Title :
A particle swarm optimiser with passive congregation approach to thermal modelling for power transformers
Author :
Tang, W.H. ; He, S. ; Prempain, E. ; Wu, Q.H. ; Fitch, J.
Author_Institution :
Dept. of Electr. Eng. & Electron., Liverpool Univ., UK
Abstract :
This paper employs an intelligent learning technique based on a particle swarm optimiser with passive congregation (PSOPC) algorithm to identify the thermal parameters of a simplified thermoelectric analogous thermal model (STEATM) for transformers, based upon only a few onsite measurements instead of experimental methods. The model outputs deliver good agreements with the onsite data based upon a single set of parameters obtained from the PSOPC learning with a fast convergence rate. The simulation results are compared with that obtained using an artificial neural network (ANN) approach.
Keywords :
learning (artificial intelligence); neural nets; particle swarm optimisation; power transformers; intelligent learning; particle swarm optimiser; passive congregation algorithm; power transformer; simplified thermoelectric analogous thermal model; thermal modelling; thermal parameter; Artificial neural networks; Convergence; Particle swarm optimization; Power generation; Power system modeling; Power system simulation; Power transformers; Temperature; Thermal loading; Thermoelectricity;
Conference_Titel :
Evolutionary Computation, 2005. The 2005 IEEE Congress on
Conference_Location :
Edinburgh, Scotland
Print_ISBN :
0-7803-9363-5
DOI :
10.1109/CEC.2005.1555039
