Online Tuning of Power System Stabilizers using Fuzzy Logic Network with Fuzzy C-Means Clustering
Hajizade Kanafgorabi M* and Dr. Karami A
Department of Electrical Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran
- *Corresponding Author:
- Hajizade Kanafgorabi M
Electrical Engineering Department
Faculty of Engineering
University of Guilan
Rasht, P.O. Box 3756,Iran
E-mail: [email protected]
Received Date: September 10, 2014; Accepted Date: December 30, 2014; Published Date: January 15, 2015
Citation: Hajizade Kanafgorabi M, Karami A (2015) Online Tuning of Power System Stabilizers using Fuzzy Logic Network with Fuzzy C-Means Clustering. J Electr Electron Syst 4:136. doi:10.4172/2332-0796.1000136
Copyright: © 2015 Hajizade Kanafgorabi M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Power system stabilizers (PSS) have been widely used to enhance damping due to the electromechanical low frequency oscillations occurrence in power systems. In this paper, a new method is used for the online tuning of parameters of conventional power system stabilizers (CPSS) using fuzzy logic. Fuzzy logic enables mathematical modeling and computation of some nonlinear parameters of the system, which are usually, derived empirically by utilization of expert knowledge rules. Various literatures has shown that fuzzy logic controller is one of the most useful methods for expert knowledge utilization. This type of controller is adaptive in nature and can be used successfully as a power system stabilizer. The design of fuzzy logic controllers is mainly based on fuzzy rules and input/output membership functions. Simple and efficient clustering algorithms allow data classification in distinct groups using distance and/or similarity functions. In the present paper, the optimum generation of fuzzy rules base using Fuzzy C-means (FCM) clustering technique is used. In fact, data are classified and the number of fuzzy rules which depends on convergence radius is determined. Finally, the performance of proposed FCM controller is compared with that of conventional controller. The active power, reactive power and bus voltages used as inputs to the fuzzy logic network based power system stabilizer and the parameters of the optimum stabilizer , i.e. gain factor as well as time constants of the lead/lag compensator, are
the outputs of the proposed system. The design method has been successfully implemented on a single machine power system connected to an infinite bus over various operating conditions.