基于深度置信网络的柔性直流配电网高灵敏故障辨识策略

针对柔性直流配电网现有保护手段应对高阻接地故障灵敏性较差的问题,提出一种基于深度置信网络(DBN)的柔性直流配电网高灵敏故障辨识策略.该策略利用DBN的特征提取能力对正负极暂态电压时域波形数据进行特征提取,以进行故障辨识.利用仿真采集样本数据对DBN进行训练,获取模型最优参数,形成适用于直流配电网故障辨识的DBN模型.基于PSCAD/EMTDC仿真平台对模型性能进行测试.测试结果表明,所提辨识策略对于高阻接地故障具有较好的灵敏性,并且具有较强的泛化能力.

High-sensitivity Fault Identification Strategy for Flexible DC Distribution Network Based on Deep Belief Networks

Aiming at the problem that the existing protection methods for flexible DC distribution network are not sensitive to high-resistance grounding faults, a high-sensitivity fault identification strategy based on deep belief networks (DBNs) is proposed. The strategy uses the feature extraction ability of DBN to extract the features of positive and negative transient voltage waveform data in time domain for fault identification. The simulation sample data is used to train the DBN, and the optimal parameters of the model are obtained to form a DBN model suitable for DC distribution network fault identification. The performance of the model is tested based on PSCAD/EMTDC simulation platform. The test results show that the proposed identification strategy has good sensitivity and generalization ability for high-resistance grounding faults.