Multi-end PD location algorithm using segmented correlation and trimmed mean data filtering techniques for MV Underground Cable

Medium voltage underground cables are widely used in urban area as distribution power lines due to their advantages over overhead cables. Underground cables may suffer from partial discharge because of insulation degradation after certain period of time. In this paper, an improved multi-end partial discharge location algorithm called segmented correlation trimmed mean algorithm is proposed. The algorithm uses segmented correlation technique and trimmed mean data filtering technique to enhance the accuracy of partial discharge location algorithm. The algorithm had been tested in MATLAB environment which consists white Gaussian noise. First, discrete wavelet transform is carried out to suppress noisy signals. Next, segmented correlation technique is applied to the de-noised segmented partial discharge signals. Segmented correlation technique has manipulates partial discharge signals by performing cross correlation process on segmented partial discharge signals in order to reduce memory usage and algorithm execution time. Lastly, trimmed mean data filtering technique is applied to the estimated partial discharge location values to estimate new partial discharge location value. Trimmed mean data filtering technique does statistical process control on estimated partial discharge location values in order to minimize the error of estimated partial discharge location. The segmented correlation trimmed mean algorithm had been compared with the existing multi-end correlation-based algorithm. The result shown that the segmented correlation trimmed mean algorithm has better accuracy than multi-end correlation-based algorithm.