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A modern power system consists of many areas and interconnected by tie lines. It is well known that the load demand varies every instant. For stable operation of the power system, the deviations in system frequency & tie-line power exchange must to be minimum. If the active power generated becomes less than the load power demand at any instant of time during sudden disturbances, the system frequency decreases and vice versa. The objective of an interconnected power system is to maintain spinning reserve, nominal frequency in each area, voltage profile and tie-line power exchange within their scheduled values by matching the instantaneous load demand and generation. Automatic generation control (AGC) plays an important task to rapidly balance the power demand & power generation, thus minimizing the system frequency deviation. The area control error (ACE) is the controlled output of AGC. In order to make the system frequency and tie line power errors to zero in AGC, ACE is driven to zero by adopting suitable controller (Kundur, 1994; Elgerd, 1982)
Literature survey indicates that, many researchers have proposed several control strategies for AGC of power systems to maintain the system frequency and tie line power flow at their prescribed values at nominal loading as well as during small and slow load perturbations. In order to achieve stringent power quality requirements, sophisticated tools based on most accurate and realistic models with high degree of reliability and faster convergence characteristics are required in AGC. However, substantial progress has been made in the development of bio-inspired controllers and their applications to power system. The literature survey reveals that Cohn had started the early work on AGC in 1957 (Cohn, 1957). The design and development of modern optimal controller for interconnected power system was initiated by Elgerd and Fosha (1970). IEEE Committee Report, described the details of dynamic models for steam and hydro turbines in power system studies in 1973 (IEEE Committee Report, 1973). AGC in decentralized area-wise optimal solution is reported by Calovic in 1984 (Calovic, 1984). The earlier control strategies with various control aspects for AGC by incorporating additional control devices are reported by Kumar and Kothari (2005). The application of gain scheduling control in terms of robustness to wide range of operating conditions, simple and also easy for implementation to AGC of interconnected power system was proposed by Lee et al. (1991). The rapid development and expansion in size and complexity of electric power systems due to nonlinear load characteristics and variable operating points has necessitated the use of intelligent controllers to address satisfactorily the performances under small load perturbations. Several intelligent controllers such as hybrid firefly and pattern search algorithm, Lozi map-based chaotic algorithm (LCOA) based PID controllers, evolutionary fuzzy PI controller, combined intelligent techniques, fuzzy based PSO controller, generalized neural network approach for AGC studies have been proposed by researchers (Sahu et al., 2015; Farahani et al., 2012; Juang & Lu, 2004; Karnavas & Papadopulos, 2002; Ghosal, 2004; Chaturvedi, 1999). Other algorithms reported in load frequency control are gain scheduling method with adaptive fuzzy application, genetic algorithm(GA) based PI/PID controller, PSO based fuzzy PID controller, self-tuning fuzzy PID controller, adaptive neuro-fuzzy interference system (ANFIS) for AGC studies for interconnected power system (Chang et al., 1997; Cam et al., 2005; Pinkang, 2002; Yesil et al., 2004; Khuntia, 2012; Aström & Hagglund, 1995).