Research on the Method of Optimizing Adaptive Control for Grid Connection of Photovoltaic Energy Storage Based on RBF Neural Network
Conventional photovoltaic energy storage grid connected adaptive control mainly adopts voltage dynamic ad-justment to achieve adaptive control,ignoring the impact of grid connected current waveform deviation on the control results,resulting in a large overshoot of the control results.Therefore,a method for optimizing adaptive control of photovoltaic energy storage grid connection based on RBF neural network is proposed.Based on the analysis of the output characteristics of the equivalent circuit for photovoltaic energy storage and grid connection,an equivalent cir-cuit connection architecture based on RBF neural network is established.The energy storage action mode is identi-fied,and the harmonic compensation value of the grid connection current is calculated based on the identification classification results.The compensated inner loop current is analyzed,and energy demand parameters are introduced to adaptively optimize the grid connection output power control strategy.The experimental results show that the con-trol results obtained after the application of the proposed method exhibit a low overshoot,only 1.8％,and the control effect is excellent,meeting the practical application requirements of photovoltaic energy storage and grid connection.
photovoltaic energy storage and grid connectionRBF neural networkadaptive controloptimize controlcontrol methodsgrid connection control
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维普
