Estimation of composite load model parameters using improved particle swarm optimization

• Autorzy: Regulski P. , Gonzalez-Longatt F. , Terzija V.
• Języki publikacji: EN

Abstrakty

EN

Power system loads are one of its crucial elements to be modeled in stability studies. However their static and dynamic characteristics are very often unknown and usually changing in time (daily, weekly, monthly and seasonal variations). Taking this into account, a measurement-based approach for determining the load characteristics seems to be the best practice, as it updates the parameters of a load model directly from the system measurements. To achieve this, a Parameter Estimation tool is required, so a common approach is to incorporate the standard Nonlinear Least Squares, or Genetic Algorithms, as a method providing more global capabilities. In this paper a new solution is proposed -an Improved Particle Swarm Optimization method. This method is an Artificial Intelligence type technique similar to Genetic Algorithms, but easier for implementation and also computationally more efficient. The paper provides results of several experiments proving that the proposed method can achieve higher accuracy and show better generalization capabilities than the Nonlinear Least Squares method. The computer simulations were carried out using a one-bus and an IEEE 39-bus test system.

Słowa kluczowe

EN

load modelling; parameter estimation; particle swarm optimization (PSO); composite load model

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Present Problems of Power System Control
• Rocznik: 2012
• Tom: Nr 2
• Strony: 41--51
• Opis fizyczny: Bibliogr. 9 poz., rys. 6

Twórcy

autor

Regulski P.

autor

Gonzalez-Longatt F.

autor

Terzija V.

• School of Electrical and Electronic Engineering, The University of Manchester, UK,

Bibliografia

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• [8] MAITRA A., GAIKWAD A., POURGEIK P., BROOKS D., Load Model Parameter Derivation Using an Automated Algorithm and Measured Data, Power and Energy Society General Meeting -Conversion and Delivery of Electrical Energy in the 21st Century, 2008 IEEE, pp. 1–7, 20-24 July 2008.
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