Optimization of Optimal Power Flow Problem Using Multi-Objective Manta Ray Foraging Optimizer

Kahraman, Hamdi Tolga; Akbel, Mustafa; Duman, Serhat

doi:10.1016/j.asoc.2021.108334

Cited by 82 publications

(28 citation statements)

References 37 publications

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“…Fuel cost ($/h) Emissions (ton/h) MOSGA 42497.0130 1.2712 GBICA [21] 42138.3695 1.3941 MGBICA [21] 42369.0664 1.2940 ESDE [22] 42863.3243 1.2662 ESDE-EC [22] 42863.2116 1.2387 ESDE-MC [22] 42857.4869 1.2191 ISPEA [23] 42444.5535 1.2904 SPEA2 [23] 42320.2545 1.4054 NSGA-II [23] 43567.7653 1.2979 rNSGA-II [23] 42635.7170 1.3784 MPIO-PFM [30] 43205.8477 1.2386 MPIO-COSR [30] 43131.2743 1.2314 MOPSO [31] 43279.6398 1.2546 NMBAS [31] 43117.8602 1.2245 DE-PFA [33] 43331.7568 1.2180 NSGA-II [33] 43353.5661 1.2272 HFBA-COFS [33] 43259.3013 1.2129 MOJFS [35] 43888.2320 1.2383 MOQRJFS [35] 43713.0146 1.3074 IHOA [36] 43864.8798 1.2192 MHBAS [37] 43174.05 1.2211 MDE [37] 43505.90 1.2236 IMOMRFO [38] 41742.9442 1.7912 [22] 42020.7439 12.2155 ESDE-EC [22] 42013.3395 11.9668 ESDE-MC [22] 41998.3588 11.8415 NSGA-II [33] 42125.6042 11.1296 HFBA-COFS [33] 42122.0140 10.6995 MOJFS [35] 42591.8712 15.1461 MOQRJFS [35] 41846.2247 15.8873 IHOA [36] 42419.5253 10.8192 MHBAS [37] 42084.81 10.5043 MDE [37] 42125.83 10.9193…”

Section: Methodsmentioning

confidence: 99%

“…Step [14] 823.278 0.29078 GBICA [21] 830.8524 0.2488 MGBICA [21] 830.8514 0.2484 ESDE [22] 833.474 0.2540 ESDE-EC [22] 831.0943 0.2510 ESDE-MC [22] 830.718 0.2483 ISPEA [23] 865.9499 0.2234 SPEA2 [23] 860.9832 0.2305 NSGA-II [23] 850.9166 0.2442 rNSGA-II [23] 848.1499 0.2464 MOEA/D-SF [29] 829.515 0.2501 MPIO-PFM [30] 833.1703 0.2397 MPIO-COSR [30] 832.4655 0.2351 MOPSO [31] 835.3988 0.2386 NMBAS [31] 831.4393 0.2337 MHBAS [37] 832.0355 0.2337 MDE [37] 833.1728 0.2346 IMOMRFO [38] 817.9615 0.2736 [22] 828.8413 5.5901 ESDE-EC [22] 827.9487 5.4524 ESDE-MC [22] 827.1592 5.2270 MPIO-PFM [30] 832.2274 5.1270 MPIO-COSR [30] 831.5576 5.1085 MOPSO [31] 837.3469 5.2635 NMBAS [31] 831.1550 5.0707 DE-PFA [33] 833.4465 5.1354 NSGA-II [33] 835.4439 5.1599 HFBA-COFS [33] 832.3203 5.0796 MHBAS [37] 832.1236 5.0566 MDE [37] 833.7892 5.1517…”

Section: ) Overall Proceduresmentioning

confidence: 99%

“…The authors indicated that MHBAS was better than NSGA-II and multi-objective differential evolution (MDE) for solution quality and computational cost. For dealing with the MOOPF problem with different objectives, Kahramana et al [38] developed an improved multiple objective manta ray foraging optimizer (IMOMRFO) based on a Pareto strategy and crowding distance method.…”

Section: Introductionmentioning

confidence: 99%

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Multiobjective Optimal Power Flow Using Multiobjective Search Group Algorithm

Huy

Kim

2022

IEEE Access

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This paper proposes a new multi-objective method that efficiently solves the multi-objective optimal power flow (MOOPF) problem in power systems. The objective of solving the MOOPF problem is to concurrently optimize the fuel cost, emissions, and active power loss. The proposed multi-objective search group algorithm (MOSGA) is an effective method that combines the merits of the original search group algorithm with fast nondominated sorting, crowding distance, and archive selection strategies to acquire a nondominated set in a single run. The MOSGA is employed on IEEE 30-bus and 57-bus systems to validate its robustness and efficiency. It was found that implementing MOSGA to solve the MOOPF significantly enhanced the performance of power systems in terms of economic, environmental, and technical benefits. As for Case 6, the fuel cost, emissions, and active power loss were reduced by 16.5707%, 52.0605%, and 60.9443%, respectively. The simulation results were analyzed and compared with those of previously reported studies based on the best individual solutions, compromise solutions, and performance indicators. The comparative results confirmed the potential and advantage of MOSGA when solving the MOOPF problem efficiently and MOSGA had high-quality optimal solutions. INDEX TERMS Multi-objective search group algorithm, multi-objective optimal power flow, fuel cost, emissions

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Section: Methodsmentioning

confidence: 99%

Section: ) Overall Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiobjective Optimal Power Flow Using Multiobjective Search Group Algorithm

Huy

Kim

2022

IEEE Access

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“…Voltage magnitude, (13) Reactive power, Apparent power, (18) is the lower and higher values of Vijaya Bhaskar K et al…”

Section: 22inequality Constraintsmentioning

confidence: 97%

An improved mayfly algorithm based optimal power flow solution for regulated electric power network

2022

IJATEE

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This paper presents an improved mayfly algorithm (IMA) for identifying the optimum control settings of optimal power flow problem in regulated electric power networks. IMA is the improved version of mayfly algorithm (MA) by implementing simulated binary crossover and polynomial mutation instead of arithmetic crossover and normal distribution mutation operators in MA. The attributes of genetic algorithm (GA), particle swarm optimization (PSO), and firefly algorithm (FA) are taken into account in IMA. Single objective functions such as total fuel cost, total active power losses, total voltage variation, and voltage stability index are used to assess the performance of the algorithms. The optimal solution of each objective function is evaluated by representing the test systems in MATPOWER. The results of IMA are compared with GA, PSO, and MA. Investigations based on the optimal solution, convergence characteristics, and statistical measures of the solution ensure IMA's superiority over alternative algorithms. The performance of the algorithms is evaluated by simulation of the IEEE-30 bus system, 62-bus Indian utility system and the IEEE-118 bus system. For IEEE-30 bussystem the optimal solution of the objective functions are 802.1448 $/hr, 3.6487 MW, 0.5279 pu and 0.1247, for 62-bus utility system the optimal solution of the objective functions are 13305.4267 $/hr, 73.8746 MW, 0.8049 pu and 0.0986, for IEEE-118 bus system the optimal solution of the objective functions are 129611.5389 $/hr, 76.5261 MW, 0.8632 pu and 0.0611 are obtained by implementing IMA.

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“…In [112], a boosted whale optimization algorithm named EWOA-OPF is developed to boost the global search capability of the WOA in solving the OPF problem by employing Levy motion in the encircling phase and utilizing Brownian motion to work with a canonical bubble-net attack. Kahraman et al [113] proposed an effective method by introducing a crowding distance-based Pareto archiving strategy to solve the multi-objective OPF problem. Akdag et al [98] introduced the improved Archimedes optimization algorithm (IAOA) using the dimension learning-based strategy to build a neighborhood and spread the information flow between search agents.…”

Section: Related Workmentioning

confidence: 99%

Hybridizing of Whale and Moth-Flame Optimization Algorithms to Solve Diverse Scales of Optimal Power Flow Problem

et al. 2022

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The optimal power flow (OPF) is a practical problem in a power system with complex characteristics such as a large number of control parameters and also multi-modal and non-convex objective functions with inequality and nonlinear constraints. Thus, tackling the OPF problem is becoming a major priority for power engineers and researchers. Many metaheuristic algorithms with different search strategies have been developed to solve the OPF problem. Although, the majority of them suffer from stagnation, premature convergence, and local optima trapping during the optimization process, which results in producing low solution qualities, especially for real-world problems. This study is devoted to proposing an effective hybridizing of whale optimization algorithm (WOA) and a modified moth-flame optimization algorithm (MFO) named WMFO to solve the OPF problem. In the proposed WMFO, the WOA and the modified MFO cooperate to effectively discover the promising areas and provide high-quality solutions. A randomized boundary handling is used to return the solutions that have violated the permissible boundaries of search space. Moreover, a greedy selection operator is defined to assess the acceptance criteria of new solutions. Ultimately, the performance of the WMFO is scrutinized on single and multi-objective cases of different OPF problems including standard IEEE 14-bus, IEEE 30-bus, IEEE 39-bus, IEEE 57-bus, and IEEE118-bus test systems. The obtained results corroborate that the proposed algorithm outperforms the contender algorithms for solving the OPF problem.

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Optimization of Optimal Power Flow Problem Using Multi-Objective Manta Ray Foraging Optimizer

Cited by 82 publications

References 37 publications

Multiobjective Optimal Power Flow Using Multiobjective Search Group Algorithm

Multiobjective Optimal Power Flow Using Multiobjective Search Group Algorithm

An improved mayfly algorithm based optimal power flow solution for regulated electric power network

Hybridizing of Whale and Moth-Flame Optimization Algorithms to Solve Diverse Scales of Optimal Power Flow Problem

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