## 5 Stability Enhancement Using FACTS Devices
### Voltage Stability
The authors concluded that UPFC leads. A detailed investigation on the effects of FACTS devices (SVC, UPFC, SSSC, STATCOM) on the enhancement of voltage stability in multi-network systems is described in [155]. In general, it is concluded that all FACTS devices increased the voltage stability margin. In-depth comparison between FACTS devices shows that the UPFC is superior in this role and the SSSC scores the least. Another comprehensive comparison of FACTS controllers (SVC, STATCOM, SSSC, UPFC) for voltage stability and reactive power sustainability was performed by [PERSON] [156] using PSAT/MATLAB v2.1.2. Results reflect that the UPFC enhanced stability and reduced the loss of reactive power. Consequently, FACTS controllers (TCSC, UPFC, and IPFC) are placed in a Nigerian 58 bus, 330 kV network to enhance power transfer capability by controlling transmission voltage, reducing reactive power losses, and damping power system oscillations [157]. The authors concluded that the UPFC is more efficient than the SSSC and IPFC. Table 4 shows the role of various FACTS devices and their pros and cons. Similarly, several novel techniques like classical trial and error, partial swarm optimization (PSO), ant colony optimization (ACO), and hybrid PSO-ACO for tuning the STATCOM PI controller, to provide voltage regulation support, are presented in [158]. It is concluded that biological methods (PSO, ACO, and hybrid PSO-ACO) have better performance than classical methods (trial and error). Similarly, various conventional optimization techniques such as modal analysis [159], index method [160], numerical optimization [161], sensitivity method [162], and eigenvalue [163] are used to optimize the FACTS devices in voltage stability studies. Furthermore, some other techniques like the analytical approach [164], mixed-integer optimization [165], and linear programming [166] are also used. Moreover, artificial-intelligence-based optimization techniques used for voltage stability analysis are the genetic algorithm (GA) [167], particle swarm optimization (PSO) [160], artificial neural network (ANN) method [168], and fuzzy linear programming [169]. In addition, hybrid techniques used to optimize FACTS devices for voltage stability studies are OPF and GA [170], EP and SA, and much more. Similarly, the authors of [168] concluded that artificial-intelligence-based optimization algorithms such as PSO, GA, and harmony search algorithms are still preferred. A detailed discussion about optimization techniques is provided in the literature [19,171]. But the optimal location of FACTS devices has great importance in power networks. However, one important aspect is cost because FACTS devices require a huge initial investment. Therefore, proper optimal location, size, and capability cannot be found without using optimization techniques and an in-depth study of power networks [172,173]. Therefore, each objective function (voltage stability, transient stability, reactive power planning, cost minimization, congestion management, etc.) for power optimization techniques plays a key role in this regard. That is why optimization techniques can be classified as single or multi-objective based on the objective functions under study. Furthermore, based on objectives' priority perspectives, multi-objective optimization techniques are categorized into non-dominated or dominated algorithms. Dominated optimization techniques give priority to one specific objective function over other objective functions whereas, in the non-dominated algorithms, the compromise between various objective functions results in a Pareto optimal front of solutions. In general, it is concluded that the selection of optimization techniques and required goals is a trade-off in selecting the FACTS devices and type of algorithm.