Applied Soft Computing 21 (2014) 1–11 Contents lists available at ScienceDirect Applied Soft Computing journal homepage: www.elsevier.com/locate/asoc Application of type-2 fuzzy logic system for load frequency control using feedback error learning approaches Kamel Sabahi, Sehraneh Ghaemi ∗ , Saeed Pezeshki Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran article info Article history: Received 27 May 2013 Received in revised form 25 December 2013 Accepted 14 February 2014 Available online 20 March 2014 Keywords: Load frequency control Type-2 fuzzy logic system Feedback error learning and restructure power system abstract In this paper, the type-2 fuzzy logic system (T2FLS) controller using the feedback error learning (FEL) strategy has been proposed for load frequency control (LFC) in the restructure power system. The orig- inal FEL strategy consists of an intelligent feedforward controller (INFC) (i.e. artificial neural network (ANN)) and the conventional feedback controller (CFC). The CFC acting as a general feedback controller to guarantee the stability of the system plays a crucial role in the transient state. The INFC is adopted in forward path to take over the control problem in the steady state. In this work, to improve the perfor- mance of the FEL strategy, the T2FLS is adopted instead of ANN in the INFC part due to its ability to model uncertainties, which may exist in the rules and measured data of sensors more effectively. The proposed FEL controller has been compared with a type-1 fuzzy logic system (T1FLS) – based FEL controller and the proportional, integral and derivative (PID) controller to highlight the effectiveness of the proposed method. © 2014 Elsevier B.V. All rights reserved. 1. Introduction One of the principle aspects of automatic generation control (AGC) of power system is the maintenance of frequency and power change over the tie-lines at their scheduled values. Therefore, it is a simultaneous load frequency control (LFC) [1]. In LFC problem, each area has its own generator(s), and it is responsible for its own load and scheduled interchanges with neighbouring areas. The tie- lines are utilities for contracted energy exchange between areas and they provide inter-area support in abnormal conditions. Area load changes and abnormal conditions lead to mismatches in fre- quency and scheduled power interchanges between areas. These mismatches have to be corrected by LFC, which is defined as the regulation of the power output of generators within a prescribed area [2–4]. Therefore, the LFC task is very important in intercon- nected and restructure power systems. It is well known that power systems are nonlinear and uncertain, where the parameters are a function of the operating point, and the loading in power sys- tem is never constant. To control these large scale power systems, the control algorithms must be able to deal with mechanical and electrical nonlinear dynamics and must be operated under impre- cise and uncertain conditions, which are mainly caused by random ∗ Corresponding author. Tel.: +98 411 3393740; fax: +98 411 3300819. E-mail address: ghaemi@tabrizu.ac.ir (S. Ghaemi). load demands. It is obvious that the fixed gain controllers which are designed at nominal operating conditions fail to provide best control performance over a wide range of operating conditions. Thus, some classical adaptive controllers are presented for LFC in [5–8]. Despite the promising results achieved by these controllers, the control algorithms are complicated and require some on-line model identifications. Consequently, model-free approaches are generally preferred to both modelling and controlling purposes of these systems. The most common model-free approaches are using artificial neural networks (ANNs), fuzzy logic systems (FLSs) and fuzzy neural networks (FNNs) [9–14]. The FNN includes advan- tages of both FLS, in handling uncertain information, and ANN, in learning from process [14]. Although these controllers have shown promising results, they have not considered measurement noise and parametric uncertainties of the power system. The straight- forward way to deal with these problems is using of type-2 FLSs (T2FLSs) [15]. The T2FLS is proposed as an extension of the T1FLS which is able to model the uncertainties that invariably exist in the rule base of the system [15]. In type-1 fuzzy sets, membership func- tions are totally certain, whereas in type-2 fuzzy sets membership functions are themselves fuzzy. In other words, a Type-2 fuzzy set can be visualized as a three dimensional, primary and secondary membership function. The primary membership is any subset in [0, 1] and there is a secondary membership value corresponding to each primary membership value that defines the possibility for pri- mary membership. The advantage of the third dimension gives an http://dx.doi.org/10.1016/j.asoc.2014.02.022 1568-4946/© 2014 Elsevier B.V. All rights reserved.