基于RBF神经网络的换流站交流滤波器故障预警研究

Study on Fault Warning of AC Filter at Converter Station Based on RBF Neural Network

换流站交流滤波器是高压直流输电系统的重要组成部分,其跳闸事故的发生将直接影响直流输电系统的输送功率。本文提出了一种根据交流滤波器断路器的分合闸电流来在线识别交流滤波器健康状态的方法。首先,定义了一系列交流滤波器断路器分合闸电流的时域特征量和频域特征量;在此基础上,利用基于径向基神经网络的人工智能方法来实现交流滤波器故障预警;某实际换流站的实证结果表明本文方法具有较高的故障预警准确度,能够在交流滤波器异常扩大或造成不良影响之前发出告警,提醒相关人员及时检查和处理缺陷,减少交流滤波器异常跳闸现象的发生。

AC filter in converter station is an important part of HVDC transmission system and the occurrence of tripping will affect directly the transmission power of the DC transmission system. In this paper,a kind of healthy status of AC filter which is on-line identified by the opening and closing current of AC filter circuit breaker is proposed. Firstly,both time and frequency domain feature of the opening and closing current of AC filter breaker is defined. On this basis,the fault warning of AC filter is achieved by the artificial intelligence method based on radial neural network. It is shown by the actual result of certain converter station that the method has high fault warning accuracy,can give warning before expansion or negative influence due to abnormality of the AC filter,and remind related people to check and deal with the defects on time so to reduce the occurrence of abnormal tripping of the AC filter.