TY - GEN
T1 - Comparative analysis of PSO and quasi-Newton methodsfor economic dispatch of power systems
AU - Sun, Yanxia
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/14
Y1 - 2017/11/14
N2 - The economic dispatch (ED) is one ofthe important parts of power systems. ED includes the objective function(s) and constraints. Many optimization algorithms including traditional optimization methods and intelligent optimization algorithms have been applied to ED problems. However these two kinds of optimization algorithms are based on different philosophies and they are different from the simplicity ofmethods/algorithms and performance. It is necessary to investigate the efficiency and performance of these two kinds of optimization methods and find the proper situations methods/algorithms for different. In this paper, the Quasi-Newton Method (QNM) is chosen as the representation of the traditional optimization methods and Particle Swarm Optimization (PSO) is taken as the representation of the intelligent optimization algorithms, both QNM and PSO are applied to the ED problems with different numbers of generators, load power demands and constrains. Finally the simulation results show the performance of these two optimization algorithms and the guidance of choosing QNM or PSO is provided for ED problems.
AB - The economic dispatch (ED) is one ofthe important parts of power systems. ED includes the objective function(s) and constraints. Many optimization algorithms including traditional optimization methods and intelligent optimization algorithms have been applied to ED problems. However these two kinds of optimization algorithms are based on different philosophies and they are different from the simplicity ofmethods/algorithms and performance. It is necessary to investigate the efficiency and performance of these two kinds of optimization methods and find the proper situations methods/algorithms for different. In this paper, the Quasi-Newton Method (QNM) is chosen as the representation of the traditional optimization methods and Particle Swarm Optimization (PSO) is taken as the representation of the intelligent optimization algorithms, both QNM and PSO are applied to the ED problems with different numbers of generators, load power demands and constrains. Finally the simulation results show the performance of these two optimization algorithms and the guidance of choosing QNM or PSO is provided for ED problems.
KW - Optimization Performance
KW - Particle Swarm Optimization
KW - Power Economic Dispatch
KW - Quasi-Newton Method
UR - http://www.scopus.com/inward/record.url?scp=85042584850&partnerID=8YFLogxK
U2 - 10.1109/ICMLC.2017.8108953
DO - 10.1109/ICMLC.2017.8108953
M3 - Conference contribution
AN - SCOPUS:85042584850
T3 - Proceedings of 2017 International Conference on Machine Learning and Cybernetics, ICMLC 2017
SP - 390
EP - 396
BT - Proceedings of 2017 International Conference on Machine Learning and Cybernetics, ICMLC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th International Conference on Machine Learning and Cybernetics, ICMLC 2017
Y2 - 9 July 2017 through 12 July 2017
ER -