Abstract
Partial discharge of power equipment is one of the common faults in power systems. How to quickly and accurately determine the location of partial discharge is a problem that needs to be solved in practice. The signal arrival time difference estimation technique in signal processing is one of the effective methods to solve this problem. When the power equipment is partially discharged, an ultrasonic signal is generated. Therefore, the local discharge can be positioned according to the ultrasonic signal, however, the traditional signal arrival time difference estimation methods are not ideal for the actual low signal-to-noise ratio and narrow-band ultrasonic signals. In this paper, an improved correlation coefficient waveform comparison time difference estimation algorithm based on complete ensemble empirical mode decomposition with adaptive noise（CEEMDAN）, threshold denoising is proposed, referred to as CEEMDAN-TDE. Firstly, according to the characteristics of the actual ultrasonic signals, the double-exponential decay oscillation model is used to model the partial discharge ultrasonic signals, and Gaussian white noises are added as the interference signals. secondly, the CEEMDAN threshold denoising is used to improve the signal-to-noise ratio of the partial discharge signals; thirdly, the cross-correlation coefficient is calculated, then the arrival time difference can be obtained by comparing the waveforms of the correlation coefficients, and the partial discharge location information is known. The computer simulations of the CEEMDAN-TDE method, and the generalized correlation method, LMS method, and correlation coefficient waveform comparison method estimation are performed. Experimental results show that the estimating performance in arrival time difference of proposed method, CEEMDAN-TDE, is better than the other three methods’ under low SNR and narrowband. The CEEMDAN-TDE method has the hopeful more application in practice.
Highlights
Partial discharge (PD), as a key feature of high-voltage electrical insulation aging, its immediate detection is of great significance for the safe operation of power systems [1]
The CEEMDAN-based waveform comparison time difference estimation algorithm proposed in this paper aims to improve the time difference estimation accuracy of PD ultrasonic narrow-band signals in power equipment in complex environments
For the characteristics that the PD ultrasonic signal is a narrowband signal, the correlation coefficient waveform comparison method is introduced to solve the generalized correlation method, LMS algorithm and other peak detection algorithms, which are commonly used for PD signal delay calculation, has low estimation accuracy
Summary
Partial discharge (PD), as a key feature of high-voltage electrical insulation aging, its immediate detection is of great significance for the safe operation of power systems [1]. The collected signals in the field usually contain interference signals such as white noise and periodic narrow-band interference, which causes the accuracy of the traditional time difference estimation algorithm to be seriously reduced or even invalid. The literature avoids the peak detection problem by using the correlation coefficient waveform comparison method, and improves the accuracy of the narrow-band signal time difference estimation, but its estimation accuracy is significantly reduced in the case of low SNR [11]. The waveform comparison time difference estimation method based on wavelet transform is applied to the passive location of radio signals, which improves its robustness under low SNR conditions [12]. An improved waveform comparison time difference estimation algorithm, referred to as CEEMDAN-TDE, based on CEEMDAN denoising is applied to the time difference estimation of PD ultrasonic signals
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