## 3 Artificial Intelligence (AI) Techniques in Power Systems
### Artificial Neural Networks  
An artificial neural network (ANN) [38] is a system inspired by biology that converts one set of inputs into another set of outputs using a network of neurons. Each neuron in the network produces one output as a function of the inputs that it receives. A fundamental neuron can be considered a processor that performs a straightforward nonlinear operation on the information supplied to generate a single output. In order to construct computers that are capable of handling real-world problems that involve identifying patterns and pattern recognition, it is necessary to understand the behavior and interconnections of neurons. This can be achieved by examining the neural circuitry. [PERSON] et al. [39]  
Figure 1: AI-based optimization techniques used to solve power quality issues [37]. have reviewed the experimental evidence for metastable dynamics in neural systems and explored the theoretical approaches for studying metastable activity in neural circuits. Artificial neural networks (ANNs) are classified based on their structure and the way they process information [40]. The architecture of an ANN refers to the number of layers it has, while the topology refers to the pattern of connections between the layers. ANN can have either a feedforward or recurrent structure. In a feedforward ANN, the information flows in only one direction, as shown in Figure 2. In a recurrent ANN, the information is passed back and forth between layers. The typical structure of an artificial neural network is illustrated in Figure 3 below. The issues with power generation, transmission, and distribution can be input to ANNs to find an appropriate solution. ANNs work on biological instincts and perform biological evaluations of real-world situations. It is possible to establish the precise values of the parameters given the limitations of a realistic transmission and distribution system. For instance, ANNs may numerically determine the values of inductance, capacitance, and resistance in a transmission line while accounting for variables such as environmental influences, imbalanced circumstances, and other potential issues [42]. Additionally, a transmission line's resistance, capacitance, and inductance values can be supplied as inputs to produce a composite normalized value for the parameters. The skin effect and proximity impact can be somewhat diminished in this way. Artificial neural networks (ANNs) primarily address the skin effect and proximity impact through their pattern recognition capabilities derived from training data, including various scenarios. By processing historical data where these effects were significant, ANNs  
Figure 3: Typical structure of an ANN [41]. Figure 2: The architecture of a feedforward ANN [41]. can learn to predict and mitigate their impacts under similar circumstances. The neural network accomplishes this by adjusting real-time operational parameters such as frequency, phase angle, and current amplitude to minimize these undesirable effects. Moreover, the application of ANNs extends to optimizing the physical configuration of cables and scheduling load operations, which can inherently reduce skin effects and proximity impact. For instance, ANNs can suggest alterations in the layout or the cross-sectional area of cables to reduce these effects without compromising the system's efficiency or safety. The enhanced capability of ANNs to predict and manage such complexities arises from their layered structure, which can model intricate relationships within data much more effectively than traditional computational methods. This allows for a nuanced understanding and handling of the skin effect and proximity impact, ensuring more stable and efficient power system operations. In practical applications, a study detailed in [43] explored various neural network techniques for evaluating steady-state stability in power networks. This analysis included five algorithms: cascade-forward NN (CFNN), Elman NN (ENN), layer recurrent NN (LRNN), linear layer NN (LLNN), and feedforward NN (FFNN).