Online Voltage Stability Monitoring and Contingency Ranking using RBF Neural Network

Voltage stability is one of the major concerns in competitive electricity markets. In this paper, RBF neural network is applied to predict the static voltage stability index and rank the critical line outage contingencies. Three distinct feature extraction algorithms are proposed to speedup the neural network training process via reducing the input training vectors dimensions. Based on the weak buses identification method, the first developed algorithm introduces a new feature extraction technique. The second and third algorithms are based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA) respectively which are statistical methods. These algorithms offer beneficial solutions for neural network training speed enhancement. In all presented algorithms, a clustering method is applied to reduce the number of neural network training vectors. The simulation results for the IEEE-30 bus test system demonstrate the effectiveness of the proposed algorithms for online voltage stability index prediction and contingency ranking.