Weighted Islanding Detection for DC Microgrid Based on Random Forest Classification

Abstract

At present, the main form of microgrid is AC grid. DC microgrids have received extensive attention and research with the rapid development of various DC power. The operation mode of the DC microgrid is divided into grid-connected operation and islanding operation. Islanding is formed after the circuit breaker tripped, which connects microgrid to large grid. Islanding operation can be divided into planned islanding and unplanned islanding. Unplanned islanding will cause certain harm to users and systems, so it is necessary to detect islanding accurately in the DC microgrid. This paper proposes an islanding detection method for DC microgrid based on random forest classification. Firstly, raw data is cleaned, extracted features and generated feature vector set. The extracted features include six islanding characteristic indexes, which consist of voltage, current, output active power and their first order backward difference on the DC bus side. Then, based on random forest classification, building the islanding detection model. Islanding detection model for DC microgrid can distinguish islanding event successfully and accurately. Based on weighted random forest classification, it can detect islanding event more accurately compared with decision tree classification when processing large amounts of data.