基于改进Elman网络的光伏电池最大功率跟踪

通过对光伏电源系统的特性及最大功率点跟踪原理的深入分析,提出了一种用OIF-Elman网络算法来实现太阳能电池最大功率点跟踪的控制方法。该方法将太阳能电池和Cuk斩波电路作为一个整体,直接根据检测到的电路中电压、电流的变化,通过神经网络算法来控制Cuk电路的占空比,从而实现最大功率点的跟踪,并且OIF-Elman网络不仅计入了隐层节点的反馈,还考虑输出层节点的反馈,提高了网络泛化能力和预测精度。实验结果表明,该系统在日照强度、环境温度变化时仍能够快速、准确地跟踪到太阳能电池的最大功率点,并具有较好的稳定性。

Maximum Power Point Tracing of Photovoltaic Cells with OIF-Elman Network

A control method of maximun power point tracing(MPPT) based on output-input feedback (OIF)-Elman neural network algorithm is proposed,by the analysis of photovoltaic power system's characteristics and MPPT principle.It takes photovoltaic(PV) module and Cuk converter as a whole.It directly detects voltage and current of curcit and control the duty of the Cuk converter by the neural network algorithm.Compared with convertional Elman network, OIF-Elman network takes into account the hidden nodes feedback and the output feedback so as to obtain more information from limited sampling spots.The method makes the system very simple.The experimental results show that the proposed method can track MPP quickly,exactly and steadily,irrespective the large scale change of irradiation intensity and circumstance temperature.