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Top1. Introduction
Low-frequency oscillations are observed in power system when exposed to disturbance owing to the nonlinear dynamics and the complex characteristics of the power system. For the insufficiency of damping torque in the synchronous generator, the separations occur if adequate damping is not provided to compensate it. Due to these negative implications, researchers and engineers have been continually tasked to come up with simple, effective and economical strategies to stabilize the power system. In early 1960s, fast-acting, high-gain automatic voltage regulator (AVR) was applied in the generator excitation system which in-turn invites the problem of low frequency electromechanical oscillations in the power system by reducing the damping torque. The power system stabilizer (PSS), with an addition of supplementary stabilizing signal to the excitation system (Ugalde-Loo, Acha & Liceaga-Castro, 2010; Arrifano & Oliveira, 2007) emerged as a simple and cost-effective approach (Kashki & Abido, 2010) in reducing the low-frequency oscillations. The use of PSS in power system is economical and becomes successful in improving the power system stability and is expected to be installed on many generators connected to the system. PSS generates a supplementary stabilizing signal that is added to excitation control loop of generating unit to produce extra damping. The uniformly adopted type of PSS is known as conventional PSS (CPSS) (Talaq, 2012) which consists of the lead-lag type components. Similar to CPSS, a proportional-integral-derivative (PID) controller (Bevrani & Hiyama, 2011) may also be connected to alter the signal of the AVR to damp-out the small signal oscillations. Since PSSs are tuned at the nominal operating point, damping is adequate only in the vicinity of those operating points. But, since power systems are highly nonlinear in nature, therefore, the machine parameters change with loading and time. Many approaches based on modern control theory have been applied to design different PSS structures such as adaptive controller (Wu & Malik, 2006), Fuzzy logic controller (Hossenizadeh & Kalam, 1999) and extended integral controller (Hoensu & Hyun, 2002). In (Ellithy et al., 2014) proposed the design of the PSSs based on 
-controller to enhance power systems stability and improve power transfer capability using lead-lag PSS structure. Damping torque technique is applied to tune the PSS parameters and the results have been verified by eigen value analysis and time-domain simulations. In (Farahain et al., 2015) online trained fuzzy neural network controller (OTFNNC) derived by the Lyapunov stability has been employed to improve the stability in a power system. The overall dynamic performance of the power system by using a comprehensive analysis of the effects of the different CPSS has been presented in (Kundur, Klein, Rogers & Zywno, 1989). (Khezri et al., 2015) presents intelligent fuzzy-based coordinated control for AVR and PSS, to prevent losing synchronism after sudden faults and to achieve appropriate post-fault voltage level in multi-machine power systems. The proposed controller is applied on 11-bus 4-generator power system. 
Optimization techniques (Yang, 1997; Asgharian, 1994) have been designed for the tuning of the PSS problem. However, the importance and difficulties in the selection of the weighting function of the 
optimization problem have been reported. In addition, additive and / or multiplicative uncertainty representation cannot treat situations where a nominal stable system becomes unstable after being perturbed (Vidyasagar & Kimura, 1986) On the other hand, the order of the 
based stabilizer is as high as that of the plant. This gives rise to complex structure of the stabilizers and reduces its applicability. In the past few years, Artificial Neural Network (ANN) techniques have been used for designing PSS (Zhang, Chen, Malik & Hope, 1993; Mahabuba & Khan, 2009). The ANN approach has its own merits and demerits. Even though the performance of the system is improved by the ANN based controller, yet, it suffers from long training time, the selecting number of layers and the number of neurons in each layer. Another technique like pole shifting is illustrated in (Kothari, Bhattacharya & Nanda, 2002; El-Sherbiny, Hasan, El-Saady & Yousef, 2003) to design PSS. However, the above stated demerits has been overcome by optimization methods (Izquierdo et al., 2017; Kaliannan et al., 2017). Here, the tuning process is converted to a constrained optimization problem which is solved by using an optimization algorithm. Fuzzy logic based PSS (FLPSS) and adaptive controller-based PSS with some capabilities have been developed in the recent years (Khodabakhshian, 2005; (Kvasov, Menniti, & Pinnarelli, 2008; Chaturvedi & Malik, 2008; Ramirez-Gonzalez & Malik, 2008).