Application of modified NSGA-II algorithm to multi-objective reactive power planning

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• 作者：S. Ramesh^a ; ^{rameshsme@gmail.com} ; S. Kannan^b ; ^{kannaneee@rediffmail.com} ; S. Baskar^c ; ^{sbeee@tce.edu}
• 关键词：Covariance Matrix Adapted Evolution Strategy (CMA-ES) ; Dynamic crowding distance ; Multi-objective ; Multi-attribute decision making ; Non-dominated sorting genetic algorithm ; Optimization ; Reactive power planning ; TOPSIS
• 刊名：Applied Soft Computing Journal
• 出版年：2012
• 出版时间：February, 2012
• 年：2012
• 卷：12
• 期：2
• 页码：741-753
• 全文大小：703 K

文摘

This paper discusses the application of Modified Non-Dominated Sorting Genetic Algorithm-II (MNSGA-II) to multi-objective Reactive Power Planning (RPP) problem. The three objectives considered are minimization of combined operating and VAR allocation cost, bus voltage profile improvement and voltage stability enhancement. For maintaining good diversity in nondominated solutions, Dynamic Crowding Distance (DCD) procedure is implemented in NSGA-II and it is called as MNSGA-II. The standard IEEE 30-bus test system, practical 69-bus Indian system and IEEE 118-bus system are considered to analyze the performance of MNSGA-II. The results obtained using MNSGA-II are compared with NSGA-II and validated with reference pareto-front generated by conventional weighted sum method using Covariance Matrix Adapted Evolution Strategy (CMA-ES). The performance of NSGA-II and MNSGA-II are compared with respect to best, mean, worst and standard deviation of multi-objective performance measures namely gamma, spread, minimum spacing and Inverted Generational Distance (IGD) in 15 independent runs. The results show the effectiveness of MNSGA-II and confirm its potential to solve the multi-objective RPP problem. A decision-making procedure based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used for finding best compromise solution from the set of pareto-solutions obtained through MNSGA-II.