Data mining technique can effectively determine the threshold settings of islanding detection

which has become an important islanding detection approach. This paper proposes a comprehensive islanding detection data mining system consisting of three parts: critical feature identification

base learner and meta learner. First

the negative effect of inferior features of islanding detection samples on classification is analyzed. Correspondingly

the recursive elimimation of features(RELIEF) algorithm is provided to identify those critical features. Then

since a single classifier causes inductive bias phenomenon

multiple classifiers are combined to enhance islanding detection accuracy. Finally

a new approach based on meta-learning is proposed. In order to verify the effectiveness of the above method

power imbalance and voltage disturbance are taken into consideration in simulation examples. Results show that the three parts of the system perform remarkably well in improving the islanding detection accuracy and generalization ability.