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作 者:G.Girishkumar S.Ganesan N.Jayakumar S.Subramanian
机构地区:[1]Department of EEE,Government Polytechnic College,Keelapaluvur,Ariyalur,621707,Tamil Nadu,India [2]Department of EEE,Government College of Engineering,Salem,636011,Tamil Nadu,India [3]Department of EEE,Government Polytechnic College,Uthangarai,635207,Tamil Nadu,India [4]Department of Electrical Engineering,Annamalai University,Annamalai Nagar,608002,Tamil Nadu,India
出 处:《Intelligent Automation & Soft Computing》2023年第1期609-625,共17页智能自动化与软计算(英文)
摘 要:The optimizationfield has grown tremendously,and new optimization techniques are developed based on statistics and evolutionary procedures.There-fore,it is necessary to identify a suitable optimization technique for a particular application.In this work,Black Widow Optimization(BWO)algorithm is intro-duced to minimize the cost functions in order to optimize the Multi-Area Economic Dispatch(MAED).The BWO is implemented for two different-scale test systems,comprising 16 and 40 units with three and four areas.The performance of BWO is compared with the available optimization techniques in the literature to demonstrate the strategy’s efficacy.Results show that the optimized cost for four areas with 16 units is found to be 7336.76$/h,whereas it is 121,589$/h for four areas with 40 units using BWO.It is also noted that optimization algo-rithms other than BWO require higher cost value.The best-optimized solution for emission is achieved at 9.2784e+06 tones/h,and it is observed that there is a considerable difference between the worst and the best values.Also,the suggested technique is implemented for large-scale test systems successfully with high precision,and rapid convergence occurs in MAED.
关 键 词:Black widow optimization algorithm multi-objective multi-area economic dispatch emission optimization cost optimization
分 类 号:TP311.5[自动化与计算机技术—计算机软件与理论]
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