Identification and ranking of weak buses using modified counterpropagation neural network

Abstract

The task of maintaining power system security in the recently deregulated environment is gigantic with uncertain and diverse power transactions and benefit based operational schemes. Monitoring of the system and predicting possible voltage collapses can be accomplished by defining suitable indices that can trigger the preventive or corrective control actions when predefined thresholds are reached to make the system insecure. This paper proposes a modified counterpropagation neural network (MCPN) enhanced by the concept of neo fuzzy neurons for static voltage security assessment. The proposed method works by identifying weak buses on the basis of available reactive margin and ranks them in order of their sensitivity to voltage collapse. A novel feature selection method is used to reduce the dimension and training time of the neural network. The proposed method has been tested on a practical 75-bus Indian system and is found to identify weak buses correctly even for previously unseen operating conditions, instantaneously.