Abstract

Summary In a microgrid, the islanding condition occurs when the utility circuit breaker (CB) is opened. In this case, the microgrid is disconnected from the main grid. Therefore, the islanding condition can be detected by monitoring the variables that caused the utility CB to open. In this paper, the utility CB current is measured at the grid side, and the islanding condition is detected based on a feature extracted from the measured signal before the utility CB opening. Discrete wavelet transform is used to extract the features of the measured current, and then, the artificial neural network is trained in order to detect the islanding conditions based on the extracted features. Since the disturbances of distributed generation (DG) units or fault occurrence have no effect on the measured current, the proposed method is an effective one for islanding detection in distribution networks with high penetration of DGs. Copyright © 2014 John Wiley & Sons, Ltd.

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