Fuzzy Multi-Objective Optimal Power Flow Using Genetic Algorithms Applied to Algerian Electrical Network

Salhi, Ahmed; Naimi, Djemai; Bouktir, Tarek

doi:10.15598/aeee.v11i6.832

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…For this case violating constraints of reactive power occurs in two generating units: -58.9227 MVAR (G2), and 101.0024 MVAR (G8). It is important to note that the reactive power limits are the same considered in ref [17]. For the solution provided by HFPSO-NM, this result can be considered as the infeasible solutions, due to the voltage limits has been violated in buses 8 and 11 (1.11 pu).…”

Section: A Results and Commentsmentioning

confidence: 99%

“…This accounts for the inclusion of multiple non-differential points in the cost characteristic function and thus, turns it into a non-smooth function. It shall be expressed as a quadratic and a sinusoidal function given below: (17) where i d and i e are the coefficients that represent the valve-point loading effects, and min gi P is the minimum output power generation of the ith generating unit. In this case, the cost function is non-convex due to valve-point effects and the global minimum is very difficult to ascertain.…”

Section: Case 2: Quadratic Fuel Cost With Valve-point Effectmentioning

confidence: 99%

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An Efficient Chaotic Cuckoo Search Framework for Solving Non-Convex Optimal Power Flow Problem

Bentouati

Chettih

Djekidel

et al. 2017

JERA

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The optimal power flow (OPF) problem is a very complicated task in power systems. OPF problem has a set of equality and inequality constraints. This paper looks at a chaotic cuckoo search (CCS) algorithm for solving non-convex OPF problem. The proposed CCS is a bio-inspired optimization calculation that is inspired by the behaviour of cuckoos people in nature. The chaotic guide is a variation of qualities combined with CS. A sinusoidal chaotic is integrated with CS algorithm and tested on standard IEEE 30-bus test system to the point of improving its global speed of convergence and enhancing its performance. The elitism scheme is also serves to save the best cuckoo during amid the procedure when updating the cuckoo. The results show clearly the superiority of CCS in searching for the best function values results when compared with well-known metaheuristic search algorithms.

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Section: A Results and Commentsmentioning

confidence: 99%

Section: Case 2: Quadratic Fuel Cost With Valve-point Effectmentioning

confidence: 99%

An Efficient Chaotic Cuckoo Search Framework for Solving Non-Convex Optimal Power Flow Problem

Bentouati

Chettih

Djekidel

et al. 2017

JERA

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“…Luckily, meta-heuristic algorithms have recently demonstrated themselves to be highly affordable and the most trusted computing method to deal with optimization problems, including OPF problems. For instant, different meta-heuristic algorithms have been applied to solve the OPF problems such as backtracking search algorithm (BSA) [1], genetic algorithm (GA) [2], Harris Hawks optimization (HHO) [3], [4], grey wolf optimizer algorithm (GWOA) [5], AMTPG-Jaya algorithm [6], differential evolution (DE) [7], [8], effective Cuckoo search algorithm (ECSA) [9], particle swarm optimization (PSO) [10], Gorilla troops algorithm (GTA) [11], modified coyote optimization algorithm (MCSA) [12], black hole optimization (BHO) [13], the hybrid method of Cuckoo search algorithm (MCOA) and sunflower optimization (SFO) [14], improved multi-objective multi-verse algorithm (IMOMVA) [15], firefly krill herd algorithm (FHHA) [16], improved moth-flame optimization (IMFO) [17], antlion optimization (ALO) and its improved version [18], [19], marine predator algorithm (MPA) [20], social spider algorithm (SSA) [21], slime mould algorithm (SMA) [22], whale optimization algorithm (WOA) [23], and golden ratio optimization (GRO) [24]. The effectiveness of meta-heuristic algorithms has shown a huge leap forward when compared with conventional computing methods, such as the Newton-Raphson technique [25], in terms of time response, robustness, and precision degree of the final results.…”

Section: Issn: 2302-9285 mentioning

confidence: 99%

“…An optimal solution to such an OPF problem often includes control and dependent variables. Normally, the control variables include the active power generated by thermal units excluding the one connected with the slack node, the voltage magnitudes at all thermal units, the reactive power of shunt capacitors, and the transformer tap [2]. The dependent variables are the active power generated by the thermal unit at the slack node, the voltage magnitude at load nodes, and the reactive power output of all thermal units.…”

Section: Introductionmentioning

confidence: 99%

Skill optimization algorithm for solving optimal power flow problem

Hien,

Duong,

Pham

2024

Bulletin EEI

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This research presents the implementation of a modern meta-heuristic algorithm called the skill optimization algorithm (SOA) to solve the optimal power flow problem (OPF). An IEEE 30-bus transmission system is selected to test the real performance of SOA. The main objective function of the study is to minimize the total fuel cost (TFC) of all thermal units. To clarify the high performance of SOA, a classical meta-heuristic named particle swarm optimization (PSO) is also applied for comparison. All results reached by SOA are compared with those of PSO on different criteria. Particularly, SOA has reached smaller cost than PSO by $1.04, equivalent to 0.13% of PSO’s TFC. Furthermore, SOA has reached a more stable performance by finding better average and maximum TFC over fifty runs. The evaluation of these criteria indicates that SOA completely outperforms PSO. Besides, the optimal solution reached by SOA satisfies all considered constraints with zero violation of the dependent variables. Therefore, SOA is highly suggested to handle the OPF problem.

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“…1887 order to satisfy the line limitations. There is consequently a need for an effective method of integrating the security limitations into ED [14].…”

Section: Constraint 3: Upper and Lower Limits Of Node Voltagesmentioning

confidence: 99%

Artificial intelligence of optimal real power dispatch with constraints of lines overloading

Abderrahmani¹,

Nasri²,

Gasbaoui³

2023

IJPEDS

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<span lang="EN-US">In the literature on optimal power flow (OPF), it has been shown that the suggested ways offer a higher degree of satisfaction in optimizing overall production costs while fulfilling power flow equations, system security, and equipment operational constraints. Despite this, the overloaded of the transmission lines are taken as a performance index but not a primary constraint. This article presents an improved approach to artificial intelligence algorithms of optimal real power dispatch with the security of lines; the main difference concerning our point seen relies on the additional penalization of the choices, which does not respect this constraint. The problem is implemented in the IEEE 14-bus system with "5" generator units. The results of the simulations of the metaheuristic algorithms without/with constraint (overloaded lines) were compared. Furthermore, this article suggests hybridizing ant colony optimization (ACO) and genetic algorithm (GA) as a means to enhance the optimization performance of these algorithms. This hybridization involves using ACO to generate a set of initial solutions, which are then refined using GA. The compound results obtained by the ant system-genetic algorithm hybrid (H-ASGA) for the problem of overloaded lines validated its potential.</span>

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Fuzzy Multi-Objective Optimal Power Flow Using Genetic Algorithms Applied to Algerian Electrical Network

Cited by 5 publications

References 15 publications

An Efficient Chaotic Cuckoo Search Framework for Solving Non-Convex Optimal Power Flow Problem

An Efficient Chaotic Cuckoo Search Framework for Solving Non-Convex Optimal Power Flow Problem

Skill optimization algorithm for solving optimal power flow problem

Artificial intelligence of optimal real power dispatch with constraints of lines overloading

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