## 4 Discussion
and the FOD performance analysis's results are superior in the case of fuzzy PID (0.21 with 1% increase in load demand), contrary to PID (0.68 in the same conditions) [68]. Frequency stability using improved FCSs when interconnecting multi-area power systems is the aim of many researchers. Thus [63; 64; 71; 136] used simulations to demonstrate the efficacy of their hybrid type-2 fuzzy controllers and compared their proposed methods with type-1 fuzzy controllers. [PERSON] and his colleagues in [71] combine the UIO (Unknown Input Observer) with interval type-2 control to increase the stability and the unknown input aspect. They demonstrate in their work that the proposed type-2 FLC deals much better with undershoot frequency deviations than type-1 FLCs and PI controllers. The authors of [136] describe the combination of an interval type-2 FLC (two inputs and two outputs), a PI controller, and a dynamic selector switch to build a hybrid control system that decreases frequency ripples (maximum overshoot (MO) 0.1), contrary to type-1 fuzzy + PI (MO-0.51) and PI control (MO-0.56). The authors of [75; 140] use optimization algorithms (TLBO--teaching learning-based optimization; and ABC--artificial bee colony) to adapt the parameters of the controllers. The researchers prove in [75] that their TLBO better tunes the proposed fuzzy-PID through a filter controller than a GA (ITAE = 2.74) and other methods from the literature (fuzzy PD-PI--IATE = 0.17), as the proposed method gave the lowest error (ITAE = 0.0976). The ABC algorithm tunes the two FLCs used in [140] better than PSO (particle swam optimization), as the ISE is 3.75 for ABC-FLCs and 4.25 for PSO-FLCs, and the PI is 30.13. Smart fuzzy control that solves uncertainty factors by dealing with the FLC's outputs and values differently is proposed in [130]. Experimental validation using test setups is presented in [75; 122], whereas [102] shows real-time efficacy justification. Indeed, a large-scale real-time laboratory simulation is used in [102] to compare the methods described in the paper. Thus, the BAAL (balancing authority ACE limit) for the proposed universe variable FLC was 94.84%, better than the adaptive FLC (BAAL = 92.36%) and the improved PI (BAAL = 89.06%) [102]. Maintaining voltage levels between the standard limits is a desiderate of electricity utility companies, as this aspect is required for power systems' safe operation and the normal functioning of electricity consumers' installations and apparatus. Thus, voltage control is necessary in cases of voltage sags or swells, voltage fluctuations, and variations. These scenarios appear in several situations, for example when connecting distributed generation units, as presented in [94; 142; 143]. A STATCOM regulated the voltage in a wind power system being controlled by a type-1 (two inputs-two outputs) fuzzy controller and gave superior results than a PI controller in a simulation study [143]. The authors of [142] demonstrated using simulations that a multi-mode fuzzy (three type-1 FLCs with three inputs and one output) controller that controlled an inverter in a power system with high-ratio PVs was better at suppressing voltage variations than a simple fuzzy controller and a simple multi-mode controller. The results showed that the multi-mode + fuzzy controller had a voltage offset equal to 12.23, and the simple fuzzy approach had a voltage offset of 34.46. Fuzzy-based reactive and active power control is proposed in [94] to regulate voltage considering an on-load tap changer and distributed generation units. Simulation and hardware in-loop tests demonstrated the superiority of the proposed method in comparison with a simple version of the control system. The authors of [119] present two FCSs (adaptive tap control and adaptive reference control) that command power transformers dedicated to voltage control, considering simulations with real data and real-time experimental tests. The adaptive reference control was the superior method.