A method for separating multisource partial discharges (PDs) in a substation is proposed based on selected bispectra of ultrahigh frequency (UHF) electromagnetic waves. Bispectra are sensitive to Gaussian noises and processes of symmetrical distribution. The phase information contained in bispectra can be useful and important for further signal processing. Bifrequencies where Fisher-like class separability measures between signals’ bispectra achieve their maximums are selected as characteristic parameters of the signals. Then, the selected bispectra are utilized for training the radial basis neural network to separate PD UHF signals in a substation. The method is used to analyze simulated UHF signals mixed with Gaussian white noise and frequency-fixed interference, and to separate PD UHF signals that are collected in a 500 kV substation. In order to prove the validity of the proposed separation method, the localization results are compared with the results calculated by time delay sequence, and the proposed separating algorithm is verified in the interference circumstances of a substation. However, the exact location of PD sources cannot be calculated according to the time delay sequence when the PD sources in a substation are close to each other or there are fewer than four antennas for receiving signals.

中文翻译：

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基于特高频信号双谱的变电站多源局部放电分离方法

提出了一种基于选定的超高频 (UHF) 电磁波双谱分离变电站中多源局部放电 (PD) 的方法。双谱对高斯噪声和对称分布过程很敏感。双谱中包含的相位信息对于进一步的信号处理非常有用和重要。选择信号双谱之间的类Fisher 类可分性度量达到其最大值的双频作为信号的特征参数。然后，选择的双谱用于训练径向基神经网络以分离变电站中的 PD UHF 信号。该方法用于分析混有高斯白噪声和定频干扰的模拟超高频信号，分离500 kV变电站采集的局放超高频信号。为了证明所提出的分离方法的有效性，将定位结果与时延序列计算的结果进行了比较，并在变电站干扰情况下验证了所提出的分离算法。然而，当变电站中的局部放电源彼此靠近或接收信号的天线少于四个时，无法根据时延序列计算出局部放电源的准确位置。