基于行波特征与KOA-CNN-BiGRU-AM的柔直输电线路故障诊断  

Fault diagnosis of MMC-MTDC based on traveling wave characteristics and KOA-CNN-BiGRU-AM

作　　者：余波 高学军[1] 王灿 李瑞灵 徐彦彬 荣梦杰 YU Bo;GAO Xuejun;WANG Can;LI Ruiling;XU Yanbin;RONG Mengjie(College of Electrical Engineering and New Energy,China Three Gorges University,Yichang 443002,China)

机构地区：[1]三峡大学电气与新能源学院,湖北宜昌443002

出　　处：《电力工程技术》2025年第2期185-196,共12页Electric Power Engineering Technology

基　　金：国家自然科学基金资助项目(52107108)。

摘　　要：针对多端柔性直流电网(multi-terminal direct current grid based on modular multilevel converter,MMC-MTDC)故障诊断存在的人工整定阈值过程复杂、高阻故障不易检测的问题,提出一种基于行波特征的诊断方法。首先,通过分析系统的故障特征,得出边界元件对高频信号的阻滞作用;其次,利用经验模态分解(empirical mode decomposition,EMD)对功率进行分解,得到本征模态函数(intrinsic mode function,IMF)分量,将其能量值作为故障特征量训练由卷积神经网络(convolutional neural network,CNN)和双向门控循环单元(bidirectional gated recurrent unit,BiGRU)组成的CNN-BiGRU网络;然后,采用开普勒优化算法(Kepler optimization algorithm,KOA)和注意力机制(attention mechanism,AM)对CNN-BiGRU网络进行改进,实现MMC-MTDC的故障诊断;最后,在PSCAD/EMTDC中搭建仿真模型。结果表明,该方法不仅可以实现母线故障和线路故障的检测,还可以在满足保护可靠性和速动性的前提下,解决高阻故障保护易拒动的问题。Based on travelling wave features,a diagnostic method is proposed to address the complexity of manual threshold setting process and the difficulty of detecting high-resistance faults in the fault diagnosis of multi-terminal direct current grid based on modular multilevel converter(MMC-MTDC).Firstly,the blocking effect of boundary elements on high-frequency signals is identified by analyzing the fault characteristics of the system.Secondly,empirical mode decomposition(EMD)is employed to decompose power signals into intrinsic mode function(IMF),and the energy values of the IMF is utilized as fault features to train the CNN-BiGRU network composed of convolutional neural network(CNN)and bidirectional gated recurrent unit(BiGRU).On this basis,the Kepler optimization algorithm(KOA)and attention mechanism(AM)are employed to enhance the CNN-BiGRU network to realize the fault diagnosis of the MMC-MTDC.Finally,the simulation model is built in PSCAD/EMTDC.The results show that the method can not only realize the detection of bus faults and line faults but also solve the problem of easy refusal of protection under the high resistance state while meeting the requirements of protection reliability and speed.

关 键 词：多端柔性直流电网(MMT-MTDC) 故障特性 经验模态分解(EMD) 开普勒优化算法(KOA) 注意力机制(AM) 卷积神经网络(CNN) 双向门控循环单元(BiGRU) 故障诊断 

分 类 号：TM712[电气工程—电力系统及自动化] TM773[自动化与计算机技术—控制理论与控制工程] TP18[自动化与计算机技术—控制科学与工程]