A Data Mining Approach for Real-time Corrective Switching

Corrective transmission switching can improve the flexibility of power system. To avoid the computational complexity, a data mining approach for real-time corrective switching is proposed in this paper. A factorized expression that measures the approximate switching effects is first derived. The expression can be utilized to generate candidate transmission switching lists. Based on the factorized expression, an algorithm is proposed. The algorithm can be used to generate rank list of candidate switching actions. Since the algorithm bypasses the need to solve complex mixed integer optimization problems, it can provide candidate switching actions for real-time corrective control efficiently. The validity of the presented approach is demonstrated by tests conducted on IEEE RTS96 system.