Artificial Neural Network Based Contingency Ranking
Increased power demand without appropriate expansion of transmission lines has resulted in exploitation of the existing power transmission system. In view of this, the problem of voltage instability leading to voltage collapse is attracting more and more attention. Continuous monitoring of the system status through on-line contingency analysis and contingency ranking is therefore a necessary requirement. Due to its ability to learn off-line and produce accurate results on-line, Artificial Neural Network (ANN) is widely applied for on-line ranking of critical contingencies. Therefore this paper proposes an Artificial Neural Network based approach for fast voltage contingency ranking. The off-line load flow studies are adopted to find the Minimum Singular Value (MSV), which reflects the degree of severity of the contingencies in terms of voltage stability margin of the system, and the results from load flow study are used to train the multilayered ANN for estimating the MSV. The effectiveness of the proposed method is demonstrated by applying it to line outage contingency ranking under different loading conditions for a practical 22-bus Indian system. Once trained the neural network gives fast and accurate ranking for unknown patterns and classifies the contingency considered into groups in accordance to their severity based on the predicted value of MSV. The developed model is found suitable for on-line applications at load dispatch centers.
KeywordsArtificial Neural Networks Voltage Collapse Singular value decomposition Contingency Ranking
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- 7.Manjaree, P., Laxmi, S., Jaydev, S.: Fast voltage contingency selection using fuzzy parallel self-organizing hierarchical neural network. IEEE Trans. on Power Systems 18(2), 657–664 (2003)Google Scholar