Cascade BPN based transmission line overload prediction and preventive action by generation rescheduling
This paper is a modest attempt to address an intelligent technique based on Cascade Back Propagation Neural network (CBPN) for detection of line overloads, prediction of overloading amount and alleviation of overloads using generation rescheduling with N-1 contingency in restructured power system. In the present competitive power market, power transmission congestion has become more intensified and recurrent than the vertically bundled system. Transmission line congestion initiates the cascading outages which forces the system to collapse. Accurate prediction and alleviation of line overloads are the suitable corrective actions to avoid network collapse. The most obvious technique for network Congestion Management (CM) is rescheduling the power output of generators. The proposed CBPN comprises three Artificial Neural Networks (ANNs) in cascade. ANN1 is used as a classifier, which classifies the system state as secure or insecure. ANN2 is used for the prediction of overloading amount in the overloaded lines and ANN3 gives optimal generation rescheduling values for alleviating the overloads. The real power and reactive power of loads at all the load buses and the line outage number are considered as input variables to ANN1, ANN2 and ANN3. The effectiveness of the proposed approach is tested for various contingencies in the IEEE30 Bus System. The experimental results show that the proposed approach is computationally fast, reliable and efficient, in restoring the system to the normal state after a contingency with minimal control actions.
bpn n-1 contingency transmission congestion congestion management power system security