Optimal power flow solution using fuzzy evolutionary and swarm optimization

Kumar, Sanjeev; Chaturvedi, D. K.

doi:10.1016/j.ijepes.2012.11.019

Cited by 87 publications

(40 citation statements)

References 20 publications

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“…It is an astute power flow that utilizes optimization algorithms to regulate power grid control settings optimally amid diverse constraints [3,4]. Many classical optimization algorithms have been utilized to deal with the OPF problem, like non-linear programming [5,6], the Newton algorithm [7], quadratic programming [8], and decomposition algorithms [9].…”

Section: Introductionmentioning

confidence: 99%

Optimal Power Flow Using the Jaya Algorithm

et al. 2016

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This paper presents application of a new effective metaheuristic optimization method namely, the Jaya algorithm to deal with different optimum power flow (OPF) problems. Unlike other population-based optimization methods, no algorithm-particular controlling parameters are required for this algorithm. In this work, three goal functions are considered for the OPF solution: generation cost minimization, real power loss reduction, and voltage stability improvement. In addition, the effect of distributed generation (DG) is incorporated into the OPF problem using a modified formulation. For best allocation of DG unit(s), a sensitivity-based procedure is introduced. Simulations are carried out on the modified IEEE 30-bus and IEEE 118-bus networks to determine the effectiveness of the Jaya algorithm. The single objective optimization cases are performed both with and without DG. For all considered cases, results demonstrate that Jaya algorithm can produce an optimum solution with rapid convergence. Statistical analysis is also carried out to check the reliability of the Jaya algorithm. The optimal solution obtained by the Jaya algorithm is compared with different stochastic algorithms, and demonstrably outperforms them in terms of solution optimality and solution feasibility, proving its effectiveness and potential. Notably, optimal placement of DGs results in even better solutions.

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Section: Introductionmentioning

confidence: 99%

Optimal Power Flow Using the Jaya Algorithm

et al. 2016

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“…3. [17] 801.960 9.080 FGA [17] 801.121 9.030 PSO [17] 800.960 9.080 FPSO [17] 800.720 8.750 Bat Algorithm [23] 800.929 9.220…”

Section: A Ieee 30 Bus Systemmentioning

confidence: 99%

“…The comparison of simulation results in the DE with the proposed methods such as GA method [17], fuzzy GA [17], PSO [17], fuzzy PSO [17], and bat algorithm [23] can be seen in Table III. The results show that the cost saving is $0.22/hour compared to the FPSO and $0.429/hour compared to the bat algorithm method.…”

Section: A Ieee 30 Bus Systemmentioning

confidence: 99%

“…The test on the IEEE 30 bus systems showed better results and required a shorter computation time instead of using GA, TS, or SA. On the other hand, fuzzy integration system with GA and PSO was used to solve the problems happened on OPF [17]. The test results on the IEEE 30 bus system showed it had better solution and was able to cut the computation time compared to standard methods of GA and PSO.…”

Section: Introductionmentioning

confidence: 99%

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The Solution for Optimal Power Flow (OPF) Method Using Differential Evolution Algorithm

Ariantara

Sarjiya

Hadi

2017

IJITEE

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Abstract-Optimal Power Flow (OPF) is one of techniques used to optimize the cost of power plant production while maintaining the limit of system reliability. In this paper, the application of differential evolution (DE) method is used to solve the OPF problem with variable control such as the power plant output, bus voltage tension, transformer tap, and injection capacitor. The effectiveness of the method was tested using IEEE 30 buses. The result shows that this method is better than generic algorithm (GA), particle swarm optimized (PSO), fuzzy GA, fuzzy PSO, and bat-algorithm. The simulation of the power plant systems of 500 kV Java-Bali with the proposed method can reduce the total cost of generation by 13.04% compared to the operating data PT. PLN (Persero).

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“…Test results on IEEE 30-bus system demonstrate that the proposed hybrid TS/SA approach can perform better than GA, SA, or TS alone. A hybrid approach integrating fuzzy systems with GA and PSO algorithm was proposed for the application for OPF problem [70]. A hybrid algorithm of DE and EP (DEEP) was proposed for solving ORPF problem [71].…”

Section: Hybrid Approachmentioning

confidence: 99%

A review on applications of heuristic optimization algorithms for optimal power flow in modern power systems

Ming

Wan

2014

J. Mod. Power Syst. Clean Energy

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Optimal power flow (OPF) is one of the key tools for optimal operation and planning of modern power systems. Due to the high complexity with continuous and discrete control variables, modern heuristic optimization algorithms (HOAs) have been widely employed for the solution of OPF. This paper provides an overview of the latest applications of advanced HOAs in OPF problems. The most frequently applied HOAs for solving the OPF problem in recent years are covered and briefly introduced, including genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and evolutionary programming (EP), etc.

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Optimal power flow solution using fuzzy evolutionary and swarm optimization

Cited by 87 publications

References 20 publications

Optimal Power Flow Using the Jaya Algorithm

Optimal Power Flow Using the Jaya Algorithm

The Solution for Optimal Power Flow (OPF) Method Using Differential Evolution Algorithm

A review on applications of heuristic optimization algorithms for optimal power flow in modern power systems

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