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Estimation of Composite Load Model Parameters Using an Improved Particle Swarm Optimization Method.
- Source :
- IEEE Transactions on Power Delivery; Apr2015, Vol. 30 Issue 2, p553-560, 8p
- Publication Year :
- 2015
-
Abstract
- Power system loads are one of the crucial elements of modern power systems and, as such, must be properly modelled in stability studies. However, the static and dynamic characteristics of a load are commonly unknown, extremely nonlinear, and are usually time varying. Consequently, a measurement-based approach for determining the load characteristics would offer a significant advantage since it could update the parameters of load models directly from the available system measurements. For this purpose and in order to accurately determine load model parameters, a suitable parameter estimation method must be applied. The conventional approach to this problem favors the use of standard nonlinear estimators or artificial intelligence (AI)-based methods. In this paper, a new solution for determining the unknown load model parameters is proposed—an improved particle swarm optimization (IPSO) method. The proposed method is an AI-type technique similar to the commonly used genetic algorithms (GAs) and is shown to provide a promising alternative. This paper presents a performance comparison of IPSO and GA using computer simulations and measured data obtained from realistic laboratory experiments. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08858977
- Volume :
- 30
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Power Delivery
- Publication Type :
- Academic Journal
- Accession number :
- 101734290
- Full Text :
- https://doi.org/10.1109/TPWRD.2014.2301219