Exchange market algorithm based optimum reactive power dispatch

Rajan, Abhishek; Malakar, Tanmoy

doi:10.1016/j.asoc.2016.02.041

Cited by 79 publications

(59 citation statements)

References 29 publications

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“…As a subproblem of the optimal power flow calculation, it aims to minimize transmission losses or other concerned objective functions. In the past, computational intelligence-based techniques, such as seeker optimization algorithm [19], wo-point estimate method [20], teaching learning algorithm [21], PSO [22], differential evolution algorithm [23], oppositional krill herd algorithm [24], exchange market algorithm [25], and firefly algorithm [26] have been applied for solving ORPD problem. In [3], the PSO-imperialist competitive algorithm (PSO-ICA) is proposed and applied to the ORPD problem to minimize the total voltage deviation (TVD).…”

Section: Engineering Application Ii: Optimal Reactive Power Dispatch mentioning

confidence: 99%

A Hybrid Particle Swarm Optimization‐Cuckoo Search Algorithm and Its Engineering Applications

Ding

Wang

Zhang

et al. 2019

Mathematical Problems in Engineering

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This paper deals with the hybrid particle swarm optimization-Cuckoo Search (PSO-CS) algorithm which is capable of solving complicated nonlinear optimization problems. It combines the iterative scheme of the particle swarm optimization (PSO) algorithm and the searching strategy of the Cuckoo Search (CS) algorithm. Details of the PSO-CS algorithm are introduced; furthermore its effectiveness is validated by several mathematical test functions. It is shown that Lévy flight significantly influences the algorithm’s convergence process. In the second part of this paper, the proposed PSO-CS algorithm is applied to two different engineering problems. The first application is nonlinear parameter identification for the motor drive servo system. As a result, a precise nonlinear Hammerstein model is obtained. The second one is reactive power optimization for power systems, where the total loss of the researched IEEE 14-bus system is minimized using PSO-CS approach. Simulation and experimental results demonstrate that the hybrid optimal algorithm is capable of handling nonlinear optimization problems with multiconstraints and local optimal with better performance than PSO and CS algorithms.

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Section: Engineering Application Ii: Optimal Reactive Power Dispatch mentioning

confidence: 99%

A Hybrid Particle Swarm Optimization‐Cuckoo Search Algorithm and Its Engineering Applications

Ding

Wang

Zhang

et al. 2019

Mathematical Problems in Engineering

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“…Recently, many metaheuristic methods inspired from nature phenomenon or behavior of animals have been more widely and successfully applied for solving such ORPD problem. Many methods have been continually grown and become a big family of methods like the variants of genetic algorithm (GA) [15][16][17][18][19], variants of differential evolution (DE) [20][21][22][23][24], variants of particle swarm optimization (PSO) [25][26][27][28][29][30][31], variants of gravitational search algorithm (GSA) [32][33][34][35], and many new standard methods [36][37][38][39][40][41][42][43][44][45][46][47][48][49]. In adaptive genetic algorithm (AGA) [15], the method changed both mutation probability and crossover probability based on comparison of the maximum fitness value and average fitness value of the population to enhance global search quality and fast convergence speed.…”

Section: Complexitymentioning

confidence: 99%

Optimal Dispatch of Reactive Power Using Modified Stochastic Fractal Search Algorithm

Nguyen

Tran

et al. 2019

Complexity

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This paper applies a proposed modified stochastic fractal search algorithm (MSFS) for dealing with all constraints of optimal reactive power dispatch (ORPD) and finding optimal solutions for three different cases including power loss optimization, voltage deviation optimization, and L-index optimization. The proposed MSFS method is newly constructed in the paper by modifying three new solution update mechanisms on standard stochastic fractal search algorithm (SSFS). The first modification is to keep only one formula and abandon one formula in the diffusion process while the second modification and the third modification are used in the first update and the second update. In two updates of SSFS, solutions with low quality are updated with high probability while other solutions with high quality do not get chances to be updated. This manner results in the fact that some promising solutions around the high quality solutions can be missed. In order to tackle this restriction, the second modification of MSFS is to newly update the worst solutions in the first update and the best solutions in the second update. In the third modification, all existing formulas of SSFS in the two updates are abandoned and the same new proposed technique is used for updating such solutions in two updates. Compared to SSFS, the three modifications can bring advantages to MSFS such as using smaller number of produced solutions per iteration, spending shorter execution time, finding better optimal solutions, and owning more stable search ability. Furthermore, the proposed method also sees its effectiveness and robustness over SSFS by testing on IEEE 30-bus system and IEEE 118-bus system with three different single objectives for each system. The proposed method can find less minimum, average, and maximum for all the cases in addition to faster search speed. Besides, the proposed method is also compared to other methods such as PSO-based method group, GA-based method group, DE-based method group, and other recent methods. Result comparisons also indicate that the proposed method can be more efficient than almost all these methods with respect to less minimum and smaller values of control parameters. As a result, evaluation of the performance of the proposed method is that it should be used for seeking solutions of ORPD problem.

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“…ese methods have been successfully and widely applied to solving the ORPF problem, consisting many original methods, improve/modified methods, or combined/hybrid methods. ey have been constantly developed and have become a big method family such as particle swarm optimization (PSO) family [13][14][15][16][17], differential evolution (DE) family [18][19][20][21], and genetic algorithm (GA) family [22][23][24][25], while many standard methods have been also applied in [26][27][28][29][30][31][32][33][34]. Sahli et al [16] presented a combination between particle swarm optimization and tabu search (PSO-TS) by incorporating the best search function of PSO and TS.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the three above method family, other standard methodologies have been also applied for solving ORPF problem such as gravitational search algorithm (GSA) [26], ant lion optimizer (ALO) [27], quasi-oppositional teaching learning based optimization (QOTLBO) [28], teaching learning based optimization (TLBO) [28], Pooledneighbor swarm intelligence algorithm (PNSA) [29], hybrid Nelder-Mead simplex-based firefly algorithm (HFA-NMS) [30], chaotic krill herd algorithm (CKHA) [31], artificial bee colony algorithm (ABC) [32], exchange market algorithm (EMA) [33], backtracking search algorithm (BTSA) [34], and harmony search algorithm (HSA) [35]. In summary, all methods have demonstrated their qualification for addressing almost constraints of ORPF problem with acceptable solutions.…”

Section: Introductionmentioning

confidence: 99%

Optimal Reactive Power Flow for Large-Scale Power Systems Using an Effective Metaheuristic Algorithm

Duong

Phan

et al. 2020

Journal of Electrical and Computer Engineering

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In this paper, stochastic fractal search method (SFS) is employed for solving the optimal reactive power flow (ORPF) problem with a target of optimizing total active power losses (TPL), voltage deviation (VD), and voltage stability index (VSI). SFS is an effective metaheuristic algorithm consisting of diffusion process and two update processes. ORPF is a complex problem giving challenges to applied algorithms by taking into account many complex constraints such as operating voltage from generators and loads, active and reactive power generation of generators, limit of capacitors, apparent power limit from branches, and tap setting of transformers. For verifying the performance, solutions of IEEE 30 and 118-bus system with TPL, VD, and VSI objectives are found by the SFS method with different control parameter settings. Result comparisons indicate that SFS is more favorable than other methods about finding effective solutions and having faster speed. As a result, it is suggested that SFS should be used for ORPF problem, and modifications performed on SFS are encouraged for better results.

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Exchange market algorithm based optimum reactive power dispatch

Cited by 79 publications

References 29 publications

A Hybrid Particle Swarm Optimization‐Cuckoo Search Algorithm and Its Engineering Applications

A Hybrid Particle Swarm Optimization‐Cuckoo Search Algorithm and Its Engineering Applications

Optimal Dispatch of Reactive Power Using Modified Stochastic Fractal Search Algorithm

Optimal Reactive Power Flow for Large-Scale Power Systems Using an Effective Metaheuristic Algorithm

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