Optimal Power Flow Solution using Efficient Sine Cosine Optimization Algorithm

Messaoudi, Abdelmoumene; Belkacemi, M.

doi:10.5815/ijisa.2020.02.04

Cited by 3 publications

(5 citation statements)

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“…The Author(s). Published by CBIORE 2018), modified Jaya algorithm with the adaptive factors (MJAYA) (Warid 2020), Marine predator algorithm (MPA) (Farhat et al, 2022), Gorilla troop algorithm (GTA) (Ginidi et al, 2022), Ant lion optimization (ALO) (Tiwari et al, 2020), Slime mould algorithm (SMA) (Khunkitti et al, 2021), Social spider algorithm (SSA) (Nguyen 2019), Krill herd algorithm (KHA) (Abdollahi et al, 2020), and Sine cosine optimization algorithm (SCOA) (Messaoudi et al, 2020). These studies have shown different results obtained by different metaheuristic algorithms for some tests with large scale system up to 118 nodes.…”

Section: Research Articlementioning

confidence: 99%

Optimal power flow solutions to power systems with wind energy using a highly effective meta-heuristic algorithm

Le²,

Huynh

et al. 2023

Int. J. Renew. Energy Dev.

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This paper implements two novel meta-heuristic algorithms, including the Coati optimization algorithm (COA) and War strategy optimization (WSO) for determining the optimal solutions to the optimal power flow problem incorporating the use of wind turbines. Two objective functions are considered in this study, including minimizing the entire electricity generation cost (EEGC) with the value point effect and minimizing the voltage fluctuation index (VFI). IEEE 30-bus system is chosen to conduct the whole study and validate the efficiency of the two applied methods. Furthermore, DFIG wind turbines are used in grids with varying power output and power factor ranges. The comparison of the results obtained from the two methods in all case studies reveals that WSO is vastly superior to COA in almost all aspects. In addition, the positive contributions of wind turbines to the EEGE and VFI while they are properly placed in the grid are also clarified by using WSO. As a result, WSO is acknowledged as a highly effective search method, and we strongly recommend using WSO for dealing with such OPF problems considering the presence of renewable energy sources.

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Section: Research Articlementioning

confidence: 99%

Optimal power flow solutions to power systems with wind energy using a highly effective meta-heuristic algorithm

Le²,

Huynh

et al. 2023

Int. J. Renew. Energy Dev.

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“…International Journal of Intelligent Engineering and Systems, Vol. 15 [18] √ ----PSO [19] √ ----PSO [20] √ ----PSO [21] √ ----GWO [22] √ √ √ --DE [22] √ √ √ --HHO [23] √ √ ---SSA [24] √ √ ---MFO [25] √ √ ---PSO [26] √ --√ √ BBO [27] √ --√ √ GSA [28] √ --√ √ SSA [29] √ √ -√ -MBO [30] √ √ -√ -HHO [31] √ √ -√ -MVO [31] √ √ -√ -GOA [31] √ √ -√ -ABC [33] √ √ -√ √ FA [32] √ √ -√ √ DA [32] √ √ -√ √ ESCA [35] √ √ -√ √ MSA [32] √ √ -√ √ DSA [34] √ √ -√ √ WOA [36] √ √ ---SCA [38] √ √ -√ -MSCA [38] √ √ -√ -EGA [37] √ √ --√ BHBO [39] √ √ √ √ √ GWO [41] √ √ √ √ √…”

Section: Introductionmentioning

confidence: 99%

“…In [29][30][31] the sparrow search algorithm (SSA) and monarch butterfly optimization (MBO), grasshopper optimization algorithm (GOA), multi-verse optimizer (MVO) and HHO have been employed to reduce the generation fuel cost, active power transmission loss and improve the voltage profile by minimizing the voltage deviation. In [32] and [33][34][35] the generation fuel cost, active power loss, voltage deviation have been minimized and the voltage stability has been improved by using the FA, dragonfly algorithm (DA), moth swarm algorithm (MSA), ABC, DSA and efficient sine cosine optimization algorithm (ESCA). In [36], the whale optimization algorithm (WOA) has been applied to solve the OPF problem.…”

Section: Introductionmentioning

confidence: 99%

“…In [38], the SCA and modified SCA (MSCA) have been proposed to minimize the generation fuel cost, and active and reactive power loss and improve the voltage profile. In [35] an efficient sine cosine optimization algorithm (ESCA) has been proposed. In [39,40,41], five objectives namely, reduction of generation fuel cost, voltage deviation, active and reactive power losses and improvement of voltage stability have been optimized by using the black-hole optimization (BHBO) and GWO algorithm.…”

Section: Introductionmentioning

confidence: 99%

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A Modified Honey Badger Algorithm for Solving Optimal Power Flow Optimization Problem

2022

IJIES

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This paper proposes a modified honey badger algorithm (MHBA) for solving the optimal power flow (OPF) problem. This problem is a highly non-linear, non-convex and complex optimization problem with several decision variables and constraints. The original honey badger algorithm (HBA) has the problem of trapping in local optima due to the loss of population diversity, especially in solving complex optimization problems. Therefore, the MHBA aims at sufficient improvement in finding the optimal solution and feasibility. Opposition-based learning strategy (OBL) is integrated with the MHBA to preserve the diversity of the population and enhance the convergence toward the optimal solution. The effectiveness of the MHBA algorithm is evaluated on five objective functions of the OPF problem namely, total generation fuel cost minimization, active power and reactive power transmission losses minimization, voltage deviation and voltage stability enhancement. The performance of the proposed algorithm is tested and validated on the IEEE 30-bus test system. The proposed MHBA is compared with the HBA and other nature-inspired optimization algorithms reported in the literature. The results indicate that the proposed MHBA algorithm has the superiority to jump out of the local optimal and better convergence in solving the OPF problem. This is due to the strategy used in the algorithm which helps in maintaining the population diversity and provides a proper balance between exploration and exploitation.

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“…These qualities make metaheuristics enormously fitting for genuine enhancement issues. There are several research conducted on the basis swarm intelligence those is Particle swarm improvement (PSO) [20], Bat Algorithm (BA) [21], Artificial Bee Colony (ABC) [22], Firefly Algorithm (FA) [23], Flower Pollination Algorithm (FPA) [24], Ant Colony Optimization (ACO) [25], Cuckoo Search optimization(CSO) [26], Elephant swarm water search algorithm(ESWSA) [27,28], Simulated Annealing [29], Sine Cosine Optimization [30], Salp Swarm Algorithm [31] and so on. For improving better computational time, precision, intermingling speed, better investigation, proper parameters tuning and misuse capacity a single metaheuristic is not appropriate for taking care of all this, so a metaheuristics use to change the existing one, is known as the No Free Lunch hypothesis [32].…”

Section: Introductionmentioning

confidence: 99%

An Improved Grey Wolf Optimization Algorithm for Liquid flow Control System

Dutta¹,

Majumder²,

Kumar³

2021

IJEM

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Liquid flow in a process industry is one of the significant factors which should be controlled to get the better quality and decrease the expense of generation. Customary methodology includes manual tuning of the input process parameter to obtain the required flow rate is tedious and exorbitant. Notwithstanding, estimation of a precise computational model for fluid stream control procedure can fill in as elective methodology. It is only a non-straight enhancement issue. As a contextual investigation, the WFT -20-I measure control arrangement for flow rate measurement and Control issue is thought of. In this work we proposes a hybrid improved particle swarm optimization (PSO-GWO) used to start the people's position, which can build the decent variety of the wolf pack, balance the global and neighborhood search capacity of the calculation and improve the intermingling pace of the calculation contrast with the Gray wolf enhancement (GWO) and Particle swarm advancement (PSO). Non linear models are improved utilizing those recently proposed streamlining strategies. Additionally all the utilized optimization techniques can anticipate the fluid stream rate with good exactness. The outcomes were investigated by utilizing the root mean square error (RMSE), exactness, and the different measures to evaluate the level of identification performance of the liquid flow contextual analysis model. The trustworthiness of the present models was compared with the past model for similar subsystems utilizing competitive intelligent methodologies. The measurable examination of the acquired outcomes produced the proposed HPSOGWO has most elevated generally speaking proficiency (i.e.99.96%) and it beat the others strategies for the majority of the instances of demonstrating for fluid stream control process. The outcomes of the present model show that the proposed approach gives prevalent demonstrating execution and outflanks its rivals.

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Optimal Power Flow Solution using Efficient Sine Cosine Optimization Algorithm

Cited by 3 publications

References 0 publications

Optimal power flow solutions to power systems with wind energy using a highly effective meta-heuristic algorithm

Optimal power flow solutions to power systems with wind energy using a highly effective meta-heuristic algorithm

A Modified Honey Badger Algorithm for Solving Optimal Power Flow Optimization Problem

An Improved Grey Wolf Optimization Algorithm for Liquid flow Control System

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