Dynamic security contingency screening and ranking using neural networks

Abstract

This paper summarizes BC Hydro's experience in applying neural networks 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 BC Hydro and Hydro Quebec, in total 1691 detailed transient stability simulation were conducted, 1158 for BC Hydro system and 533 for the Hydro Quebec system. The simulation program was equipped with the energy margin calculation module (second kick) 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: 1) the effectiveness of output; 2) the number of outputs; 3) the type of features (static versus dynamic); 4) the number of features; 5) system partitioning; and 6) the ratio of training samples to features. The final results obtained using the large scale systems of BC Hydro and Hydro Quebec demonstrates a good potential for neural network in dynamic security assessment contingency screening and ranking.

Power system security assessment using neural networks: feature selection using Fisher discrimination

“…Neural networks have shown great promise as a means of predicting the security of large electric power systems [1]- [3]. Neural networks offer several advantages over traditional techniques including the ability to learn from examples.…”

“…A common approach is to simulate the system in response to various disturbances and then collect a set of pre-disturbance system features along with the corresponding system security index. Possible security indices include the Critical Clearing Time (CCT) [1] and the system Energy Margin (EM) [3]. One of the most important aspects of achieving good neural network performance has proven to be the proper selection of training features.…”

Redes Neurais Artificiais: Uma Aplicação ao Planejamento da Operação de Sistemas Hidrotérmicos de Geração

“…As redes neurais artificíais (RNA) tipo MLP tem sido largamente utilizadas como ferramenta de solução em diversos problemas da engenharia elétrica como vê-se nas referências [6], [7], [8], [9] contudo, ainda não ao planejamento de sistemas de geração hidrotérmicos.…”

Transient Stability Assessment of Power System Based on Support Vector Machine

“…[2] summarized experiences in applying artificial neural network (ANN) to TSA. ANN requires long-time training process and the selection of the number of neurons in hidden layer is usually determined by a trial.…”