Partial Discharge Pulse Segmentation Approach of Converter Transformers Based on Higher Order Cumulant

As one of the most effective methods to detect the partial discharge (PD) of transformers, high frequency PD detection has been widely used. However, this method also has a bottleneck problem; the biggest problem is the mixed pulse interference under the fixed length sampling. Therefore, this paper focuses on the study of a new pulse segmentation technology, which can separate the partial discharge pulse from the sampling signal containing impulse noise so as to suppress the interference of pulse noise. Based on the characteristics of the high-order-cumulant variation at the rising edge of the pulse signal, a method for judging the starting and ending time of the pulse based on the high-order-cumulant is designed, which can accurately extract the partial discharge pulse from the original data. Simulation results show that the location accuracy of the proposed method can reach 94.67% without stationary noise. The field test shows that the extraction rate of the PD analog signal can reach 79% after applying the segmentation method, which has a great improvement compared with a very low location accuracy rate of 1.65% before using the proposed method.

Pulse interference identification and suppression for OFDM signal

The new generation smart meter is the core node of information acquisition and flows in the power Internet of Things (IoT). Because it is powered by the low-voltage power line, various short-time pulse interference signals in the line can be easily coupled to the received signal of the communication module embedded in the meter, resulting in the degradation of communication performance. In view of this situation, an identification-suppression algorithm of the short-time pulse-interference signal is proposed. The core idea is to comprehensively judge the existence of the interference signal and its specific position in the time domain based on the energy ratio of adjacent OFDM symbols and the change of mean square error value between frequency domain amplitude signals. Based on this, an interference energy elimination scheme with an adaptive threshold is realized. Theoretical analysis and simulation results show that the scheme has low computational complexity, high accuracy of pulse interference identification and position judgment, and can effectively reduce the energy of pulse interference signal, and thus improving the decoding performance of OFDM while it receives signals.

Geomagnetic signal de-noising method based on improved empirical mode decomposition and morphological filtering

To solve the problem that geomagnetic signals are susceptible to random noise and instantaneous pulse interference in geomagnetic navigation, a geomagnetic signal de-noising method based on improved empirical mode decomposition (IEMD) and morphological filtering (MF) is proposed. The instantaneous pulse interference is eliminated by designing different structural elements according to the characteristics of the pulse signal. The signal after filtering the instantaneous pulse interference is decomposed by EMD, and the intrinsic mode functions (IMFs) obtained from the decomposition are determined as two modes (i.e. noise IMFs and mixed IMFs) by the cross-correlation coefficient criterion. The noise IMFs are removed directly, and a normalized least means square filter (NLMS) is designed to remove noise from mixed IMFs, which can adaptively adjust the filtering parameters according to the noise level of different IMF components. The noise-reduced mixed IMFs and residual are reconstructed to obtain the final geomagnetic signal. Experiment results illustrate that the proposed MF-IEMD method can effectively achieve noise reduction. Comparing with the traditional EMD and MF-EMD de-noising methods, the root mean square errors(RMSE) decreased by 49.27% and 24.79%, respectively.