A novel scheme for island detection in microgrids based on fuzzy c-means clustering technique

Abstract

Microgrids (MGs) are capable to work at different operation modes, namely grid-connected or islanded, which make a significant change in the network fault current level. These changes may lead to problems and should be detected fast to do the proper protection actions accordingly and prevent blackouts. Moreover, some island detection methods suffer from the drawbacks of high computation burden and time-consuming procedure of training data to detect the islanded mode. For this purpose, in this paper, a faster and less computation burden island detection scheme without the need for training data is proposed which detects the islanded mode by analyzing the fault current data obtained from a continuous sampling using the phasor measurement unit (PMU). The sampled data are utilized in the fuzzy c-means (FCM) clustering to determine the network operation mode. The proposed scheme works in two phases. In the offline phase, the root mean square (RMS) of the current amplitude for islanded mode is determined, and in the online phase, the center of the measured data is compared to the RMS value to detect the MG operation mode at a decision making procedure. It is proved that the proposed island detection scheme is an applicable technique for detecting the islanded mode in MGs.

Microgrid Working Conditions Identification Based on Cluster Analysis—A Case Study From Lambda Microgrid

“…The article proposes a scheme for island detection of microgrid using data mining. The method is based on fuzzy c-means clustering [23]. In article [24], a support management system based on stochastic mixedinteger linear programming was proposed.…”