基于改进FOA优化BP神经网络算法的光伏系统MPPT研究

针对基于BP神经网络的光伏系统MPPT策略在光照强度突变时存在较大误差的问题,提出了一种改进的果蝇优化算法用于BP神经网络的权值和阈值优化,并建立了基于IFOA-BP神经网络算法的光伏系统MPPT控制的仿真模型。测试和仿真结果表明,IFOA的收敛速度和求解精度较改进前均有明显提升;IFOA优化后的BP神经网络收敛速度加快,预测误差减少;较之于电导增量法,IFOA-BP神经网络的MPPT策略在稳态条件下能明显抑制功率波动,在外界条件发生突变时,能迅速准确地追踪到最大功率点,具有良好的稳态精度和动态特性。

Research on the photovoltaic system MPPT based on IFOA-BP neural network algorithm

When the BP neural network is adopted to predict the voltage at the maximum power point, there is a big error if the light intensity changes drastically. Aiming at this problem, a novel improved fruit fly optimization algorithm(IFOA) determining the optimal BP neural network parameters (weight and threshold) is proposed, and a simulation model of the photovoltaic system MPPT control strategy based on the IFOA-BP neural network algorithm was established. The test and simulation results show that, IFOA has a great advantage in search speed and accuracy than FOA; IFOA-BP neural network can effectively increases the convergence speed and reduces the prediction error; compared with the incremental conductance(INC) method, the proposed photovoltaic system MPPT control algorithm based on IFOA-BP neural network could suppress the oscillation around the maximum power point(MPP) under steady-state conditions and track down the MPP quickly and accurately when light intensity and temperature change drastically, which verifies the stability, precision and rapidity of the proposed MPPT method.