@article {10.3844/jcssp.2012.1644.1648,
article_type = {journal},
title = {Reactive Power Optimization Using Quantum Particle Swarm Optimization},
author = {Mauryan, K. S. Chandragupta and Thanushkodi, K. and Sakthisuganya, A.},
volume = {8},
number = {10},
year = {2012},
month = {Aug},
pages = {1644-1648},
doi = {10.3844/jcssp.2012.1644.1648},
url = {https://thescipub.com/abstract/jcssp.2012.1644.1648},
abstract = {Problem statement: The problem of controlling a power system is not an easy task; it is subjected to various constraints. There are at risks of voltage instability problems due to highly stressed operating conditions caused by increased load demand and other constraints in the power system network. Approach: This study presents the implementation of Quantum Particle Swarm Optimization (QPSO) in solving the Reactive Power Optimization (RPO) problem. The main aim of this algorithm is the minimization of the real power loss and to improvise  the voltage in the system. In this new algorithm, the particles were made to perform studies on itself and also the best ones in the system. Results: The implementations of QPSO were carried on modified IEEE 14 bus system for obtaining solution to the reactive power optimization and the output results are found predominant with classical PSO. Conclusion: This technique is used to find the best solution and also the convergence time is reduced. The proposed QPSO  method is demonstrated and results are compared with traditional optimization methods.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}