Title :
Intelligent Fault Diagnosis of Inverter Based on Improved PSO Algorithm
Author :
Liu, Qing-rui ; Han, Ning ; Wang, Zhong-jie
Author_Institution :
Coll. of Electr. Eng. & Inf. Sci., Hebei Univ. of Sci. & Technol., Shijiazhuang, China
Abstract :
In order to overcome drawbacks of the BP network, an improved non-linear dynamic adaptive particle swarm algorithm is used in this paper. The value of inertia weight can automatically change with the change of fitness value for avoiding particles premature. The particle velocity avoids getting into local minima by adding a negative disturbance. This method is applied to the inverter circuit. Simulation results indicated that: improved particle swarm algorithm was superior to BP algorithm in accuracy, convergence speed, ability to search the optimal solution.
Keywords :
backpropagation; fault diagnosis; invertors; particle swarm optimisation; BP algorithm; BP network; convergence speed; fitness value; improved PSO algorithm; improved particle swarm algorithm; inertia weight; intelligent fault diagnosis; inverter circuit; local minima; negative disturbance; nonlinear dynamic adaptive particle swarm algorithm; particle velocity; Artificial neural networks; Circuit faults; Electron tubes; Fault diagnosis; Inverters; Particle swarm optimization; Training; fault diagnosis; improvedPSO algorithm; neural network design;
Conference_Titel :
Electrical and Control Engineering (ICECE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-6880-5
DOI :
10.1109/iCECE.2010.938
