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| A Combined De-Noising Method for Power Quality Disturbances Events |
| Xiao Xiangui1, Li Kaicheng1, Cai Delong2, Wang Menghao1, Wang Wei1 |
1. State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and Technology Wuhan 430074 China;
2. State Key Laboratory of Power Grid Environmental Protection Wuhan Branch of China Electric Power Research Institute Co. Ltd Wuhan 430074 China |
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Abstract The traditional wavelet threshold de-noising algorithm has some disadvantages, such as easy to discard the real signal and poor de-noising effect. This paper presented a new joint de-noising algorithm for power quality disturbance events. Firstly, the disturbance type of signal is preliminarily determined by the number of times that the fading factor of strong tracking Kalman filter is greater than 1, and then different de-noising methods are adopted for different disturbance types. For sinusoidal signal and harmonic signal only containing noise, sparse decomposition and fast Fourier transform (FFT) are used to de-noising twice; fading factor is used to accurately indicate the beginning and ending time of disturbance for sag and rise signal, and sparse decomposition and FFT de-noising are used for each segment of signal; different processing methods are used for signal containing transient pulse and transient oscillation. Firstly, the steady-state component and transient component are obtained by sparse decomposition. The de-noising method of steady-state component is the same as that of sinusoidal signal. The impulse signal and oscillation signal of transient component are processed by retaining the actual value and variational mode decomposition (VMD) respectively. A large number of simulation results show that under different SNR conditions, the proposed algorithm can effectively suppress the noise of various disturbance signals, significantly improve the signal-to-noise ratio, and the effect is better than the wavelet threshold de-noising algorithm.
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Received: 13 October 2020
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2021, Vol. 36 
