conductance incremental method,and genetic algo-rithm,the proposed MPPT algorithm has good tracking performance,stable accuracy,and efficiency,and takes the advantages of smoother waveform and smaller amplitude.
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