电晕放电辐射信号的探测系统与识别方法

Detection System and Recognition Approach of Electromagnetic Signal Radiated from Corona Discharge

准确探测电晕放电辐射信号对精确定位电晕放电源、确保高压输电线安全稳定运行具有重要意义。为此,设计了基于差分降噪原理的探测系统,分别对背景噪声、电晕放电和火花放电信号进行了测试。实验结果及其时频分析表明：该差分模块能很好地将0～100 MHz和600～1 000 MHz范围内的广播、电视、通讯信号以及空间电磁干扰信号滤除,在复杂电磁场环境下探测到静电放电信号。火花放电与电晕放电的频谱特征相似,为排除火花放电干扰,基于学习向量化（LVQ）神经网络算法,对2者的时域波形进行了特征提取与模式识别,实现了静电放电类型的判别,且判别准确率达95%。因此,利用探测系统和提出的LVQ神经网络模式识别方法能够有效探测和识别电晕放电辐射信号,为高压输电线的电晕放电监测以及电晕放电目标定位提供参考。

Accurately detecting electromagnetic signals generated by corona discharges is of significance to precisely locate corona sources and ensure the safe and stable operation of high voltage transmission lines. Hence we designed a detecting system based on differential noise reduction, and used it to experimentally measured background noises, corona discharge signals, and spark discharge signals. The results and their time-frequency analyses show that, in complex elec- tromagnetic backgrounds, the system with differential noise reduction module is able to filter out the interference signals within ranges of 0-100 MHz and 600-1 000 MHz, including broadcasting signals, television signals, telecommunication signals, and electromagnetic interference signals, and to probe the electrostatic discharge signals in the meantime. More- over, to exclude the interference from spark discharges, we introduced the learning vector quantization （LVQ） neural network to distinguish the signals from spark discharges and corona discharges, which are quite similar in their spectrum characteristics. Through feature extraction and mode recognition in time domain, the LVQ neural network is able to dis- tinguish the two discharges with accuracy rate of 95%. It is concluded that the detection system along with the LVQ neural network is effective in detecting and recognizing signals generated by corona discharge, and can support corona monitoring and corona locating for high-voltage transmission lines.