This work proposes a novel approach for multi-type optimal placement of flexible AC transmission system(FACTS) devices so as to optimize multi-objective voltage stability problem. The current study discusses a way for...This work proposes a novel approach for multi-type optimal placement of flexible AC transmission system(FACTS) devices so as to optimize multi-objective voltage stability problem. The current study discusses a way for locating and setting of thyristor controlled series capacitor(TCSC) and static var compensator(SVC) using the multi-objective optimization approach named strength pareto multi-objective evolutionary algorithm(SPMOEA). Maximization of the static voltage stability margin(SVSM) and minimizations of real power losses(RPL) and load voltage deviation(LVD) are taken as the goals or three objective functions, when optimally locating multi-type FACTS devices. The performance and effectiveness of the proposed approach has been validated by the simulation results of the IEEE 30-bus and IEEE 118-bus test systems. The proposed approach is compared with non-dominated sorting particle swarm optimization(NSPSO) algorithm. This comparison confirms the usefulness of the multi-objective proposed technique that makes it promising for determination of combinatorial problems of FACTS devices location and setting in large scale power systems.展开更多
Purpose–The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current algorithms.The proposed application is for engineering design problems.Design/...Purpose–The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current algorithms.The proposed application is for engineering design problems.Design/methodology/approach–This study proposes two novel approaches which focus on faster convergence to the Pareto front(PF)while adopting the advantages of Strength Pareto Evolutionary Algorithm-2(SPEA2)for better spread.In first method,decision variables corresponding to the optima of individual objective functions(Utopia Point)are strategically used to guide the search toward PF.In second method,boundary points of the PF are calculated and their decision variables are seeded to the initial population.Findings–The proposed methods are tested with a wide range of constrained and unconstrained multi-objective test functions using standard performance metrics.Performance evaluation demonstrates the superiority of proposed algorithms over well-known existing algorithms(such as NSGA-II and SPEA2)and recent ones such as NSLS and E-NSGA-II in most of the benchmark functions.It is also tested on an engineering design problem and compared with a currently used algorithm.Practical implications–The algorithms are intended to be used for practical engineering design problems which have many variables and conflicting objectives.A complex example of Welded Beam has been shown at the end of the paper.Social implications–The algorithm would be useful for many design problems and social/industrial problems with conflicting objectives.Originality/value–This paper presents two novel hybrid algorithms involving SPEA2 based on:local search;and Utopia point directed search principles.This concept has not been investigated before.展开更多
文摘This work proposes a novel approach for multi-type optimal placement of flexible AC transmission system(FACTS) devices so as to optimize multi-objective voltage stability problem. The current study discusses a way for locating and setting of thyristor controlled series capacitor(TCSC) and static var compensator(SVC) using the multi-objective optimization approach named strength pareto multi-objective evolutionary algorithm(SPMOEA). Maximization of the static voltage stability margin(SVSM) and minimizations of real power losses(RPL) and load voltage deviation(LVD) are taken as the goals or three objective functions, when optimally locating multi-type FACTS devices. The performance and effectiveness of the proposed approach has been validated by the simulation results of the IEEE 30-bus and IEEE 118-bus test systems. The proposed approach is compared with non-dominated sorting particle swarm optimization(NSPSO) algorithm. This comparison confirms the usefulness of the multi-objective proposed technique that makes it promising for determination of combinatorial problems of FACTS devices location and setting in large scale power systems.
文摘Purpose–The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current algorithms.The proposed application is for engineering design problems.Design/methodology/approach–This study proposes two novel approaches which focus on faster convergence to the Pareto front(PF)while adopting the advantages of Strength Pareto Evolutionary Algorithm-2(SPEA2)for better spread.In first method,decision variables corresponding to the optima of individual objective functions(Utopia Point)are strategically used to guide the search toward PF.In second method,boundary points of the PF are calculated and their decision variables are seeded to the initial population.Findings–The proposed methods are tested with a wide range of constrained and unconstrained multi-objective test functions using standard performance metrics.Performance evaluation demonstrates the superiority of proposed algorithms over well-known existing algorithms(such as NSGA-II and SPEA2)and recent ones such as NSLS and E-NSGA-II in most of the benchmark functions.It is also tested on an engineering design problem and compared with a currently used algorithm.Practical implications–The algorithms are intended to be used for practical engineering design problems which have many variables and conflicting objectives.A complex example of Welded Beam has been shown at the end of the paper.Social implications–The algorithm would be useful for many design problems and social/industrial problems with conflicting objectives.Originality/value–This paper presents two novel hybrid algorithms involving SPEA2 based on:local search;and Utopia point directed search principles.This concept has not been investigated before.
基金Supported by the National High Technology Research and Development of China(863Programme)(2007AA05Z458)Shanghai Natural Science Foundation(08ZR1409700)~~
