Abstract
This paper presents a new approach based on wavelet design and machine learning applied to passive islanding detection of distributed generation. Procrustes analysis is used to determine the filter coefficients of a newly designed wavelet. To automate the classification process, machine learning algorithms are used to develop appropriate models. The IEEE 13-bus standard test distribution system simulated in PSCAD/EMTDC is used as a test bed to assess the performance of the proposed approach. The numerical results demonstrating the effectiveness of the proposed approach are discussed and conclusions are drawn.