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

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.