基于随机共振方法增强光伏直流故障电弧检测特征的研究

Research on Feature Enhancement of DC Arc Fault Detection in Photovoltaic Systems Based on Stochastic Resonance

光伏直流系统中存在复杂多样的系统噪声干扰,使得故障电弧特征难以有效提取,因而增强故障电弧特征信息对准确检测故障电弧至关重要。为此,搭建含多种源荷、线路阻抗等元件的光伏直流故障电弧实验平台,研究随机共振方法对不同直流系统拓扑结构下故障电弧检测特征的增强效果。不同拓扑结构的故障电弧特征增强均有其独立的最优参数组合,蚁群算法寻找最优参数相较于传统的正交试验方法更具快速性及准确性,最优参数组合下随机共振处理后的检测特征可以更加有效地区分故障电弧与正常状态。通过对实验数据的计算与比较,验证了随机共振方法对不同拓扑结构故障电弧特征增强的普适性,最终基于支持向量机方法构建了直流故障电弧检测算法,得到较高的检测准确率。在故障电弧检测环节引入随机共振方法,可有效增强电弧检测特征的有效信息,保证高准确率的同时,降低故障电弧检测算法的设计难度,有利于高效、快速、精准地检测直流故障电弧。

There are complex and diverse system noise interferences in photovoltaic DC systems, which makes it difficult to effectively extract the arc fault characteristics.Therefore, enhancing the arc fault characteristic is very important to accurately detect the arc fault. This paper built a photovoltaic DC arc fault experiment platform with multiple types of sources and loads, line impedance and other components. The enhancement effect of stochastic resonance method on the characteristics of arc faults under different DC system topologies was studied. The enhanced arc fault characteristics of different topologies have their own independent optimal parameter combinations. Compared with the traditional orthogonal test method, the ant colony algorithm found that the optimal parameters faster and more accurate.The detection characteristics enhanced by stochastic resonance under the optimal parameter combination could more effectively distinguish the arc fault from the normal state. The calculation and comparison of experimental data verified the universality of stochastic resonance method to enhance arc fault characteristics in different topologies. Finally, a DC arc fault detection algorithm was constructed based on the support vector machine method and a higher detection accuracy rate was obtained. The usage of stochastic resonance in the arc fault detection process could effectively enhance the effective information of arc fault, reduce the difficulty of designing the arc fault detection algorithm, which is conducive to the efficient, fast and accurate detection of DC arc faults.