Low voltage risk assessment in power system using neural network ensemble

Authors:
Wei-Hua Chen;Quan-Yuan Jiang;Yi-Jia Cao
Affiliations:
College of Electrical Engineering, Zhejiang University, Hangzhou, China;College of Electrical Engineering, Zhejiang University, Hangzhou, China;College of Electrical Engineering, Zhejiang University, Hangzhou, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 2
Cited 0

Neural Network Ensembles

IEEE Transactions on Pattern Analysis and Machine Intelligence
Cooperative coevolution of artificial neural network ensembles for pattern classification

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Static voltage security is one of the important items of power system security. This paper provides an approach to calculate risk of low voltage in power system using neural network ensemble. Risk is defined as a condition under which there is a possibility of an adverse deviation from a desired outcome that is expected or hoped for. Risk index is used as an indicator of the low voltage security. It is calculated as the product of the probability of contingency and the impact of low voltage. Neural network ensemble (NNE) is used for the low voltage risk assessment to get the desired speed, accuracy and efficiency. The New England 39-bus test system is used as an example to demonstrate the efficiency of the proposed algorithm.