2021
DOI: 10.1002/oca.2815
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Reactive power management by using a modified differential evolution algorithm

Abstract: In this article, a modified differential evolution (MDE) algorithm is proposed and applied to provide the solution for reactive power management by incorporating the flexible alternating current transmission systems (FACTS) controllers. The proper siting of FACTS controller has been achieved with an objective to minimize the losses and to improve the loading capability. The power flow analysis is performed to determine the optimal position for FACTS controllers. These controllers are incorporated in the most h… Show more

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Cited by 30 publications
(12 citation statements)
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“…The proposed strategy had a poor performance in terms of settling time. Liu et al in [11] suggested a H ∞ -μ synthesis-based control strategy to minimize the frequency deviation for an isolated MG. Because of the complexity of this approach, a numbers of optimization algorithms have been proposed by researchers, including GA [12], PSO [13], BOA algorithm [14] and DE [15] to address the LFC problem.…”
Section: Introductionmentioning
confidence: 99%
“…The proposed strategy had a poor performance in terms of settling time. Liu et al in [11] suggested a H ∞ -μ synthesis-based control strategy to minimize the frequency deviation for an isolated MG. Because of the complexity of this approach, a numbers of optimization algorithms have been proposed by researchers, including GA [12], PSO [13], BOA algorithm [14] and DE [15] to address the LFC problem.…”
Section: Introductionmentioning
confidence: 99%
“…Because metaheuristic methods are stochastic in nature, therefore, statistical analysis on the generated data is required to draw definitive conclusions. As a result, WSRT [41,42] is applied to the obtained results (at a 95% confidence level) to identify the inferiority (−), superiority (+), or equivalency ( ≈ ) of a technique in contrast to the suggested MGWOA approach. Table 3 presents the WSRT results.…”
Section: Performance Analysis Of Mgwoa Approachmentioning
confidence: 99%
“…Intelligent optimization algorithms are inspired by biological intelligence or natural phenomena that the behavior of some social species in nature, such as foraging and reproduction, is simulated and abstracted into quantifiable key indicators of mathematical models, and can deal with combinatorial and complex optimization problems. [7][8][9] Intelligent algorithms can be specified into four categories: nature-like optimization, evolutionary algorithm, plant growth simulation algorithm, and swarm intelligence optimization.…”
Section: Introductionmentioning
confidence: 99%