Self-adaptive Separation of Multiple Partial Discharge ... - IEEE Xplore

246

M.-X. Zhu, et al.: Self-adaptive Separation of Multiple Partial Discharge Sources

Self-adaptive Separation of Multiple Partial Discharge Sources Based on Optimized Feature Extraction of Cumulative Energy Function Ming-Xiao Zhu, Qing Liu, Jian-Yi Xue, Jun-Bo Deng, Guan-Jun Zhang State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering Xi’an Jiaotong University. Xi’an, Shaanxi, 710049, China

Xian-Jun Shao, Wen-Lin He Research Institute of State Grid Zhejiang Electric Power Company Hangzhou, Zhejiang, 310014, China

An-Xiang Guo and Xiao-Wei Liu Research Institute of State Grid Shaanxi Electric Power Company Xi’an, Shaanxi, 710048, China ABSTRACT Partial discharge (PD) measurement is an effective tool for insulation condition assessment of high-voltage power equipment. The occurrence of multiple PD sources causes great difficulty on pattern recognition and failure risk assessment. This paper presents a self-adaptive PD separation algorithm based on optimized feature extraction of cumulative energy (CE) function. The CE functions in time domain (TCE) and frequency domain (FCE) are calculated from PD waveforms and their FFT spectrums, respectively. By using an oblique line to cross the CE curves, width features are extracted from the intersection points between them. Through the mathematical morphology gradient (MMG) operation, sharpness features are extracted to characterize the rise steepness of CE. It is found that the separation capability of width and sharpness are dependent on the pre-selected oblique line and the structure element length (SEL) in MMG, respectively. In order to obtain satisfactory PD separation results for various experimental conditions, a density-function based parameter is proposed to evaluate the separation capability, and the oblique line and SEL are optimized with the goal of maximizing the evaluation parameter. A clustering algorithm is adopted to discover different clusters in feature space and separate PD signals. The separation algorithm is examined with mixed PD current pulses and ultrahigh-frequency (UHF) signals acquired from experiments in laboratory and on-site equipment. The results indicate that the self-adaptive separation method is immune to the change of experimental conditions, and is effective for separating mixed PD signals. Index Terms — Partial discharge, self-adaptive PD separation, cumulative energy function, optimized feature extraction.

1 INTRODUCTION Partial discharge (PD) measurement provides valuable information of insulation condition, and gradually becomes an effective tool for offline test and online monitoring of highvoltage (HV) equipment [1-4]. PD source recognition and failure risk assessment are two major steps in the PD-based diagnostics, and mainly adopt features extracted from the phase resolved Manuscript received on 25 February 2016, in final form 15 September 2016, accepted 12 October 2016. Corresponding author: G.-J. Zhang.

partial discharge (PRPD) patterns [5, 6]. In operation conditions, more than one PD sources may occur simultaneously in HV equipment, which would cause the mixture of PRPD patterns. Under this circumstance, the classification method based on fingerprints calculated from PRPD patterns may not give right results [7-11]. Thus, the separation of mixed PD signals into subclasses corresponding to individual sources is of great importance for reliable PD signal interpretation. Numerous researches indicate that the PD signals generated by same source are of similar features while various sources are

DOI: 10.1109/TDEI.2016.005893

IEEE Transactions on Dielectrics and Electrical Insulation Vol. 24, No. 1; February 2017

characterized by different waveforms [7-16]. Based on this rule, the separation of mixed PD signals can be achieved by clustering the representative feature parameters extracted from PD pulse waveforms [7-16]. Several separation features were extracted by using different kinds of signal time-frequency processing techniques such as fast Fourier transform (FFT) [7, 8], spectral-power analysis [9-11], first moment time and frequency analysis [12], wavelet decomposition [13, 14], Stransform [15, 16] and mathematical morphology (MM)-based signal decomposition [14]. Moreover, some other separation methods based on PD waveform comparison such as K-means clustering of auto-correlation function [17], ultra-highfrequency (UHF) signal envelope comparison [18] etc., were also applied to PD signals separation. In our previous papers, the authors proposed a PD signal separation algorithm using cumulative energy (CE) function and mathematical morphology gradient (MMG) [19, 20]. Three feature parameters including width, sharpness and gravity are extracted from CEs and MMGs in both time and frequency domain. Some predefined parameters in the calculation procedures such as the threshold values for CE (in calculation of width) and the length of the structure element in MMG operation (in calculation of sharpness), are determined according to the experimental results and set as fixed values for all cases. The experimental conditions (equipment under test, measurement circuit and coupler performance, etc.) change the pulse wave-shapes, so the fixed parameters cannot applicable to all cases. The algorithm may not be able to satisfactorily separate mixed PD signals in some cases. As a continuance of the previous work, in this paper, a selfadaptive PD separation technique based on optimized feature extraction of CE function is proposed. In this method, the value of predefined parameters change self-adaptively for different experiments to get optimized separation results. Section 2 introduces the calculation procedures of the proposed separation algorithm. In Section 3, a density-function based parameter is proposed to evaluate the separation performance of extracted features, and the procedures for optimized feature extraction are summarized. Finally, the separation algorithm is examined with mixed PD current pulses and UHF signals acquired from experiments in laboratory and on-site equipment.

2 PD SIGNAL SEPARATION ALGORITHM

247

Owing to the variable triggering position that caused by stochastic waveform of PD signals, pulse location in records and the time-offset of CEs are different between pulses. Since the extracted features are dependent on the CE location, timeshift operation is performed to align the CEs. To achieve this, the starting point t s of each PD pulse is determined and normalized as zero time

ti  tk  ts , 1  s  N , k  s (3) where tk is the original time sequence, and ti is the time sequence after time-shift. Experiments indicate that the cumulative energy of pre-impulse section (noise ahead PD signals) are generally smaller than 20. Therefore, the starting point can be determined as the sampling point which first cross the threshold value of ET=20. 2.2 EXTRACTION OF SEPARATION FEATURES The width and sharpness features in [19, 20] are proved effective for PD separation. Their calculation procedures are redefined in this paper to improve the separation performance. Table 1 summarizes the differences between original and redefined features. Table 1. Differences between original and redefined features Features Description original Tw Time difference between ET=80 and ET=20. original Fw Frequency bandwidth of ET