###### Abstract
Furthermore, STATCOM and static synchronous series compensator (SSSC) are coordinated with PSS-STATCOM, PSS-SSSC and unified power flow controllers (UPFC), and UPFC with PSS [18, 19, 20, 21]. Optimisation techniques have been used to compute the appropriate parameters of PSS and FACTS-based controllers. The controller parameters are modified by optimisation techniques, such as conventional, deterministic, heuristic, and hybrid types. The heuristic technique solves optimisation difficulties using stochastic methods, and its improved techniques are known as metaheuristic algorithms. Numerous metaheuristic optimisation methods have been used to tune the parameters of controllers of PSS and FACTS devices [4]. Sequential Quadratic Programming [8] and Genetic Algorithms (GA) are used to design a robust PSS and a coordinated PSS with UPFC [22, 23, 24]. The particle swarm optimisation (PSO) algorithm was also presented to design a damping controller [5, 25, 26, 27] with cuckoo, coyote optimisation and BAT optimisation algorithms [10, 28, 29, 30, 31]. The Firefly optimisation algorithm was used to design the SVC damping controller [32,33]. Various heuristic algorithms for optimisation use stochastic methods that have deficiencies in solution convergence. Tabu search (TS) has poor global search efficiency, and simulated annealing (SA) has limitations in practical applications. TS, GA, SA and PSO have deficiencies in convergence rates, that is, a local minimum stagnation problem. Several proposed methods provide good exploration in the search space but have limitations in exploitation, or vice versa [4]. The main advantage of the sine cosine algorithm (SCA) is that it allows both exploration and exploitation in the search space, that is, for the first and second halves of the iterations, respectively. The formulation of objective functions and the design of damping controllers for PSS and FACTS are accomplished by using a linearised model of the system. Single objective function and multi-objective function are the two types of objective functions used during the tuning of the parameters of the damping controllers to justify the angle stability of the power system. The single objective function as an indicator of stability is indicated by damping factors, or the damping ratios of electromechanical oscillation modes. The use of damping ratios and damping factors as indicators, and the optimisation of the indicators, moves eigenvalues towards the real axis line and to the left of the imaginary axis line of the complex s-plane, respectively. The damping ratio limits the magnitude of the overshoot of oscillations and, together with the damping factor, increases the performance of the damping controllers. In this work, a new metaheuristic algorithm called SCA was considered for optimising the parameters of the PSS in the SMIB system. The state space representation of a system was considered to investigate the angle stability of the power system. An SMIB system was used as a test system to examine the angle stability via eigenvalue analysis. The PSS parameters were optimised to shift the eigenvalues more towards the left of the imaginary axis of the complex s-plane to ensure the safety margin of the system stability. The proposed technique was used to analyse the angle stability of the power system under five different loading conditions and compared with MFO and EP algorithms. The proposed algorithm improved the power system stability by damping low frequency oscillations.