Corrective action planning using RBF neural network

In recent years, voltage limit violation and power system load-generation imbalance, i.e., line loading limit violation have been responsible for several incidents of major network collapses leading to partial or even complete blackouts. Alleviation of line overloads is the suitable corrective action in this regard. The control action strategies to limit the line loading to the security limits are generation rescheduling/load shedding. In this paper, an approach based on radial basis function neural network (RBFN) is presented for corrective action planning to alleviate line overloading in an efficient manner. Effectiveness of the proposed method is demonstrated for overloading alleviation under different loading/contingency conditions in 6-bus system and 24-bus RTS system.