[SYLWAN., 158(5)]. ISI Indexed 289 Evaluation of interval type-2 fuzzy membership function & robust design of power system stabilizer for SMIB power system D. K. Sambariya 1,1 and R. Prasad 2 1,2 Department of Electrical Engineering, Indian Institute of Technology Roorkee,Roorkee-247667, India 1 E-ma il:dksamba riya_2003@yahoo.com, 2 E-ma il: rpdeefee@iitr.ernet.in In this paper the evaluation & performance analysis of an interval type-2 fuzzy logic controller (IT2 FLC) as a power system stabilizer (PSS) is carried out. The single machine infinite bus (SMIB) system is considered for evaluation and implementation of IT2 FLC as PSS. The input signals to the controller are considered as speed deviation & acceleration. The evaluation of the controller is considered with 20 separate types of membership functions (MFs) and treated as IT2 FPSS on SMIB system to evaluate the performance of each. The performance of these MFs is evaluated graphically as well as in terms of ISE, IAE & ITAE as the performance index. The better suitable MF as FPSS is decided out of considered 20 MFs. The selected MF based IT2 FPSS is tested for small signal performance analysis of an SMIB system with wide operating conditions of a power system. The performance with IT2 FPSS is compared to the performance of the power system without PSS & with conventional PSS. Keywords: Interval type-2 fuzzy power System Stabilizer, IT2 Fuzzy M embership Function, SM IB Power System, Conventional Power System Stabilizer. 1. Introduction The electric power demand is increasing with time resulting in extension of the power system network & constraints as well. These large networks are used to be operated close to their dynamic stability, even though; it may lead to major system blackouts because of small signal oscillations (0.1-3Hz). these oscillations can be damped-out with the use of power system stabilizers (PSSs) because of their low cost & easy implementation [1, 2]. These PSSs are the fixed parameter controllers, designed over a nominal operating point to get desired performance at this (nominal) point as well as expected over a wide range of operating conditions & varying system conditions. The above PSSs are designed by use of linear control theory with fixed parameters, therefore, are termed as conventional PSS (CPSS), which constitutes a gain amplifier block, lead-lag network & washout block [1]. These CPSS are limited in performance because these may lose effective damping robustness for a wide range of operating conditions of a power system [3]. On the other hand, recent advances in technology have led to intelligent and learning PSS design methods using artificial neural networks (ANNs) [4, 5], fuzzy logics [6-9], neuro-fuzzy [10, 11] and stochastic methods like GA [12-15], PSO[16]. These methods enable to design a PSS by including non- linearity and parameter uncertainty of power system and provide optimal stabilizing performance for a wide range of operating conditions. ANN based PSS design methods uses the gradient algorithm for learning its parameters either with input/output data [17, 18] or on-line at different operating points of the power system. In fuzzy logic based PSS design, the rules are either defined in the consent of a system expert or using trial & error method. Online learning (adaptive) method of parameter tuning uses a separate controller (PSS) and an identifier network, where the stability of a closed-loop system depends closely on the performance of these networks. It makes a model of a system of inexact information. Thus, it is suitable for non-linear & complex systems whose mathematical modelling is impossible. The seminal work on fuzzy sets was introduced by Zadeh in 1965 [19] to manipulate the information & data which are uncertain & un-probabilistic. The next generation to type-1 fuzzy sets is the interval type-2 fuzzy sets (IT2-FS) were again introduced by Zadeh in 1975. The stronger part of Interval type-2 fuzzy set is to model imprecision & uncertainty in a better way. These IT2-FS were developed by Mendel, who characterized IT2-FS as the footprint of uncertainty (FOU). In most of the cases, IT2 FS performance is better than its type-1 counterpart [20, 21]. 1 Corresponding Author: D. K. Sambariya; Telephone: +91-9982252205, +91-1332-270166