Estimation of Composite Load Model Parameters Using an Improved Particle Swarm Optimization Method

P. Regulski, D. S. Vilchis-Rodriguez, S. Djurović, V. Terzija

Research output: Contribution to journalArticlepeer-review

71 Citations (Scopus)


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.

Original languageEnglish
Article number6734722
Pages (from-to)553-560
Number of pages8
JournalIEEE Transactions on Power Delivery
Issue number2
Publication statusPublished - 1 Apr 2015
Externally publishedYes


  • Composite load (CL) model
  • load modeling
  • nonlinear parameter estimation
  • particle swarm optimization (PSO)


Dive into the research topics of 'Estimation of Composite Load Model Parameters Using an Improved Particle Swarm Optimization Method'. Together they form a unique fingerprint.

Cite this