An improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problem

Bai, Wenlei; Eke, İ̇brahim; Lee, Kwang Y.

doi:10.1016/j.conengprac.2017.02.010

Cited by 98 publications

(30 citation statements)

References 34 publications

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“…Güç sistemlerinde hat, jeneratör, transformatör gibi elektriksel teçhizatlarının empedansları kendi anma gerilim ve güçleri cinsinden birim değer per-unit (pu) olarak ifade edilir. IEEE 30 bara güç sisteminde temel güç 100 MVA olarak alınmıştır [32]. Bu sistemdeki tüm analizler için bara gerilimleri 0,9 pu -1.1 pu arasındadır [22].…”

Section: Ieee 30 Baralı Güç Sistemi (Ieee 30 Bus Power System)unclassified

İyileştirilmiş su çevrim algoritmasıyla optimal yük akışı

Akdağ

Yeroğlu

2021

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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 Optimal power flow  Modified water cycle algorithm  Reducing the losses in power systems Results: The fuel cost and total active power losses are computed as 798.9021$/h, and 3.0855 MW by MWCA, respectively. Optimum values obtained by MWCA have produced better results than most of the algorithms presented in the literature. Conclusion: In this study, IEEE 30-bus test system and a cross-section of South Marmara Energy system have been used to present the minimization of active power losses and fuel cost via MWCA.

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Section: Ieee 30 Baralı Güç Sistemi (Ieee 30 Bus Power System)unclassified

İyileştirilmiş su çevrim algoritmasıyla optimal yük akışı

Akdağ

Yeroğlu

2021

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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“…But algorithms based on these techniques may have slow convergence characteristics and sometimes get stuck in the local optima due to the inappropriate initial guess, step size, and approximation, thus suffers from inaccuracy and provide infeasible solutions. In recent past researchers have developed some modern metaheuristic techniques to deal with the non-linear and multi-modal (more than one local optimum) nature of the OPF problem [28]. These methods are quite competent to handle the nonlinear and multimodal nature of the OPF problem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…When equality constraints violates their specified ranges, they are repaired in process of load flow program as follows in (28): (28) It is also worth noting here that when optimization algorithms are used to solve any type of objective function, it is difficult to handle all respective constraints of diverse nature. So, there must be some mechanism to deal with constraints handling task.…”

Section: E Constraints Handling Approach (Cha)mentioning

confidence: 99%

Multi-Objective Optimal Power Flow With Integration of Renewable Energy Sources Using Fuzzy Membership Function

et al. 2020

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In recent past, to meet the growing energy demand of electricity, integration of renewable energy resources (RESs) in an electrical network is a center of attention. Furthermore, optimal integration of these RESs make this task more challenging because of their intermittent nature. Therefore, in the present study power flow problem is treated as a multi-constraint, multi-objective optimal power flow (MOOPF) problem along with optimal integration of RESs. Whereas, the objectives of MOOPF are threefold: overall generation cost, real power loss of system and carbon emission reduction of thermal sources. In this work, a computationally efficient technique is presented to find the most feasible values of different control variables of the power system having distributed RESs. Whereas, the constraint satisfaction is achieved by using penalty function approach (PFA) and to further develop true Pareto front (PF), Pareto dominance method is used to categorize Pareto dominate solution. Moreover, to deal with intermittent nature of RES, probability density function (PDF) and stochastic power models of RES are used to calculate available power from RESs. Since, objectives of the MOOPF problem are conflicting in nature, after having the set of non-dominating solutions fuzzy membership function (FMF) approach has been used to extract the best compromise solution (BCS). To test the validity of developed technique, the IEEE-30 bus system has been modified with integration of RESs and final optimization problem is solved by using particle swarm optimization (PSO) algorithm. Simulation results show the achievement of proposed technique managing fuel cost value long with the optimal values of other objectives. INDEX TERMS fuzzy membership function; multi-objective optimal power ïňĆow problem; renewable energy sources.

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“…Teaching-learning based optimization (TLBO) algorithm was applied in [15] to find the optimal solution for OPF problems. An enhanced artificial bee colony (ABC) algorithm based on orthogonal learning was presented in [16]to handle the OPF problem. The performance of the algorithm was boosted by maintaining a balance between the exploration and exploitation search.…”

Section: Introductionmentioning

confidence: 99%

Optimal Power Flow Considering Intermittent Solar and Wind Generation using Multi-Operator Differential Evolution Algorithm

Hossain¹,

Sallam²,

Elsayed³

et al. 2021

Preprint

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In this paper, a multi-operator differential evolution algorithm (MODE) is proposed to solve the Optimal Power Flow (OPF) problem and is called MODE-OPF. The MODE-OPF utilizes the strengths of more than one differential evolution (DE) operator in a single algorithmic framework. Additionally, an adaptive method (AM) is proposed to update the number of solutions evolved by each DE operator based on both the diversity of population and quality of solutions. This adaptive method has the ability to maintain diversity at the early stages of the optimization process and boost convergence at the later ones. The performance of the proposed MODE-OPF is tested by solving OPF problems for both small and large IEEE bus systems (i.e., IEEE-30 and IEEE-118) while considering the intermittent solar and wind power generation. To prove the suitability of this proposed algorithm, its performance has been compared against several state-of-the-art optimization algorithms, where MODE-OPF outperforms other algorithms in all experimental results and thereby improving a network's performance with lower cost. MODE-OPF decreases the total generation cost up to 24.08%, the real power loss up to 6.80% and the total generation cost with emission up to 8.56%.

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An improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problem

Cited by 98 publications

References 34 publications

İyileştirilmiş su çevrim algoritmasıyla optimal yük akışı

İyileştirilmiş su çevrim algoritmasıyla optimal yük akışı

Multi-Objective Optimal Power Flow With Integration of Renewable Energy Sources Using Fuzzy Membership Function

Optimal Power Flow Considering Intermittent Solar and Wind Generation using Multi-Operator Differential Evolution Algorithm

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