含光伏电源配电网故障的智能辨识方法研究

Research on intelligent fault identification method of distribution network including photovoltaic power supply

在光伏电源接入配电网后,由于光伏发电的不确定性、间歇性、波动性增加了配电网故障的辨识难度。针对此问题,提出一种熵-变分模态分量与神经网络改进ResNet模型相结合的方法。首先搭建含光伏电源配电网PSCAD仿真模型,获取不同复杂故障情景下的批量数据。其次,利用熵-变分模态(E-VMD)方法重构样本的特征矩阵,再采用改进残差网络进一步挖掘故障样本的隐含特征,然后通过模型的训练与测试。与其他文献模型的分类效果对比,改进ResNet模型故障类型识别的准确率平均达到99.95%,故障馈线识别的准确率达到99.75%,且具有良好的鲁棒性,可以有效地实现含光伏电源配电网故障的快速辨识。

After the PV power supply is connected to the distribution network,the uncertainty,intermittency and fluctuation of PV power generation increase the difficulty of identifying faults in the distribution network.To address this problem,this paper proposes a method combining the entropy-variance modal component with a neural network to improve the ResNet model.Firstly,a PSCAD simulation model of the distribution network containing PV power is built to obtain batch data under different complex fault scenarios.Secondly,the entropy-variance modal(E-VMD)method is used to reconstruct the feature matrix of the samples,and then the improved residual network is used to further explore the implied features of the fault samples,and then the model is trained and tested.In comparison with the classification results of other models in the literature,the improved ResNet model achieves an average accuracy of 99.95%for fault type identification and 99.75%for fault feeder identification,and has good robustness,which can effectively achieve fast fault identification in distribution networks containing PV power supplies.