Recurrent Functional-Link-Based Fuzzy-Neural-Network-Controlled Induction-Generator System Using Improved Particle Swarm Optimization

• Published in: IEEE transactions on industrial electronics (1982) Vol. 56; no. 5; pp. 1557 - 1577
• Main Authors: Lin, Faa-Jeng, Teng, Li-Tao, Lin, Jeng-Wen, Chen, Syuan-Yi
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2009; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: AC generators; Alternating current; Analog-digital conversion; Control systems; DC generators; Direct current; Electric converters; Electric potential; Electric power generation; Electric variables control; Frequency conversion; Functional-link neural network (FLNN); Fuzzy control; induction generator (IG); Inverters; Learning; Optimization; Particle swarm optimization; particle swarm optimization (PSO); power converter; Voltage; Voltage control
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2008.2010105

A recurrent functional-link (FL)-based fuzzy-neural-network (FNN) controller with improved particle swarm optimization (IPSO) is proposed in this paper to control a three-phase induction-generator (IG) system for stand-alone power application. First, an indirect field-oriented mechanism is implemented for the control of the IG. Then, an AC/DC power converter and a DC/AC power inverter are developed to convert the electric power generated by a three-phase IG from variable frequency and variable voltage to constant frequency and constant voltage, respectively. Moreover, two online-trained recurrent FL-based FNNs are introduced as the regulating controllers for both the DC-link voltage of the AC/DC power converter and the AC line voltage of the DC/AC power inverter. Furthermore, IPSO is adopted to adjust the learning rates to improve the online learning capability of the recurrent FL-based FNNs. Finally, some experimental results are provided to demonstrate the effectiveness of the proposed recurrent FL-based FNN-controlled IG system.