@article {10.3844/ajassp.2013.779.786,
article_type = {journal},
title = {Adaptive Neuro-Fuzzy Inference System-Particle Swarm Optimization Based Stability Maintenance of Power System Networks},
author = {Latha, R. and Kanakaraj, J.},
volume = {10},
year = {2013},
month = {Jul},
pages = {779-786},
doi = {10.3844/ajassp.2013.779.786},
url = {https://thescipub.com/abstract/ajassp.2013.779.786},
abstract = {During faulty condition voltage instability is one of the major crisis in power system networks. This study proposes a hybrid learning algorithm to improve the stability performance of a power system with Distributed Generations (DGs). Here the distribution system stability is maintained with reduced power loss using an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Particle Swarm Optimization (PSO) techniques. In this study distributed generations is considered as several types of DGS connected together which is called as Microgrid (MG). Initially ANFIS is trained by instability parameters to give the optimal power capacity of the microgrid and then PSO algorithm is applied to find the optimum bus for connecting microgrid in the system. The effective improvements in voltage profile and reduction in power loss of the proposed ANFIS-PSO controller is tested on IEEE-30 bus system and has been presented with few comparative results.},
journal = {American Journal of Applied Sciences},
publisher = {Science Publications}
}