Optimal reactive power dispatch using self-adaptive real coded genetic algorithm

Subbaraj, P.; Rajnarayanan, P. N.

doi:10.1016/j.epsr.2008.07.008

Cited by 239 publications

(89 citation statements)

References 29 publications

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“…The obtained optimal values of control variables, as obtained from the proposed OGSA method, are given in Table 2. The results obtained by the proposed OGSA are compared to those reported in the literature like GSA [20], biogeography-based optimization (BBO) [40], DE [16], comprehensive learning PSO (CLPSO) [41], PSO [41] and self-adaptive real coded GA (SARGA) [42]. The obtained minimum P Loss from the proposed approach is 4.4984 MW.…”

Section: Minimization Of System P Loss For Ieee 30-bus Power Systemmentioning

confidence: 84%

Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm

Shaw¹,

Mukherjee²,

Ghoshal³

2014

International Journal of Electrical Power & Energy Systems

185

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a b s t r a c tGravitational search algorithm (GSA) is based on law of gravity and the interaction between masses. In GSA, searcher agents are collection of masses and their interactions are based on Newtonian laws of gravity and motion. In this paper, to further improve the optimization performance of GSA, opposition-based learning is employed in opposition-based gravitational search algorithm (OGSA) for population initialization and also for generation jumping. In the present work, OGSA is applied for the solution of optimal reactive power dispatch (ORPD) of power systems. Traditionally, ORPD is defined as the minimization of active power transmission losses by controlling a number of control variables. ORPD is formulated as a non-linear constrained optimization problem with continuous and discrete variables. In this work, OGSA is used to find the settings of control variables such as generator voltages, tap positions of tap changing transformers and amount of reactive compensation to optimize certain objectives. The study is implemented on IEEE 30-, 57-and 118-bus test power systems with different objectives that reflect minimization of either active power loss or that of total voltage deviation or improvement of voltage stability index. The obtained results are compared to those yielded by the other evolutionary optimization techniques surfaced in the recent state-of-the-art literature including basic GSA. The results presented in this paper demonstrate the potential of the proposed approach and show its effectiveness and robustness for solving ORPD problems of power systems.

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Section: Minimization Of System P Loss For Ieee 30-bus Power Systemmentioning

confidence: 84%

Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm

Shaw¹,

Mukherjee²,

Ghoshal³

2014

International Journal of Electrical Power & Energy Systems

185

View full text Add to dashboard Cite

show abstract

“…While In [12], Minimize the losses using eq (2).The SARGA was tested on both IEEE 14 and IEEE 30 bus system to demonstrate the applicability and efficiency of this method. The result of minimization of losses compared with SARGA and evolutionary programming.…”

Section: Loss Minimizationmentioning

confidence: 99%

A Review on Optimal Reactive Power Dispatch

Loriya¹,

Patel²

2016

International Journal of Innovative Research in Electrical, Ele

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Abstract:A voltage profile at load busses can be kept with appropriate limits by adequate reactive power support. Reactive power also plays a key role in supporting the real power flow to maintain voltage stability and system reliability. It is difficult for transmission system operator to minimize the cost associated with optimal reactive power dispatch along with technical constraint. In recent years, several evolutionary computing based optimization techniques are proposed and practiced to solve various issues involved in optimal reactive power dispatch problem. The ORPD problem has been solved as a single objective as well as multi-objective optimization problem using various conventional techniques as well as recent techniques including genetic algorithm, artificial bee colony, particle swarm optimization and differential evolution etc. This paper is a attempt to understand various concept involved in reactive power dispatch and to present a different work carried out by the various researchers in this field by using evolutionary computing based techniques.

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“…converging in local solution, b. large iteration number, c. sensitivity to an initial search point, d. limited modeling capabilities (in handling nonlinear, discontinuous functions and constraints,…). These problems can be overcome by the introduction of intelligent techniques such as Neural networks [4], Fuzzy logic [5] and Evolutionary algorithms [6][7][8][9][10][11]. With the advancement of soft computing during the last years, many new stochastic search methods were developed for global optimization problems.…”

Section: Q DImentioning

confidence: 99%

“…Metaheuristics are stochastic algorithms for solving a wide range of problems for which there is no known effective conventional methods. These techniques are often inspired from biology (Evolutionary algorithms [6][7][8][9][10][11], Differential evolution [12][13][14][15]), physics (Simulated annealing [16][17][18], Gravitational search algorithm [19]) and ethnology [20][21][22][23][24].…”

Section: Q DImentioning

confidence: 99%

A Hybrid Formulation between Differential Evolution and Simulated Annealing Algorithms for Optimal Reactive Power Dispatch

Laouafi¹,

Boukadoum²,

Leulmi³

2018

TELKOMNIKA

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Optimal reactive power dispatch using self-adaptive real coded genetic algorithm

Cited by 239 publications

References 29 publications

Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm

Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm

A Review on Optimal Reactive Power Dispatch

A Hybrid Formulation between Differential Evolution and Simulated Annealing Algorithms for Optimal Reactive Power Dispatch

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