Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO)

被引：37

作者：

Hemeida, Mahmoud G. ^{[1
]}

Alkhalaf, Salem ^{[2
]}

Mohamed, Al-Attar A. ^{[3
]}

Ibrahim, Abdalla Ahmed ^{[3
]}

Senjyu, Tomonobu ^{[4
]}

机构：

[1] Minia Higher Inst Engn, Dept Elect Engn, Al Minya 61111, Egypt

[2] Qassim Univ, Dept Comp Sci, Arrass Coll Sci & Arts, Qasim 51431, Saudi Arabia

[3] Aswan Univ, Dept Elect Engn, Fac Engn, Aswan 81511, Egypt

[4] Univ Ryukyus, Fac Engn, 1 Senbaru, Nakagami 90370213, Japan

来源：

ENERGIES | 2020年 / 13卷 / 15期

关键词：

optimization techniques; manta ray foraging optimization algorithm; multi-objective function; radial networks; optimal power flow; RADIAL-DISTRIBUTION SYSTEM; FRACTAL SEARCH ALGORITHM; OPTIMAL ALLOCATION; DISTRIBUTION NETWORKS; OPTIMAL PLACEMENT; OPTIMAL LOCATION; SIZE; MODELS; DG;

D O I：

10.3390/en13153847

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Manta Ray Foraging Optimization Algorithm (MRFO) is a new bio-inspired, meta-heuristic algorithm. MRFO algorithm has been used for the first time to optimize a multi-objective problem. The best size and location of distributed generations (DG) units have been determined to optimize three different objective functions. Minimization of active power loss, minimization of voltage deviation, and maximization of voltage stability index has been achieved through optimizing DG units under different power factor values, unity, 0.95, 0.866, and optimum value. MRFO has been applied to optimize DGs integrated with two well-known radial distribution power systems: IEEE 33-bus and 69-bus systems. The simulation results have been compared to different optimization algorithms in different cases. The results provide clear evidence of the superiority of MRFO that defind before (Manta Ray Foraging Optimization Algorithm. Quasi-Oppositional Differential Evolution Levy Flights Algorithm (QODELFA), Stochastic Fractal Search Algorithm (SFSA), Genetics Algorithm (GA), Comprehensive Teaching Learning-Based Optimization (CTLBO), Comprehensive Teaching Learning-Based Optimization (CTLBO (epsilon constraint)), Multi-Objective Harris Hawks Optimization (MOHHO), Multi-Objective Improved Harris Hawks Optimization (MOIHHO), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Particle Swarm Optimization (MOWOA) in terms of power loss, Voltage Stability Index (VSI), and voltage deviation for a wide range of operating conditions. It is clear that voltage buses are improved; and power losses are decreased in both IEEE 33-bus and IEEE 69-bus system for all studied cases. MRFO algorithm gives good results with a smaller number of iterations, which means saving the time required for solving the problem and saving energy. Using the new MRFO technique has a promising future in optimizing different power system problems.

引用

页数：34

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