ISLANDING DETECTION FOR DISTRIBUTED GENERATION

Abstract

This thesis presents two islanding detection techniques for distribution network hav- ing high penetration of Distributed Generations (DG's) system. Various islanding and non-islanding events for di erent values of active and reactive power mismatches are simulated using Real time digital simulator (RTDS) software. One of the proposed tech- nique is based on the machine learning technique i.e and other technique is based on the passive islanding detection technique. The machine learning tool, Random forest technique is used as a classi er to classify the islanding events and the non-islanding events. The RF classi er utilizes sequence components of voltages which are obtained by acquiring voltage samples of all the phases from point of common coupling (PCC) of the targeted DG. The passive detection technque is based on the oscillation frequency of the synchronous generator. The change in the magnitude of the oscillation frequency is used to di erentiate the islanding events from the non-islanding ones. Validity of the proposed schemes has been evaluated on large number of islanding/non-islanding cases which are generated by modeling standard IEEE 34-bus system for the technique based on Random forest classi er and by modelling standard IEEE 123 Bus System for the technique based on oscillation frequency of the synchronous generator. The simulation results indicate that the proposed schemes are capable to provide e ective discrimination between islanding situation and non-islanding events. Moreover, they also gives satis- factory results during perfect power balance situation. In addition, it provides better stability in case of non-islanding events and hence, avoids nuisance tripping.