- Tytuł:
- Estimation of composite load model parameters using improved particle swarm optimization
- Autorzy:
-
Regulski, P.
Gonzalez-Longatt, F.
Terzija, V. - Powiązania:
- https://bibliotekanauki.pl/articles/410557.pdf
- Data publikacji:
- 2012
- Wydawca:
- Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
- Tematy:
-
load modeling
parameter estimation
particle swarm optimization (PSO)
composite load model - Opis:
- 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.
- Źródło:
-
Present Problems of Power System Control; 2012, 2; 41-51
2084-2201 - Pojawia się w:
- Present Problems of Power System Control
- Dostawca treści:
- Biblioteka Nauki
Artykuł