Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems

Heidari, Ali Asghar; Abbaspour, Rahim Ali; Jordehi, Ahmad Rezaee

doi:10.1016/j.asoc.2017.04.048

Cited by 154 publications

(84 citation statements)

References 80 publications

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“…The main objective of GWO and WCA is the optimal allocation of capacitor in RDNs to optimize (minimize) the cost function such that associated cost of energy and capacitor will reduce. In the present paper, two objective functions are considered for the simulation study of GWO and WCA.…”

Section: Mathematical Problem Formulationmentioning

confidence: 99%

Optimal capacitor allocation in distribution networks for minimization of power loss and overall cost using water cycle algorithm and grey wolf optimizer

Sampangi

Jayabarathi

2020

Int Trans Electr Energ Syst

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Summary In recent years, reactive power compensation techniques are receiving more attention across the globe from both the distribution network operators and research groups due to its ability in the reduction of network power loss, voltage deviation, and enhancement of voltage stability. In this paper, two novel and robust optimization techniques implemented for solving optimal capacitor (OC) allocation problem in various distribution networks are water cycle algorithm (WCA) and grey wolf optimizer (GWO). To crisscross the feasibility, GWO and WCA techniques are tested on practical Indian 28‐bus, 47‐bus, and 52‐bus and standard 33‐bus, 69‐bus, and 85‐bus radial distribution networks with uniformly varying load levels. The simulation results obtained by WCA and GWO are compared with other techniques like inclusion and interchange variable (IIV), direct search algorithm (DSA), and teaching and learning–based optimization (TLBO) and found to be better. In addition, from the obtained results, the algorithm's efficiency is evaluated in terms of exploration and exploitation, and convergence characteristics and computational time are discussed.

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Section: Mathematical Problem Formulationmentioning

confidence: 99%

Optimal capacitor allocation in distribution networks for minimization of power loss and overall cost using water cycle algorithm and grey wolf optimizer

Sampangi

Jayabarathi

2020

Int Trans Electr Energ Syst

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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%

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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“…In addition to the implementation of popular methods like DE, PSO and GA for solving the ORPD problem, other methods have been applied such as seeker optimization algorithm (SOA) [14], harmony search algorithm (HSA) [15], gravitational search algorithm (GSA) [16], quasi-oppositional teaching learning based optimization algorithm (QOTLBOA) [17], chaotic krill herd algorithm (CKHA) [18], antlion optimizer (ALO) [19], multi-objective antlion optimization (MOALO) [20], differential search algorithm (DSA) [21], integrated strategies of Backtracking search (BSO) [22], whale optimization algorithm (WOA) [23], Gaussian bare-bones water cycle algorithm (GBWCA) [24], multi-objective grey wolf optimizer (MOGWA) [25], modified colliding bodies optimization algorithm (MCBOA) [26], moth-flame optimization method (MFOM) [27], Jaya optimization algorithm (JAYA) [28], social spider optimization algorithm (SSO) [29], modified social spider optimization algorithm (MSSO) [29], improved sine cosine algorithm (ISCA) [30], enhanced Jaya optimization technique (EJOT) [31] and adaptive chaotic symbiotic organisms search (ACSOSM) [32]. These methods were proposed for determining the perfect control variables in order to optimize objective functions.…”

Section: Introductionmentioning

confidence: 99%

Finding Solutions for Optimal Reactive Power Dispatch Problem by a Novel Improved Antlion Optimization Algorithm

Cao

Đại

et al. 2019

Energies

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In this paper, a novel improved Antlion optimization algorithm (IALO) has been proposed for solving three different IEEE power systems of optimal reactive power dispatch (ORPD) problem. Such three power systems with a set of constraints in transmission power networks such as voltage limitation of all buses, limitations of tap of all transformers, maximum power transmission limitation of all conductors and limitations of all capacitor banks have given a big challenge for global optimal solution search ability of the proposed method. The proposed IALO method has been developed by modifying new solution generation technique of standard antlion optimization algorithm (ALO). By optimizing three single objective functions of systems with 30, 57 and 118 buses, the proposed method has been demonstrated to be more effective than ALO in terms of the most optimal solution search ability, solution search speed and search stabilization. In addition, the proposed method has also been compared to other existing methods and it has obtained better results than approximately all compared ones. Consequently, the proposed IALO method is deserving of a potential optimization tool for solving ORPD problem and other optimization problems in power system optimization fields.

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Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems

Cited by 154 publications

References 80 publications

Optimal capacitor allocation in distribution networks for minimization of power loss and overall cost using water cycle algorithm and grey wolf optimizer

Optimal capacitor allocation in distribution networks for minimization of power loss and overall cost using water cycle algorithm and grey wolf optimizer

Optimal Dispatch of Reactive Power Using Modified Stochastic Fractal Search Algorithm

Finding Solutions for Optimal Reactive Power Dispatch Problem by a Novel Improved Antlion Optimization Algorithm

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