反向传播神经网络及其改进算法用于光伏MPPT的研究

研究了反向传播神经网络(BPNN)、粒子群优化反向传播神经网络(PSO-BPNN)、萤火虫优化反向传播神经网络(FA-BPNN),以及斐波那契优化反向传播神经网络(IM-FSM-BPNN)用于光伏组件在局部阴影下最大功率点的跟踪，以及上述算法在太阳能无人机中飞行光伏发电跟踪。结果首先表明，局部阴影下，IM-FSM-BPNN功率预测精度最低，跟踪时间最长，鲁棒性差，原因是控制参数多，依赖参数初始值。FA-BPNN功率预测精度最高且鲁棒性较好，原因是在训练过程中有效避免梯度消失的问题。其次，在样本数据量增加和太阳能无人机的应用中，发现FA-BPNN的预测效果好和IM-FSM-BPNN的局限性。最后，探讨了参数变化对预测结果的影响。IM-FSM-BPNN、PSO-BPNN和FA-BPNN较BPNN更适用于多样本数据预测，IM-FSM-BPNN相较于其他3种算法更适用于较小的学习率，4种算法的平均跟踪时间和功率平均预测精度随隐含层节点数震荡。

Research on BackPropagation Neural Network and Its Improved Algorithm for MPPT on PV

This paper studies the back-propagation neural network (BPNN), the particle swarm optimization back-propagation neural network (PSO-BPNN), the firefly-optimized back-propagation neural network (FA-BPNN), and the Fibonacci-optimized back-propagation neural network (IM-FSM-BPNN) for the tracking of the maximum power point of photovoltaic modules under local shadows, and the above-mentioned algorithms fly photovoltaic power generation tracking in solar drones. The results show that: First of all, under partial shading, the power prediction accuracy of IM-FSM-BPNN is the lowest, the tracking time is the longest, and the robustness is poor, the reason is the large number of control parameters and the dependence on the initial values of the parameters. The power pre-diction accuracy of FA-BPNN is the highest and the robustness is better, because it can effectively avoid the problem of gradient disappearance during the training process. Secondly, In the increase of sample data and the application of solar drones, it is found that the prediction effect of the FA-BPNN is good and the limitations of the IM-FSM-BPNN . Finally, the influence of parameter changes on the prediction results is discussed. IM-FSM-BPNN, PSO-BPNN and FA-BPNN are more suitable for multi-sample data prediction than BPNN, and IM-FSM-BPNN is more suitable for smaller learning rates than the other three algorithms. The average tracking time and power average prediction accuracy of the four algorithms fluctuate with the number of hidden layer nodes.