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Radial Basis Function Networks for Fast Contingency Ranking
| Content Provider | Semantic Scholar |
|---|---|
| Author | Devaraj, D. Yegnanarayana, Bayya Ramar, K. |
| Abstract | This paper presents an arti®cial neural network-based approach for static-security assessment. The proposed approach uses radial basis function (RBF) networks to predict the system severity level following a given list of contingencies. The RBF networks are trained off-line to capture the nonlinear relationship between the pre-contingency line ¯ows and the post-contingency severity index. A method based on mutual information is proposed for selecting the input features of the networks. Mutual information has the advantage of measuring the general relationship between the independent variables and the dependent variable as against the linear relationship measured by the correlation-based methods. The performance of the proposed approach is demonstrated through contingency ranking in IEEE 30-bus test system. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://speech.iiit.ac.in/svlpubs/article/Devaraj2002387.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Algorithm Artificial neural network Backpropagation Biological Neural Networks Data point Feature selection Goto Iteration Loss function Memory-level parallelism Mutual information Neural Network Simulation Nonlinear system Online and offline Radial (radio) Radial basis function network Randomness |
| Content Type | Text |
| Resource Type | Article |
