Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints

Chen, Gonggui; Liu, Lilan; Zhang, Zhizhong; Huang, Shanwai

doi:10.1016/j.asoc.2016.11.008

Cited by 118 publications

(68 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Table compares the obtained results by the proposed algorithm QPSODM for Case (Deterministic approach for 14 bus test) with some previously published results on MO‐ORPD problem. According to this table, the proposed algorithm can achieve better and logical results comparing with other methods reported in . In fact, the comparison of the results depends not only on the algorithms used but also on the introduced limit values (generator voltage, OLTC and shunt compensator value), the constraints handling method.…”

Section: Case Study Simulation Results and Discussionmentioning

confidence: 86%

“…In order to compare our results obtained with the test system (IEEE 14‐bus) used in simulation with those of other references, some recent algorithms published in the literature were employed in this context, namely: PSO, Whale optimisation algorithm (WOA), ALO and CSA, PSO, DE, Interior point method (IPM), PSO with cauchy mutation (PSO‐CM) and adaptive mutation (PSO‐AM), Modified DE (MDE), Artificial bee colony (ABC) and (DE–ABC), Ant colony optimization (ACO), Modified Gaussian barebones teaching learning‐based optimization (MGBTLBO), Gravitational search algorithm (GSA), GSA with conditional selection strategies (GSA‐CSS), improved GSA‐CSS (IGSA‐CSS), GAMS, WOA . To the best of the authors' knowledge, no literature has carried out on stochastic ORPD using quantum‐behaved particle swarm optimization differential mutation (QPSODM) algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stochastic multi‐objective optimal reactive power dispatch considering load and renewable energy sources uncertainties: a case study of the Adrar isolated power system

Naidji

Boudour

2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Optimal reactive power dispatch (ORPD) is a particular case of the optimal power flow (OPF) which consists in determining the state of an electric power system by optimizing a specific objective function and satisfying a set of some operating constraints. In this paper, the purpose is to solve deterministic and stochastic multi‐objective ORPD (MO‐ORPD) problem under load and renewable energy sources (RES) uncertainties. The uncertainty is modelled using stochastic scenario‐based approach (SSBA). The objectives to be minimized are active power loss and cumulative voltage deviation from their corresponding nominal values. The MO‐ORPD is solved using sum weighed method, and fuzzy satisfying method is used to select the best compromise solution among Pareto front of non‐dominated solutions. In this paper, quantum‐behaved particle swarm optimization differential mutation (QPSODM) algorithm is proposed to solve the ORPD problem. The proposed methodology has been examined and confirmed on the IEEE 14‐bus and the practical Adrar's isolated power system. The performance of the proposed methodology is compared with recent algorithms. Simulation results show that the proposed methodology can solve the MO‐ORPD including RES effectively and can give best and logic results. Furthermore, a sensitivity analysis is carried out to show the performance of the proposed algorithm comparing to own developed algorithms particle swarm optimization (PSO) and quantum PSO (QPSO).

show abstract

Section: Case Study Simulation Results and Discussionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Stochastic multi‐objective optimal reactive power dispatch considering load and renewable energy sources uncertainties: a case study of the Adrar isolated power system

Naidji

Boudour

2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…V i is the voltage magnitude at bus i. It is noted that the equality constraints are satisfied because they are considered as the termination conditions when calculating Jacobian matrix in Newton Raphson load flow calculation [11].…”

Section: Equality Constraintmentioning

confidence: 99%

“…Therefore, to a certain extent, the traditional methods cannot solve the OPF problem successfully. With the development of computers, the intelligent optimization methods based on the combination of computer technology and biological simulation are proposed to overcome many drawbacks of the traditional methods; and these heuristic algorithms include artificial bee colony algorithm (ABC) [7], particle swarm optimization (PSO) [8], differential search algorithm (DSA) [9], differential evolution algorithm (DE) [10], gravitational search algorithm (GSA) [11], etc. Numerous studies indicate that each algorithm has different performance, pros and cons in different cases, so there are more and more modified methods based on intelligent algorithms to solve the OPF problem effectively.…”

Section: Introductionmentioning

confidence: 99%

Improved Krill Herd Algorithm with Novel Constraint Handling Method for Solving Optimal Power Flow Problems

Chen

Zhang

2018

Energies

Self Cite

View full text Add to dashboard Cite

Abstract:As one of the most important tools used in operation and planning of power systems, the optimal power flow (OPF) problem considering the economy and security is large-scale, complex and hard to solve. In this paper, an improved krill herd algorithm (IKHA) has been proposed. In IKHA, the onlooker search mechanism is introduced to reduce the probability of falling into local optimum; and the parameter values of the proposed algorithm including inertia weight and step-length scale factor are varied according to the iteration of evolutionary process, which improves the exploration and exploitation capabilities. Moreover, a novel constraint handling method is proposed to guide the individual to the feasible space and ensure that the optimal solution satisfies the security constraints. Then, IKHA is combined with the novel constraint handling method to solve the multi-constrained OPF problem, and its performance is tested on the IEEE 30 bus, IEEE 57 bus and IEEE 118 bus systems for 10 different simulation cases containing linear and non-linear objective functions. The simulation results demonstrate that the proposed method can solve the OPF problem successfully and obtain better solutions compared with other methods reported in the recent literatures, which prove the feasibility and effectiveness of the improvements in this work.

show abstract

Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints

Cited by 118 publications

References 26 publications

Stochastic multi‐objective optimal reactive power dispatch considering load and renewable energy sources uncertainties: a case study of the Adrar isolated power system

Stochastic multi‐objective optimal reactive power dispatch considering load and renewable energy sources uncertainties: a case study of the Adrar isolated power system

Optimal Dispatch of Reactive Power Using Modified Stochastic Fractal Search Algorithm

Improved Krill Herd Algorithm with Novel Constraint Handling Method for Solving Optimal Power Flow Problems

Contact Info

Product

Resources

About