Chandragupta Mauryan KUPPAMUTHU SIVALINGAM, Subramanian RAMACHANDRAN, Purrnimaa Shiva Sakthi RAJAMANI

Reactive power optimization in a power system network through metaheuristic algorithms

Reactive power optimization (RPO) in a power system is a rudimentary necessity for the reduction of the loss of power. For the requirement of a unity power factor in the RPO system, the reduction of the system losses is ensured. The pivotal requirements of a power system are inclusive of a perfect compensation technique and methodology for stable reactive power compensation. The proposed concept in this paper utilizes the different reactive power optimization algorithms and performs a comparison. The process is accomplished by the use of IEEE 6-bus, 14-bus, and 30-bus systems to test the optimization technique. The conclusive information reinforces the outperformance of the based optimization algorithm to the other algorithm, thereby providing high stability to the system. The algorithm ensures the con nement of the voltage pro le of the system within the permissible limits.

PDF

___

[1] Meegahapola G, Vittal E, Keane A, Flynn D. Voltage security constrained reactive power optimization incorporating wind generation. IEEE Int Conf Pow Sys Tech 2012; 1-5.
[2] Bhattacharya A, Chattopadhyay PK. Solution of Optimal Reactive Power Flow using Biogeography-Based Opti- mization. International Journal of Electrical and Electronics Engineering 2010; 4: 621-629.
[3] Su H, Zhang P. Research of distribution network reactive power optimization based on improved cloud particle swarm optimization BP neural network. International Journal of Computer and Electrical Engineering 2013; 5: 164-168.
[4] Kennedy J, Eberhart R. Swarm Intelligence. 1st ed. San Francisco, CA, USA: Academic Press, 2001.
[5] Sujin PR, Prakash T, Mary Linda M. Particle swarm optimization based reactive power optimization. J Comp 2010; 2: 73-78.
[6] Platel MD, Schliebs S, Kasabov N. Quantum-inspired evolutionary algorithm: a multimodal EDA. IEEE T Evol Comp 2009; 13: 1218-1232.
[7] Bhattacharyya B, Gupta VK. Fuzzy based evolutionary algorithm for reactive power optimization with FACTS devices. Elec Pow Ener Sys 2014; 61: 39-47.
[8] Yuan G, Zhu L, Tong Yu. Reactive power optimization based on parallel immune particle swarm optimization. J Comp 2014; 9: 2198-2205.
[9] Muthu Vijaya Pandian S, Thanushkodi K. Considering transmission loss for an economic dispatch problem without valve-point loading using an EP-EPSO algorithm. Turk J Elec Eng & Comp Sci 2012; 20: 1259-1267.
[10] Sun L, Jian X, Yuan W. The application and practice of the voltage reactive power optimization automatic control system (AVC) in the power grid of Wuhu Region. International Journal of Online Engineering 2016; 12: 10-15.
[11] Zhao H, Luan Z, Han GC. Multi-objective reactive power optimization of distribution network with distributed generation. MATEC Web of Conf 2016; 1-6.
[12] Miller TJE. Reactive Power Control in Electric Systems. 1st ed. India, Wiley, 2012.
[13] Valle Y, GaneshKumar V, Mohagheghi S. Particle swarm optimization: basic concepts variants and applications in power systems. IEEE T Evol Comp 2008; 12: 171-194.
[14] Wei Z, Cui Z, Zeng J. Social cognitive optimization algorithm with reactive power optimization of power system. IEE Int Conf on Comp Asp of Soc Net 2010; 11-14.
[15] Kaur A, Goyal S. A survey on the applications of bee colony optimization techniques. International Journal of Computer and Electrical Engineering 2011; 3: 3037-3046.
[16] Karaboga D, Basturk B. Arti cial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems. IFSA Springer 2007; 4529: 789-798.
[17] Mahadevan K, Kannan PS. Comprehensive learning particle swarm optimization for reactive power dispatch. App Soft Comput 2010; 10: 641-652.
[18] Chandragupta Mauryan KS, Thanushkodi K. Reactive power optimization using quantum particle swarm optimiza- tion. J Comp Sci 2012; 8: 1644-1648.
[19] Chandragupta Mauryan KS, Thanushkodi K. Optimization of reactive power based on improved particle swarm algorithm. Euro J Sci Res 2012; 72: 608-617.