基于改进量子粒子群优化稀疏分解的局放信号去噪方法

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Abstract Noise suppression is one of the key links for partial discharge(PD) online monitoring. Focusing on the noise suppression, a PD signal denoising method based on improved quantum-behaved particle swarm optimization sparse decomposition was given in this paper. More specifically, the principle of this method is signal sparse decomposition. The partial discharge signal matching overcomplete dictionary which only matches the features of PD signals was built. Based upon these, polluted PD signal was sparse decomposed by matching pursuit(MP) algorithm in this dictionary to search the best matching atoms. Meanwhile, the improved quantum-behaved particle swarm optimization(IQPSO) was presented to accelerate the searching process, and the residual ratio was chosen to be the terminating condition of the iteration as well. Since no noise PD signal component can be sparse represented by the best atoms while the noise cannot, the goal of denoising was finally achieved. The denoising method presented in this article is applied on the simulated and measuring signals, the results are critical compared with the effect of the PD denoising method based on the morphological wavelet, the results show that the denoising method of this paper is available to precisely suppress the noise interference of PD signal with high accuracy results; the distortion of waveform after denoising cannot be found and the features of PD signal were well kept.

Key words： Improved quantum-behaved particle swarm optimization(IQPSO) sparse decom- position matching pursuit(MP) partial discharge(PD) denoising

Received: 14 April 2014 Published: 14 September 2015

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