基于经验模态分解与Elman神经网络的永富直流换相失败故障诊断方法

Yongfu DC commutation failure fault diagnosis based on the EMD-Elman neural network

直流输电系统因自身的优越性能已广泛应用于电力系统。换相失败是直流输电系统中一种常见故障,会导致电流电压发生突变,给直流系统的安全稳定运行带来严重威胁。利用PSCAD/EMTDC平台搭建了永富直流输电系统模型,提出了基于经验模态分解和Elman神经网络相结合(EMD-Elman)的换相失败故障诊断方法。通过大量仿真实验发现使用Elman+样本熵可以有效的进行故障诊断,仅在50组样本的情况下对换相失败故障识别的精确度就可以达到95%以上,证明了该换相失败识别方法的有效性。

HVDC transmission systems have been widely used in power systems due to their superior performance. Commutation failure is a common fault in the DC transmission system, which will cause sudden changes in current and voltage, which poses a serious threat to the safe and stable operation of the DC system. In this paper, the model of Yongfu HVDC transmission system is built by PSCAD/EMTDC, and the fault diagnosis method of commutation failure based on EMD-Elman neural network is proposed. The commutation failures in different cases are analyzed, and the neural network is combined with the approximate entropy and sample entropy for identification. Through a large number of simulation experiments, it is found that the use of Elman+ sample entropy can effectively diagnose faults, accuracy of commutation failure fault identification can reach over 95% in only 50 sets of samples, prove the effectiveness of the commutation failure identification method.