Loadability margin calculation of power system with SVC using artificial neural network

Voltage stability has become a major concern among the utilities over the past decade. With the development of FACTS devices, there is a growing interest in using these devices to improve the stability. In this paper a method using parallel self-organizing hierarchical neural network (PSHNN) is proposed to estimate the loadability margin of the power system with static var compensator (SVC). Limits on reactive generations are considered. Real and reactive power injections along with firing angle of SVC and bus voltage at which SVC is connected, are taken as input features. To improve the performance of network, K-means clustering is employed to form the clusters of patterns having similar loadability margin. To reduce the number of input features in each cluster, system entropy information gain method is used and only those real and reactive power injections, which affect the loadability margin most, are selected. Separate PSHNN is trained for each cluster. The proposed method is implemented on IEEE-30 bus and IEEE-118 bus system. Once trained, the network produces the output, with accuracy and speed. The computation time is also independent of the system size and the load pattern.