Ant colony optimization algorithm based optimal reactive power dispatch to improve voltage stability

Rayudu, K.; Yesuratnam, G.; Jayalaxmi, A.

doi:10.1109/iccpct.2017.8074391

Cited by 13 publications

(8 citation statements)

References 12 publications

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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%

“…In addition to the presence of the three largest groups, some small groups have been also introduced to tackle ORPD problem such as the gravitational search algorithm (GSA) [32][33][34], improved GSA with feasible conditional selection strategies (IGSA-FCSS) [35], quasi-oppositional teaching learning based optimization (QOTLBO) [36], teaching learning based optimization (TLBO) [36], modified Gaussian barebones based TLBO (MGBTLBO) [37], and Gaussian barebones based TLBO (GBTLBO) [37]. From 2015 to 2017, a high number of methods were employed for ORPD problem such as the hybrid Nelder-Mead simplex based firefly algorithm (HNMS-FA) [38], Artificial Bee Colony Algorithm (ABC) [39], differential search algorithm (DSA) [40], exchange market algorithm (EMA) [41], chaotic krill herd algorithm (CKHA) [42], gray wolf optimizer (GWO) [43], Gaussian barebones water cycle algorithm (GBBWCA) [44], ant lion optimizer (ALO) [45], moth-flame optimization technique (MFOT) [46], whale optimization algorithm (WOA) [47], Ant Colony Optimization Algorithm (ACOA) [48], and backtracking search algorithm (BTSA) [49]. All in all, most of these methods had a strong search ability and outperformed deterministic algorithms, original metaheuristic algorithms in terms of solution quality, computing time, and convergence speed.…”

Section: Complexitymentioning

confidence: 99%

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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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Section: Complexitymentioning

confidence: 99%

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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“…Not only are there the above methods, but there are a lot of other methods that are used to solve the ORPD problem through various systems and techniques, with a single objective or multiple objectives. These methods are improved, such as the gravitational search algorithm (GSA) [34][35][36], the exchange market optimization algorithm (EMOA) [37], the artificial bee colony (ABC) with firefly algorithm (ABC-FF) [38], the ant lion optimizer (ALO) [39], moth flame optimization (MFO) [40], the cuckoo search optimization algorithm (CSOA) [41], the differential search algorithm (DSA) [42], the multi-objective grey wolf algorithm (MOGWA) [43], improved colliding bodies optimization (ICBO) [44], the Jaya algorithm (JA) [45], the whale optimization algorithm (WOA) [46], ant colony optimization (ACO) [47], the harmony search algorithm (HAS) [48], Gaussian bare-bones teachinglearning-based optimization (GBTLBO) [49], the hybrid Nelder-Mead simplex-based firefly algorithm (HFA-NMS) [50], the Gaussian bare-bones water cycle algorithm (GBBWCA) [51], the gray wolf optimizer (GWO) [52], the cuckoo search algorithm (CSA) [53], the chaotic krill herd algorithm (CKHA) [54], ABC [55], quasi-oppositional teaching-learning-based optimization (QOTLBO) and TLBO [2], the Rao-3 algorithm [56], and the improved Cuckoo search algorithm (ICSA) [57]. Among these methods, there are methods that have improved upon the original methods to find more promising solutions than those of the original methods for the ORPD problem.…”

Section: Introductionmentioning

confidence: 99%

Optimal Reactive Power Dispatch Using a Chaotic Turbulent Flow of Water-Based Optimization Algorithm

et al. 2022

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In this study, an optimization algorithm called chaotic turbulent flow of water-based optimization (CTFWO) algorithm is proposed to find the optimal solution for the optimal reactive power dispatch (ORPD) problem. The ORPD is formulated as a complicated, mixed-integer nonlinear optimization problem, comprising control variables which are discrete and continuous. The CTFWO algorithm is used to minimize voltage deviation (VD) and real power loss (P_loss) for IEEE 30-bus and IEEE 57-bus power systems. These goals can be achieved by obtaining the optimized voltage values of the generator, the transformer tap changing positions, and the reactive compensation. In order to evaluate the ability of the proposed algorithm to obtain ORPD problem solutions, the results of the proposed CTFWO algorithm are compared with different algorithms, including artificial ecosystem-based optimization (AEO), the equilibrium optimizer (EO), the gradient-based optimizer (GBO), and the original turbulent flow of water-based optimization (TFWO) algorithm. These are also compared with the results of the evaluated performance of various methods that are used in many recent papers. The experimental results show that the proposed CTFWO algorithm has superior performance, and is competitive with many state-of-the-art algorithms outlined in some of the recent studies in terms of solution accuracy, convergence rate, and stability.

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“…PSO was further improved by several researchers where authors in [9] has implemented modified Evolutionary PSO (EPSO) to solve optimal reactive power dispatch problem while authors in [10] has modified the acceleration coefficient of PSO technique which then produce a technique known as PSO with time varying acceleration coefficients (PSO-TVAC). Aside of the mentioned techniques, several other techniques which has been implemented to solve optimal reactive power dispatch problem are Artificial Bee Colony [11], Cuckoo Search Algorithm [12] and Ant Colony Optimization [13].…”

Section: Introductionmentioning

confidence: 99%

Gravitational Search Algorithm Based Technique for Voltage Stability Improvement

Zamani¹,

Musirin²,

Omar³

et al. 2018

IJEECS

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<p>Voltage instability problem has been known as a significant threat to power system operation since its occurrence can lead to power interruption. This phenomenon can be due to uncontrollable load increment, line and generator outage contingencies or unplanned load curtailment. Optimal reactive power dispatch involving reactive power support can be one of the options for improving voltage stability of a power system, which also requires optimization process. Optimal sizing and location can of reactive power support can avoid the system from experiencing over-compensated or under-compensated phenomena. The presence of optimization techniques has helped solving non-optimal phenomenon, nevertheless some setbacks have also been experienced in terms of inaccuracy and stuck in local optima. This paper presents the application of Gravitational Search Algorithm (GSA) technique in attempt to solve optimal reactive power dispatch problem in terms of reactive power support for voltage stability improvement. Optimization process tested on IEEE 14-bus Reliability Test System (RTS) has revealed its superiority with significant promising results in terms of voltage stability improvement in the test system. </p>

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Ant colony optimization algorithm based optimal reactive power dispatch to improve voltage stability

Cited by 13 publications

References 12 publications

Optimal Dispatch of Reactive Power Using Modified Stochastic Fractal Search Algorithm

Optimal Dispatch of Reactive Power Using Modified Stochastic Fractal Search Algorithm

Optimal Reactive Power Dispatch Using a Chaotic Turbulent Flow of Water-Based Optimization Algorithm

Gravitational Search Algorithm Based Technique for Voltage Stability Improvement

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