Large scale dynamic security screening and ranking using neural networks

This paper reports on the findings of a recently completed Canadian Electric Association (CEA) funded project [1] exploring the application of neural network to dynamic security contingency screening and ranking. The idea is to use the information on the prevailing operating condition and directly provide contingency screening and ranking using a trained neural network. To train the two neural networks for the large scale systems of B.C. Hydro and Hydro Quebec, in total 1691 derailed transient stability simulation were conducted, 1158 for B.C. Hydro system and 533 for the Hydro Quebec system. The simulation program was equipped with the Energy Margin Calculation Module (Second Kick) [4] to measure the energy margin in each run. The first set of results showed poor performance for the neural networks in assessing the dynamic security. However a number of corrective measures improved the results significantly. These corrective measures included : a) the effectiveness of output, b) the number of outputs, c) the type of features (static versus dynamic), d) the number of features, e) system partitioning and f) the ratio of training samples to features. The final results obtained using the large scale systems of B.C. Hydro and Hydro Quebec demonstrates a good potential for neural network in dynamic security assessment contingency screening and ranking.