## 6 Control Methods of Intelligent Techniques
### Fuzzy Control  
In 1963, [PERSON] developed the idea of fuzzy logic. Fuzzy set theory is where fuzzy logic comes from. There is no limit to the number of members in a fuzzy set. There is a number between 0 and 1 that represents the degree of each membership for each element.  
A straightforward controller based on a state-feedback control system was presented by [PERSON] et al. [112]. The excitation control signal includes both the output signals from the fuzzy controller and the traditional PI controller.  
A stabilizer in heavy and light circumstances can function more effectively than a traditional power system stabilizer (PSS) [113]. To account for the situations above, two linear power system stabilizers (PSSs) based on the traditional frequency domain technique were constructed. Fuzzy reasoning was developed to select a stable signal that best matched the operational state.  
A fuzzy power system stabilizer that is indirectly adaptive was developed by [114]. Two unsolved nonlinear differential equations served as the power system representation. The stabilizer was created by two fuzzy logic systems based on these equations. The fuzzy logic systems were modified using the Lyapunov synthesis method.  
A direct adaptive fuzzy logic stabilizer presented in [115] has a smaller rule base than a typical one. The rule base for single-machine infinite bus (SMIB) and multi-machine power systems was tweaked online to adjust the stabilizer to various operating scenarios. Here, the variable-structure approach was used to estimate the controller settings.  
Intelligent fuzzy controller implementation in load frequency controllers (LFCs) is increasing yearly. In [116], two intelligent load frequency controllers (LFCs) were created to manage system frequency and power output by regulating the generator's speed using fuel rack position control. Fuzzy logic (FL) alone creates the first controller, whereas neural networks, genetic algorithms, and FL are combined to create the second. The suggested controllers were discovered to have sufficient overall dynamic performance and circumvent potential limitations related to other competing approaches. On the other hand, [PERSON] et al. [21] proposed a fuzzy controller based on a water cycle algorithm (WCA) for LFCs in multi-area systems. With the combination of LFC and AVR, [PERSON] et al. [22] also proposed a fuzzy PID controller using the hybridized artificial electric field algorithm (HAEFA) approach.  
A fuzzy power system stabilizer with novel input signals was proposed by [92]. These signals include the speed deviation and the tie line linking two locations. The method outperformed conventional PSS and fuzzy PSS with typical speed deviation and acceleration input signals regarding dynamic performance. The approach's costs and scanning times were cut because the same signal could be sent to all fuzzy logic PSSs.  
A new global tuning method was suggested for fuzzy power system stabilizers to reduce oscillations in a multi-machine power system [117]. The iterative adaptive, efficient partition algorithm is the foundation for this method. According to numerical findings, this method can locate the ideal global solution more quickly than a traditional GA.  
The performance of a large-scale power system can be improved by wide-area measuring systems (WAMS). Signal transmission and reception delays have the potential to impede system performance [118]. The delay-independent robust control problem was presented to address this shortcoming based on WAMS employing the H fuzzy control approach. Similar T-S fuzzy models depict this case's vast, interconnected, nonlinear power networks. Using a feedback decentralized control technique, the model stabilized.