Single and Multiobjective Optimal Reactive Power Dispatch Based on Hybrid Artificial Physics–Particle Swarm Optimization

Aljohani, Tawfiq M.; Ebrahim, Ahmed F.; Mohammed, Osama A.

doi:10.3390/en12122333

Cited by 49 publications

(28 citation statements)

References 35 publications

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“…The number of shunt devices in the IEEE 30-bus system has been increased for comparison purposes. Thus, the IEEE 30-bus system consists of six generators at buses 1, 2, 5, 8, 11, and 13, four tap-changing transformers at branches 6-9, 6-10, 4-12, and 27-28, and nine shunt compensators at 10,12,15,17,20,21,23,24, and 29. The total load on the system is 283.4 MW.…”

Section: Comparison With Existing Literature and Discussionmentioning

confidence: 99%

“…Many newly developed meta-heuristic algorithms like ant-lion optimizer [18], dragon fly optimization [19], hybrid particle swarm optimization-Tabu search (PSO-TS) [20], and hybrid artificial physics optimization-particle swarm optimization (APOPSO) [21] have been put forth to solve the RPD problem. Though all are found to be efficient methods to achieve the main objectives, none of them specifically calculate the discrete variables as discrete and integer variables as integer during the solution determination process.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Solution for Reactive Power Dispatch Problem Using Diversity-Enhanced Particle Swarm Optimization

Vishnu

2020

Energies

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Well-structured reactive power policies and dispatch are major concerns of operation and control technicians of any power system. Obtaining a suitable reactive power dispatch for any given load condition of the system is a prime duty of the system operator. It reduces loss of active power occurring during transmission by regulating reactive power control variables, thus boosting the voltage profile, enhancing the system security and power transfer capability, thereby attaining an improvement in overall system operation. The reactive power dispatch (RPD) problem being a mixed-integer discrete continuous (MIDC) problem demands the solution to contain all these variable types. This paper proposes a methodology to achieve an optimal and practically feasible solution to the RPD problem through the diversity-enhanced particle swarm optimization (DEPSO) technique. The suggested method is characterized by the calculation of the diversity of each particle from its mean position after every iteration. The movement of the particles is decided based on the calculated diversity, thereby preventing both local optima stagnation and haphazard unguided wandering. DEPSO accounts for the accuracy of the variables used in the RPD problem by providing discrete values and integer values compared to other algorithms, which provide all continuous values. The competency of the proposed method is tested on IEEE 14-, 30-, and 118-bus test systems. Simulation outcomes show that the proposed approach is feasible and efficient in attaining minimum active power losses and minimum voltage deviation from the reference. The results are compared to conventional particle swarm optimization (PSO) and JAYA algorithms.

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Section: Comparison With Existing Literature and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Solution for Reactive Power Dispatch Problem Using Diversity-Enhanced Particle Swarm Optimization

Vishnu

2020

Energies

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“…To eliminate the RPD problems, various optimization algorithms have been implemented over a period of time to achieve the optimal results. Some of the known stochastic methods implemented for solution of RPD problem includes the linear programming (LP) [ 7 ], interior point method (IPM) [ 8 ], quadratic programming (QP) [ 9 ], genetic algorithm (GA) [ 10 ], particle swarm optimization (PSO) [ 11 , 12 ], multi-objective optimization particle swarm optimization (MOPSO) algorithm [ 13 ], fractional Order PSO (FO-PSO) [ 6 ], harmony search algorithm (HSA) [ 14 ], gaussian bare-bones water cycle algorithm (NGBWCA) [ 15 ], tabu search (TS) [ 16 ], comprehensive learning particle swarm optimization [ 17 ], teaching learning based optimization (TLBO) [ 18 ], adaptive GA (AGA) [ 19 ], seeker optimization algorithm (SOA) [ 20 ], jaya algorithm [ 21 ], differential evolution (DE) [ 2 , 3 , 5 , 22 , 23 , 24 , 25 ], Artificial Bee Colony Algorithm [ 26 ], Hybrid Artificial Physics PSO [ 27 ], improved antlion optimization algorithm [ 28 ], Chaotic Bat Algorithm [ 29 ], classification-based Multi-objective evolutionary algorithm [ 30 ], evolution strategies (ES) [ 31 ], evolutionary programming (EP) [ 32 ], firefly algorithm (FA) [ 33 ], gravitation search optimization algorithm (GSA) [ 34 , 35 ], bacteria foraging optimization (BFO) [ 36 ], bio-geography-based optimization algorithm (BBO) [ 37 ] and grey wolf based optimizer algorithm (GWO) [ 38 ]. In 2017, another advanced optimizer has also been applied to the problems RPD known as gradient-based WCA (GWCA) [ 39 , 40 ] and results demonstrate the relevance and pr...…”

Section: Introductionmentioning

confidence: 99%

A New Fractional Particle Swarm Optimization with Entropy Diversity Based Velocity for Reactive Power Planning

Waleed

Yasir

Raja

et al. 2020

Entropy

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Optimal Reactive Power Dispatch (ORPD) is the vital concern of network operators in the planning and management of electrical systems to reduce the real and reactive losses of the transmission and distribution system in order to augment the overall efficiency of the electrical network. The principle objective of the ORPD problem is to explore the best setting of decision variables such as rating of the shunt capacitors, output voltage of the generators and tap setting of the transformers in order to diminish the line loss, and improve the voltage profile index (VPI) and operating cost minimization of standard electrical systems while keeping the variables within the allowable limits. This research study demonstrates a compelling transformative approach for resolving ORPD problems faced by the operators through exploiting the strength of the meta-heuristic optimization model based on a new fractional swarming strategy, namely fractional order (FO)–particle swarm optimization (PSO), with consideration of the entropy metric in the velocity update mechanism. To perceive ORPD for standard 30 and 57-bus networks, the complex nonlinear objective functions, including minimization of the system, VPI improvement and operating cost minimization, are constructed with emphasis on efficacy enhancement of the overall electrical system. Assessment of the results show that the proposed FO-PSO with entropy metric performs better than the other state of the art algorithms by means of improvement in VPI, operating cost and line loss minimization. The statistical outcomes in terms of quantile–quantile illustrations, probability plots, cumulative distribution function, box plots, histograms and minimum fitness evaluation in a set of autonomous trials validate the capability of the proposed optimization scheme and exhibit sufficiency and also vigor in resolving ORPD problems.

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“…Hybridization is a well-known strategy that boosts the capacity of optimization algorithms. Since a metaheuristic optimization algorithm cannot overcome all algorithms in solving any problem, hybridization can be a solution that merges the capabilities of different algorithms in one system [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52]. Many of these algorithms require the correct setting of control parameters, and merging several of these algorithms into a single solution increases the complexity of accurate adjustment of control parameters.…”

Section: Introductionmentioning

confidence: 99%

Settings-Free Hybrid Metaheuristic General Optimization Methods

et al. 2020

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Several population-based metaheuristic optimization algorithms have been proposed in the last decades, none of which are able either to outperform all existing algorithms or to solve all optimization problems according to the No Free Lunch (NFL) theorem. Many of these algorithms behave effectively, under a correct setting of the control parameter(s), when solving different engineering problems. The optimization behavior of these algorithms is boosted by applying various strategies, which include the hybridization technique and the use of chaotic maps instead of the pseudo-random number generators (PRNGs). The hybrid algorithms are suitable for a large number of engineering applications in which they behave more effectively than the thoroughbred optimization algorithms. However, they increase the difficulty of correctly setting control parameters, and sometimes they are designed to solve particular problems. This paper presents three hybridizations dubbed HYBPOP, HYBSUBPOP, and HYBIND of up to seven algorithms free of control parameters. Each hybrid proposal uses a different strategy to switch the algorithm charged with generating each new individual. These algorithms are Jaya, sine cosine algorithm (SCA), Rao’s algorithms, teaching-learning-based optimization (TLBO), and chaotic Jaya. The experimental results show that the proposed algorithms perform better than the original algorithms, which implies the optimal use of these algorithms according to the problem to be solved. One more advantage of the hybrid algorithms is that no prior process of control parameter tuning is needed.

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Single and Multiobjective Optimal Reactive Power Dispatch Based on Hybrid Artificial Physics–Particle Swarm Optimization

Cited by 49 publications

References 35 publications

An Improved Solution for Reactive Power Dispatch Problem Using Diversity-Enhanced Particle Swarm Optimization

An Improved Solution for Reactive Power Dispatch Problem Using Diversity-Enhanced Particle Swarm Optimization

A New Fractional Particle Swarm Optimization with Entropy Diversity Based Velocity for Reactive Power Planning

Settings-Free Hybrid Metaheuristic General Optimization Methods

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