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Top1. Introduction
For modern power systems, frequency must be constant. The frequency variation is not acceptable in current power system for worldwide. Frequency is very important for multi-area power generating sources interconnected with hybrid resources. Automatic generation control of power systems, voltage-frequency play a major role as both voltage and frequency should be properly controlled. Load Frequency Control (LFC) maintains the stable frequency for a demand level after disturbance in load connected to different area. The contribution of power exchange for different load areas are controlled by LFC in the power systems. The quality power supply can be achieved through the help of AGC for multi-area interconnected power systems with diverse energy sources. As robust power demand is a need of mankind globally, when load penetrates from its defined value with perturbation, the state of the system can change from normal to abnormal condition. AGC must identify the deviation in frequency and maintained it to constant system frequency. As the operation of interconnected power systems should be balanced between generated powers with total load demand plus system losses. If operating point differ the system frequency can deviates, cumulative cause shows unbalanced power in the exchange of areas, result may undesirable effect (Elgered, 2016; Kothari, & Nagrath, 2009; Ibraheem, Kothari, & Kumar, 2005). A single variable called as Area Control Error (ACE) is a combination of two variables one is frequency, another is tie-line power exchange. Many good ideas reflected by researchers for AGC problem, through the design of AGC regulators for uncertainty or variation, load characteristics, excitation control and other link like Alternating Current (AC)/Direct Current (DC) (Cohn, 1957; Elgerd, & Fosha,1970; Quazza,1971; Calvoic, 1971; Quazza, 1966).
In the last decade, the modern concept for AGC like Artificial Neural Network (ANN), Genetic Algorithm (GA), and Fuzzy Logic Algorithm (FLA) is used to make our AGC simple and robust, as thermal power plant associating with solar energy in Photovoltaic (PV) modules, wind turbine, plug-in Electric Vehicle (EV), micro-grid, smart grid, and Super Conducting Magnetic Energy Storage (SMES) (Ross, 1966; Kalman, 1964; Yu, Vongsuriya, & Wedman, 1970). This review paper (Kumar, 2019) gave a brief exploration of recent research articles written by various authors/researchers/technocrats used different techniques of Artificial Intelligence (AI) and Soft Computing (SC) techniques. A good number of articles are reviewed and the modern AI with SC techniques used for AGC in which different algorithms are like Ant Colony Optimization (ACO) (Jagatheesan, Dey, Anand, & Ashour, 2015), Bacteria Foraging Optimization Algorithm (BFOA) (Arya, & Kumar, 2016), Differential Evolution (DE)-Particle Swarm Optimization (DEPSO) (Singh, 2017; Sahu, Panda, & Pati, 2014), Gravitational Search Algorithm (GSA) (Kumar, et al., 2017; Khadanga, et al., 2017), Grey Wolf Optimizer (GOW) Algorithm (Saikia, et al., 2015; Guha, et al., 2016; Srinivasarathnam, et al., 2019; Padhy, et al., 2017; Aghaei, et al., 2013; Singh, et, al. 2017; Soni, et al., 2016; Abazari, et al., 2019), Firefly Algorithm/Hybrid Firefly Algorithm (FA/hFA) (Pradhan, et al., 2016; Padhana, et al., 2014; Chien, et al., 2011), Krill Herd Algorithm (KHA) (Guha, et al., 2016), Modified Grey Wolf Optimization (MGOW) (Aghaei, et al., 2013), Modified Harmony Search Algorithm (MHSA), Particle Swarm Optimization (PSO) (Nasiruddin, et al., 2012; Pathak, et al., 2018), Quasi-Oppositional Harmony Search Algorithm (QOHS) (Shiva, et al., 2015; Shiva, et al., 2016), Self Adaptive Modified Bat Algorithm (SAMBA), Teaching Learning Based Optimization (TLBO) (Sahu, et al., 2016), Whale Optimization Algorithm (WOA) (Hasanien, et al., 2018). These algorithms are justified by its authors with certain parameters, acceptability and also with their limitations.