Optimal reactive power dispatch based on harmony search algorithm

Khazali, Amirhossein; Kalantar, Mohsen

doi:10.1016/j.ijepes.2010.11.018

Cited by 282 publications

(122 citation statements)

References 28 publications

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“…The results are compared with the other heuristic methods reported in the literature and RPA demonstrated its effectiveness and robustness in reducing the real power loss. [23] 4.98 PSO (Zhao et al, 2005) [24] 4.9262 LP (Mahadevan et al, 2010) [25] 5.988 EP (Mahadevan et al, 2010) [25] 4.963 CGA (Mahadevan et al, 2010) [25] 4.980 AGA (Mahadevan et al, 2010) [25] 4.926 CLPSO (Mahadevan et al, 2010) [25] 4.7208 HSA (Khazali et al, 2011) [26] 4.7624 BB-BC (Sakthivel et al, 2013) [27] 4.690 MCS(Tejaswini sharma et al,2016) [28] 4.87231 Proposed RPA method 4.2876…”

Section: Discussionmentioning

confidence: 99%

Minimization of Real Power Loss by River Progression Algorithm

Lenin¹

2017

IJARCSSE

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Abstract-In this paper, a newmethodology, calledRiverprogression algorithm (RPA) used for solving reactive power problem. The strategy of the RPA as an optimization algorithm was an derivative from nature & after examining the whole riverprogression process which involves the flow of streams and rivers into the sea in the natural world.The proposed River progression algorithm (RPA) has been tested in standard IEEE 30 bus test system and simulation results shows clearly about the superior performance of the proposed algorithm in dropping the real power loss.Key words -Nature-inspired algorithm, Reactive Power, Transmission loss, voltage stability,RiverProgression Algorithm I. INTRODUCTIONThe main objective of reactive power dispatchproblem is to minimize the real power loss and bus voltage deviation by fulfilling a set of physical and working constraints imposed by apparatus limitations and security needs. Various mathematical techniqueslike the gradient method [1-2], Newton method [3] and linear programming [4][5][6][7] have been adopted to solve the optimal reactive power dispatch problem. Both the gradient and Newton methods has the difficulty in overseeing inequality constraints. If linear programming is applied then the input-output function has to be articulated as a set of linear functions which mostly lead to loss of correctness. The problem of voltage stability and collapse play a major role in power system planning and operation [8]. Global optimization has received extensive research alertness, and a great number of methods have been applied to solve this problem. Evolutionary algorithms such as PSO &genetic algorithm have been already designed to solve the reactive power flow problem [9,10].Evolutionary algorithm is a heuristic approach used for minimization problems by utilizing nonlinear and non-differentiable continuous space functions. In [11], Genetic algorithm has been used to solve optimal reactive power flow problem. In [12], Hybrid differential evolution algorithm is proposed to improve the voltage stability index. In [13] Biogeography Based algorithm is considered to solve the reactive power dispatch problem. In [14], afuzzy based method is used to solve the optimal reactive power scheduling method .In [15],an improved evolutionary programming is used to solvethe optimal reactive power dispatch problem. In [16], the optimal reactive power flow problem is solved by integrating a genetic algorithm with a nonlinearinterior point method. In [17], apattern algorithm is used to solve ac-dc optimal reactive powerflow model with the generator capability limits. In [18], proposes a two-step approach to evaluate Reactive power reserves with respect to operating constraints and voltage stability. In [19], a programming based proposed approach used to solve the optimal reactive power dispatch problem. In [20], presents aprobabilistic algorithm for optimal reactive power provisionin hybrid electricity markets with uncertain loads.In this work, a new optimization algorithm, recognized as the riverprogress...

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Section: Discussionmentioning

confidence: 99%

Minimization of Real Power Loss by River Progression Algorithm

Lenin¹

2017

IJARCSSE

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“…• Power system optimization Mukhopadhyay et al 2008;Fesanghary and Ardehali 2009;Coelho and Mariani 2009;Coelho et al 2010;Yas ßar and Özyön 2011;Fetanat et al 2011;Sivasubramani and Swarup 2011a, b;Khorram and Jaberipour 2011;Boroujeni et al 2011a, b, c, d;Sirjani et al 2011;Khazali and Kalantar 2011;Shariatkhah et al 2012;Ezhilarasi and Swarup 2012;Javadi et al 2012;Chatterjee et al 2012;Ameli et al 2012;Wang and Li 2013;Zhang et al 2013). …”

Section: Discussionmentioning

confidence: 99%

Music Inspired Algorithms

Xing

Gao²

2013

Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms

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In this chapter, we introduce a set of music inspired algorithms, namely, harmony search (HS), melody search (MeS) algorithm, and method of musical composition (MMC) algorithm. We first describe the general knowledge of harmony in Sect. 14.1. Then, the fundamentals and performances of HS, MeS algorithm, and MMC algorithm are introduced in Sects. 14.2 and 14.3, respectively. Finally, Sect. 14.4 summarises this chapter.

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“…Real power loss (Mw) [16] Simple genetic algorithm (SGA) 4.98 [17] Particle swarm optimization (PSO) 4.93 [18] Linear programming (LP) 5.99 [18] Expectation propagation (EP) 4.96 [18] Conjugate gradient (CG) 4.98 [18] Applied geometric algorithm (AGA) 4.92 [18] Comprehensive learning (CL)-PSO 4.72 [19] Harmony search algorithm (HAS) 4.76 [20] Big bang-big crunch (BB-BC) 4.69 Proposed EDE 4.29…”

Section: Techniquesmentioning

confidence: 99%