基于功率预测的光伏组件阴影故障类型判定

为有效区分光伏组件中存在的软、硬性阴影故障,提出一种基于功率预测的光伏组件阴影故障类型判定方法。首先,该方法采用经验模态分解(EMD)方法对光照强度进行分解,挖掘其数据趋势项,为灰色模型(GM)预测提供应用基础;其次,根据改进人工鱼群算法优化灰色模型(IAFSA-GM)和滚动式数据更新模式对光伏组件的输出功率进行预测,从而判断出软、硬阴影故障类型;最后,在阴影类型判别的基础上,通过对光伏组件实际输出功率值和内部等效参数模型所得最大输出功率理论值之差进行分析,进一步诊断硬性阴影的严重程度。仿真和实验结果验证了上述方法的有效性和正确性。

Judgment on Shadow Fault Type for Photovoltaic Module Based on Power Prediction

In order to distinguish the soft and hard shadow types of PV modules effectively, a new method of judging the shadow types based on power prediction is proposed. Firstly, the empirical mode decomposition (EMD) is taken to decompose the light intensity and the trend data is excavated to provide the application fundamentals for gray model (GM). Then, according the IAFSA-GM and rolling data update mode to predict the output power of PV module, the shadow fault types can be determined. Finally, On the basis of shadow type identification, a novel method based on the internal equivalent parameters model and measured data is proposed to further distinguish the severity of rigid shadows. The effectiveness and correctness of the proposed method is tested by the simulation and experimental results.