Automatic Generation Control of Multi-area Interconnected Power Systems Using ANN Controller Khaled Alzaareer 1* , Ali Q. Al-Shetwi 2 , Claude Zeyad El-bayeh 3 , Mohammad Bany Taha 4 1 University of Quebec (ETS), 1100 Rue Notre-Dame Quest, Montreal Québec H3C 1K3, Canada 2 Universiti Tenaga Nasional, 43000 Kajang, Selangor, Malaysia 3 Concordia University, 1250 Guy St, Montreal, Quebec H3H 2L3, Canada 4 Ericsson, GAIA Department, 8275 Trans Canada Route, saint-Laurent, Quebec H4S 0B6, Canada Corresponding Author Email: Khaled.alzaareer.1@ens.etsmtl.ca https://doi.org/10.18280/ria.340101 ABSTRACT Received: 1 October 2019 Accepted: 20 December 2019 Load as well as power flow in tie-line are continuously varying in interconnection power systems. This paper presents an efficient method based on artificial intelligence control for automatic generation control (AGC) of a three-area power network. The control method implements Artificial Neural Network (ANN) to damp the frequency deviation and the fluctuation in the tie line power caused by load disturbances. The performance of the proposed controller is compared with classical control methods (PI and PID). The results showed that ANN-based control method is more efficient than others approaches. In this paper, MATLAB/SIMULINK package is used to investigate the results. Keywords: automatic generation control, PI controller, PID controller, artificial neural network controller (ANN), tie line, Area Control Error (ACE), MATLAB/SIMULINK 1. INTRODUCTION With the development of interconnected power system, the necessity for automatic generation control has been raised in power system design and operation [1]. In real power systems, the load demands are continuously and randomly varying. When such a disturbance is occurred on power system, tie-line power interchange and area frequency change too. The ability to meet load changes (i.e. disturbances) by action of generators is weak. This is due to the physical consideration which can cause unbalance between the actual generation power and the scheduled one [2]. Since the interconnected power systems require operating at constant frequency with adequate and consistent electric power, an automatic generation controller design is needed to eliminate the variations in system frequency and sustain the power transfer between areas [3]. Automatic Generation Control (AGC) can be defined as a scheduling process for the power outputs by system generators within a particular area as a reaction for any change in network frequency, tie line transfer power in order to keep them within predetermined limits [4, 5]. Several control strategies have been developed in order to keep the network frequency and tie-line transfer power within acceptable limits. The most common controller employed for AGC is based on classical control theory. These conventional controllers can be found in the researches [6-9]. These studies observed that an integral controller is very simple to implement and can provide zero steady state deviation. However, this kind of controller gives poor dynamic response. However, power systems operate at different operating conditions. The current operating condition continuously varies according to demand amounts of consumers while the gain values of the conventional controllers are constants. This makes the conventional controllers are not valid for different operating conditions. Moreover, the goal of Automatic Generation Control is to keep the steady state errors at zero and to keep the transient behavior of the network stable. Therefore, Artificial Intelligent (AI) controllers are more suitable for that improvement. Recently, Different artificial intelligence-based methods have been proposed in the literature. Fuzzy control methods [10-12], bacterial foraging- based optimization technique [13], and neural networks [3, 14, 15] are applied to AGC problem. In this work, a comparison between the Artificial Neural Network based control and the classical control for automatic generation control is investigated. 2. FREQUENCY RESPONSE MODELING To study the dynamic behavior of power system, the mathematical modeling of each of the components of power system is needed. One of the most common methods used in the modeling is the transfer function method. 2.1 Generator model The model of any generator dynamic can be done by using swing equation given in Eq. (1). 2    2   2 =   −   (1) With speed expressed in per unit and by taking Laplace transform of Eq. (1), we obtain: () = 1 2 [  () −   ()] (2) Revue d'Intelligence Artificielle Vol. 34, No. 1, February, 2020, pp. 1-10 Journal homepage: http://iieta.org/journals/ria 1