Reliable Islanding Detection Scheme for Distributed Generation Based on Pattern-Recognition
Bokka Krishna Chaitanya, Anamika Yadav, Mohammad Pazoki
Abstract
This article presents a reliable islanding detection scheme for distributed generation (DG) to minimize the nondetection zone using a pattern-recognition method. A hybrid time-frequency signal decomposition along with a machine learning processes the voltage signal retrieved at the DG to make final decision. Amalgamation of time-varying filter and time domain decomposition obtains a modified intrinsic mode functions (MIMF), which enhances the time-frequency resolution of nonstationary signals. Moreover, the adaptive nature of the proposed hybrid signal decomposition makes it more advisable over other decomposition techniques to frame the input feature vector. Further, the random subspace ensemble framework based on ensemble k-nearest neighbor classifier is used among different machine learning techniques to identify the islanding condition by applying the feature vector generated using MIMF. The proposed scheme is thoroughly verified on two standard test systems for identifying the typical islanding condition of zero power mismatch and the proposed scheme discriminates the islanding from large scale disturbances such as capacitor switching and faults. The performance of the scheme is assessed through reliability analysis and it is also compared to other machine learning techniques.