## 1 Introduction  
The monitoring for partial discharges (PDs) has become the main method for insulation testing and diagnosis. Ultrahigh frequency (UHF) electromagnetic waves are widely used in PD detection due to their superiority of strong anti-interference, high sensitivity, and stable transmission [1; 2; 3; 4; 5; 6; 7; 8]. In order to extract signal characters and separate different PD sources, the suppression of field electromagnetic noise interference is important in PD detection and analysis. Recently, methods have been proposed for separating multiple PD sources, including time-resolved partial discharge (TRPD) [9; 10], phase-resolved partial discharge (PRPD) [11], and time-frequency analysis with neural network and fractal technologies [12; 13]. However, when multiple PD sources exist in the power station, their PRPD patterns blend together. Therefore, it is difficult to extract the characteristics of a single source from PRPD patterns. There are always many types of electromagnetic noise mixed in the UHF signals that are radiated from PDs and collected in a substation, such as white noise, wireless communication, and radio signals. The noise sources mentioned above cause difficulty in extracting waveform characteristics of PD pulses; therefore, the TRP-based method is poor for separating multisource PDs. It is also hard to reach the goal by time-frequency analysis methods due to the complication of getting high-dimension time-frequency parameters. Furthermore, the time-frequency methods can only efficiently suppress narrowband or periodical noise. As far as white noise is concerned, the time-frequency methods do not work. As properties of high order cumulants described in [14; 15; 16; 17], high order cumulants whose orders are no less than three of a Gaussian process are zero. The odd-order cumulants of the symmetric distribution procedure are also zero. Therefore, the high order cumulants of the observation process are equal to the non-Gaussian signals, which is embedded in the colored-Gaussian noise. If the spectra of PD UHF signals in substations are reconstructed by using odd-order cumulants of the signals, the reconstructed spectra will not be mixed with colored-Gaussian noise, white noise, and symmetrical distribution noise. The high order cumulant algorithms can work without prior knowledge for correlation between signals and noise, and are better than other statistical methods using high order moment. Therefore, the high order cumulants are usually utilized as the tool for analyzing non-Gaussian signals. In [18], the frequency spectrum of a signal is reconstructed based on an autoregressive and moving average (ARMA) model using high order cumulants; then, characteristic frequencies are selected by Fisher-like class separability measures. Finally, simulations and on-site experiments are used to show the capability of the algorithm. However, the phase information of signals is lost due to the spectrum reconstruction. High order cumulants and multispectra have the properties of time-shift invariance, scale variability, and phase stability [19]. Furthermore, because of the special properties of high order cumulants, it can be deduced that the application of selected bispectra can effectively suppress Gaussian noise, periodic narrow-band signals, and frequency-fixed signals blended in the PD UHF signals. The phase information maintained in bispectra can be useful and important for further signal processing. In the proposed algorithm, bifrequencies of signals where the bispectra have the maximum Fisher-like class separability measures are selected as characteristic parameters, which are used for training a radial basis neural network to separate the received signals. The simulated UHF signals mixed with fixed-frequency signals and different SNRs are analyzed by the proposed method. The result shows that the method is very effective at separating signals.