Title: Adaptive voltage regulation of self excited induction generator using FACTS controllers

Authors: Mohamed I. Mosaad; Fawzan Salem

Addresses: Higher Technological Institute (HTI), 10th of Ramadan City – Industrial area 2, near the small industries complex, Egypt; Yanbu Industrial College, P.O. Box: 30436, Yanbu Al-Sinaiya 21477, Kingdom of Saudi Arabia ' Power Electronics and Energy Conversion Department, Electronics Research Institute (ERI), Elbehous Street, Dokki, Cairo, Egypt; Yanbu Industrial College, P.O. Box: 30436, Yanbu Al-Sinaiya 21477, Kingdom of Saudi Arabia

Abstract: This paper introduces an adaptive terminal voltage regulation of a standalone self excited induction generator using flexible AC transmission systems (FACTS). FACTS in the form of static VAR compensator (SVC) is introduced. SVC is used to regulate the terminal voltage of self excited induction generator-based (SEIG) wind generator working with variable speed and load. Since, the wind generator operates over a wide range of operating conditions; the terminal voltage of the induction generator is not regulated. This terminal voltage is controlled by adapting the value of the excitation capacitance of SVC using artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS). Changing the excitation capacitance is performed by controlling the firing angle of SVC under different operating conditions to control the terminal voltage. A mathematical model of SEIG with variable load and speed along with SVC is developed and simulated in MATLAB to get the excitation capacitance required for voltage control. Simulation results are used to train both ANN and ANFIS for on-line prediction of the suitable firing angles required to control the terminal voltage of the system under these operating conditions. Results signify the supremacy of ANFIS over ANN in terms of performance measures.

Keywords: self excited induction generators; SEIG; flexible AC transmission systems; FACTS controllers; static VAR compensator; SVC; adaptive neuro-fuzzy inference systems; ANFIS; artificial neural networks; ANNs; fuzzy logic; adaptive voltage regulation; terminal voltage; wind generators; mathematical modelling; voltage control; simulation; wind energy; wind power.

DOI: 10.1504/IJIED.2014.066211

International Journal of Industrial Electronics and Drives, 2014 Vol.1 No.4, pp.219 - 226

Received: 07 Apr 2014
Accepted: 27 Apr 2014
Published online: 20 Dec 2014 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article