A novel stability classifier based on reformed support vector machines for online stability assessment

Abstract

Online transient stability assessment (TSA) has always been a tough problem for power systems. One of the promising solutions is to extract hidden stability rules from historical data by machine learning algorithms. These algorithms have not been fully accommodated to TSA, since power system has its special characteristics. To ensure conservativeness of TSA, this paper proposes a synthetic stability classifier based on reformed support vector machines. It separates samples into stable, unstable and grey area. The stable and unstable classes are expected to be exactly correct. Moreover, an SVM solver for large scale problem is designed based on sequential minimal optimization (SMO). It decomposes large scale training into parallel small scale training so as to speed up computation. Case studies on IEEE 39-bus system show no false dismissals and demonstrate the advantage of proposed classifier and SVM solver.