Underdetermined blind source separation algorithm of 220kV substation noise based on SCA

Abstract

With the increasing construction of high-voltage substation, the distance between resident area and substation is getting closer. Noise of substations has been an unavoidable problem which can be controlled better by analyzing it. Otherwise, the operational conditions of equipment in substation can be predicted by comparing the measured noise signals with the signals during normal operation. Corona noise emitted by high voltage transmission line, environmental quasi-stationary noise and noumenon noise generated by electric appliances are three main types of the noise signals in the substation which can't be measured independently by traditional methods. An Underdetermined Blind Source Separation (UBSS) algorithm based on the Sparse Component Analysis (SCA) was proposed to separate the mixed 220kV substation noise in this paper which can be applied to overcome the shortage of Independent Component Analysis (ICA). The feasibility of the algorithm was verified in the simulation and the experiment before using it to separate the substation noise. An approach to feature extraction based on the wavelet modulus maxima was employed to analyze the separated noise. All the results show that SCA can separat mixed substation noise efficiently.