Sectionalized ANN approach in predicting voltage stability in power systems
Document Type
Article
Publication Date
1-1-1999
Abstract
In recent years artificial neural networks (ANNs) have been proposed as an alternative method for solving certain difficult power system problems for which conventional techniques have not achieved the desired speed, accuracy, or efficiency. ANN methodology allows complex relationships between an initial state and a final state to be determined by an iterative mathematical algorithm, instead of by an expert. A properly trained ANN can classify the security of a previously unencountered input pattern with good accuracy. As systems grow in size and complexity, the mapping to be learned become increasingly complicated. In this paper the use of a sectionalized ANN approach is proposed for predicting the voltage stability index of a large-scale power system.
Keywords
Power system stability, Sectionalized artificial neural network, Voltage collapse indicator, Voltage stability, Algorithms, Backpropagation, Electric breakdown, Electric power systems, Indicators (instruments), Learning systems, Neural networks, System stability, Electric potential
Divisions
fac_eng
Publication Title
International Journal of Power and Energy Systems
Volume
19
Issue
1
Publisher
ACTA Press