Journal ArticleDOI
Study of differential evolution for optimal reactive power flow
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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.read more
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
P. Subbaraj,P.N. Rajnarayanan +1 more
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
Rainer Storn,Kenneth Price +1 more
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
David Corne,Marco Dorigo,Fred Glover,Dipankar Dasgupta,Pablo Moscato,Riccardo Poli,Kenneth V. Price +6 more
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.
Journal ArticleDOI
Parallelism and evolutionary algorithms
Enrique Alba,Marco Tomassini +1 more
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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