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Study of differential evolution for optimal reactive power flow

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TLDR
Differential evolution is studied in detail for optimal reactive power flow (ORPF) problems and it is found that DE is generally a good algorithm for ORPF and worthy of more attention, however, it is also found that it requires relatively large populations to avoid premature convergence.
Abstract
Differential evolution (DE) is studied in detail for optimal reactive power flow (ORPF) problems. The concept, mechanism, and parameter setting of DE are discussed. Based on the IEEE 14-, 30- and 57-bus system test cases, DE is compared with some basic or improved evolutionary algorithms that have been applied to ORPF. It is found that DE is generally a good algorithm for ORPF and worthy of more attention. However, it is also found that DE requires relatively large populations to avoid premature convergence. The impact of this shortcoming is made clear in the IEEE 118-bus system test case. The effectiveness of parallel computing technology for speeding up the computation of DE-based ORPF is also analysed.

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Citations
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Journal ArticleDOI

Optimal power flow: a bibliographic survey I

TL;DR: Optimal power flow (OPF) has become one of the most important and widely studied nonlinear optimization problems as mentioned in this paper, and there is an extremely wide variety of OPF formulations and solution methods.
Journal ArticleDOI

Optimal power flow using differential evolution algorithm

TL;DR: In this paper, an evolutionary-based approach to solve the optimal power flow (OPF) problem is presented, which employs differential evolution algorithm for optimal settings of OPF problem control variables.
Journal ArticleDOI

Optimal power flow: A bibliographic survey II non-deterministic and hybrid methods

TL;DR: Optimal power flow (OPF) has become one of the most important and widely studied nonlinear optimization problems as discussed by the authors, and there is an extremely wide variety of OPF formulations and solution methods.
Journal ArticleDOI

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

TL;DR: Self-adaptive real coded genetic algorithm (SARGA) is used as one of the techniques to solve optimal reactive power dispatch (ORPD) problem and the performance of the proposed method is compared with evolutionary programming (EP), previous approaches reported in the literature.
Journal ArticleDOI

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

TL;DR: In this article, an opposition-based gravitational search algorithm (OGSA) is applied for the solution of optimal reactive power dispatch (ORPD) of power systems, which is defined as the minimization of active power transmission losses by controlling a number of control variables such as generator voltages, tap positions of tap changing transformers and amount of reactive compensation.
References
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Journal ArticleDOI

Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Book

New Ideas In Optimization

TL;DR: The techniques treated in this text represent research as elucidated by the leaders in the field and are applied to real problems, such as hilllclimbing, simulated annealing, and tabu search.
Journal ArticleDOI

A particle swarm optimization for reactive power and voltage control considering voltage security assessment

TL;DR: In this article, a particle swarm optimization (PSO) for reactive power and voltage control (volt/VAr control: VVC) considering voltage security assessment (VSA) is presented.
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Parallelism and evolutionary algorithms

TL;DR: A modern vision of the parallelization techniques used for evolutionary algorithms (EAs) and provides a highly structured background relating to PEAs to make researchers aware of the benefits of decentralizing and parallelizing an EA.
Journal ArticleDOI

Reactive power optimization by genetic algorithm

TL;DR: The proposed method was applied to practical 51-bus and 224-bus systems to show its feasibility and capabilities and the concept is quite promising and useful in the coming computer age.
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