## 6 Control Methods of Intelligent Techniques
### Artificial Neural Networks  
Due to the nonlinear mapping characteristics of neural networks, ANNs have been successfully employed for many years to identify and control complex systems. Neural networks may be employed as controllers rather than traditional PSSs when suitably trained. The neural network (NN) must be trained for various operating situations to fine-tune the traditional PSS parameters and obtain reasonable performance. Interference is created during learning using a traditional backpropagation network under many circumstances. A modular NN was proposed by [119] in place of a backpropagation network to address this flaw. Three local expert networks and one gate network, each having three layers, make up this model. The ANN was trained directly from a traditional PSS's input and output. According to the simulation results, the modular PSS is more efficient at dampening system oscillations and delivering high-quality outcomes. A neural adaptive power system stabilizer was created by [120] using a feedforward neural network with a single hidden layer. This made use of several methods. The two subnetworks comprise each stabilizer, an adaptive neuroidentifier (ANI) and an adaptive neurocontroller (ANC). A backpropagation technique was used to train both subnetworks. The second strategy was modified online and is based on an indirect adaptive control scheme instead of the previous approach's direct adaptive control system. In order to fine-tune PSS parameters online based on real-time assessment of machine loading conditions, [120] designed a radial basis function network (RBFN). The suggested stabilizer was trained for various operating scenarios and changes in system parameters. The orthogonal least squares learning approach was used to obtain the network parameters and a collection of significant radial basis functions. The neural network employed in the approach described by [119] needs significant training. An ANN-based self-tuning PSS was suggested by [97] to circumvent this issue. This method included the ANN allowing real-time adjustment of the traditional PSS parameters. A novel method chose the number of neurons in the hidden layer. [PERSON] et al. [111] suggested a recurrent neural network (RNN) stabilization controller to enhance the transient stability of power systems under various operating conditions and parametric uncertainties. The suggested additional RNN controlled the governor and the automatic voltage reactor (AVR). Online adjustments were made to the controller's weight. For excitation control, the signal output of the first RNN was combined with the PSS signal output. The governor system's stabilizing signal came from the second RNN's output. The suggested method worked with a single-machine infinite bus. Designing intelligent controllers based on ANNs requires a significant investment in time and resources due to the complexity of large-scale power systems. These encourage [90] employment generalized neurons (GNs) to create an adaptable PSS based on generalized neurons. Offline training covered a variety of operating situations and disturbance types for the stabilizer. After that, I taught it the power system online. The simulation results of the single-machine infinite bus (SMIB) system show that the proposed stabilizer works well under solid shocks. A PSS was created using the gain scheduling technique to improve system performance. This stabilizer works better than the traditional PSS. A neural power system stabilizer that is taught using a set of gain scheduling PSS parameters was proposed by [93]. These characteristics were acquired by utilizing the pole-placement approach for various operating situations. They concluded that stabilizers would be more useful when generators absorb reactive power. [PERSON] et al. [121] used the single-neuron model to improve resilient PSS parameter optimization by probabilistic eigenvalue analysis. By constructing a helping PSS gain from the single-neuron model, the PSS performance was self-adjusted to follow the system operating state.