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The mismatch between the total active power generation and consumption along with system losses due to variation in operating condition with time in a power system causes the deviations in frequency and tie-line power exchanges between the different areas from their nominal values. Prior to implementation of automatic generation control (AGC) scheme, for maintaining system frequency at the desired value, one generating unit is usually chosen as the regulating unit which is adjusted manually to achieve the generation-load balance and the remaining units are controlled with speed droop to share the load in proportion to their ratings. After implementation of AGC scheme, multiple units in a system can automatically participate in regulation of frequency to improve stability, efficiency and economy. AGC with the help of multiple inputs and computer based control scheme, provides the most economical way to reduce the frequency deviations rapidly and also maintains the scheduled tie-line power flow at normal operating conditions with small disturbances (Miller &Malinowski, 1994). According to changes in demand and generators set point within the control areas, AGC monitors tie-line power flows and system frequency to calculate the required generation change in order to maintain the area control error (ACE) at a minimum value. The ACE popularly known as regulated output of AGC should be adjusted to zero as quickly as possible by using various secondary controllers to retain nominal frequency and scheduled tie-line power flows (Kundur, 2009).
It is investigated from literature that numerous control strategies have been recommended by several researchers over worldwide to achieve better dynamic performance for AGC of an interconnected power system. The conventional controllers like PI and PID are more commonly used in the field of power industries because of their advantages like ease to implement and low cost. A realistic multi source power system considered by (Paramar, Majhi and Kothari, 2012) for the study of AGCincluding generation rate constraint (GRC) as nonlinearity. The parameters of PI controller are tuned using a hybrid bacterial foraging optimization and particle swarm optimization (BFO-PSO) technique for AGC of an interconnected two and three-area power systems employed with nonlinearities (Panda, Mohanty and Hota, 2013). The performance of teaching learning based optimization (TLBO) based proportional-integral-double derivative (PIDD) controller verified in (Sahu, Sekhar and Panda, 2016) initially, for a two area system and then for a three area multi source system considering nonlinearities. According to Araki & Taguchi, (2003) the performance of two degree of freedom-PID (2D-PID) controller is superior over conventional PID controller due to the presence of two more extra controlling parameters (more flexibility) to tune. Hence, differential evolution (DE) optimization technique implemented (Sahu, Panda and Rout, 2013) to determine the parameters of a 2D-PID controller for AGC study of a two-area power system. Similarly, the TLBO algorithm employed to optimize the gains of 2D-PID controller in a multi-source power system (Sahu, Panda, Rout and Sahoo, 2016).