Study the performance of S-transform based extreme learning Machine for islanding detection in distributed generation

With the recent development of Microgrid system, the penetration of distributed generation (DG) into the distribution network is bringing various protection and operational problem. Among them islanding is a major issue of Microgrid system which leads to equipment hazard as well as risk of life. Therefore, the detection of islanding has become a must. The islanding detection scheme proposed in this paper constitutes of S-Transform based feature extraction method and evaluation of feature vector by the use of extreme learning machine (ELM) classifier. In this manuscript, a system containing two DGs, one synchronous wind generator and one asynchronous hydro generator are taken into consideration. The effectiveness of ELM is shown in this paper by testing and training the feature vector with different noisy environment and comparing them with other existing classifiers like artificial neural network (ANN), support vector machine (SVM), clustering etc. The whole system was simulated by using MATLAB software.