A novel approach of partial discharges detection in a real environment

The presence of partial discharges (PD) in medium voltage overhead lines with covered conductors (CC) may indicate insulation fault, rupture or downfall of the line. In a real environment (e.g. forested terrain), presence of PD usually means that branch or tree have direct contact with CC. The detection of the PD in a real environment is an important task because it helps technicians to accurately identify fault and therefore it has economical benefits. However, the automatic detection of PD in a real environment has to deal with high interference of background noise. A novel approach of PD detection based on Singular Value Decomposition (SVD) and Particle Swarm Intelligence (PSO) is proposed in this paper. Its performance is compared to Support Vector Machines (SVM), Back Propagation (BP) and Extreme Learning Machine (ELM) methods. These methods use standard features like number of peaks, fractal dimension, etc. The quality of the detection was successfully increased by the proposed approach. So the faults in a real environment can be more accurately detected and thus more economically repaired.