## 3 Artificial Intelligence (AI) Techniques in Power Systems
### Evolutionary Methods
#### 3.4.1 Genetic Algorithm  
An optimization method based on the study of natural selection and natural genetics is known as a genetic algorithm (GA) [51]. Its fundamental tenet is that a population's fittest member has the best chances and potential for survival. Genetic algorithms provide a global approach based on biological metaphors. The genetic algorithm distinguishes itself from other optimization techniques in several ways:  
* Rather than directly manipulating the variables, the genetic algorithm operates on coded representations.
* Instead of targeting a single optimal point, the genetic algorithm explores the population of potential solution points to identify optimal solutions.
* The genetic algorithm relies solely on information from the objective function.
* Unlike deterministic laws, the genetic algorithm employs probability transition laws.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Expert Systems** & **Artificial Neural Networks** & **Fuzzy Logic (FL)** \\ \hline Knowledge used & Expert knowledge in the form of rules, objects, frames, etc. & Information extracted from the training set of cases. & Expert knowledge in the form of protection criteria. \\ \hline \begin{tabular}{c} Trouble- shooting and \\ improving a relay \\ \end{tabular} & Change of rules required. & \begin{tabular}{c} Complex—the internal signals \\ are almost impossible \\ to interpret. \\ \end{tabular} & \begin{tabular}{c} Convenient—the internal \\ signals arc understandable \\ and analyzable. \\ and analyzable. \\ \end{tabular} \\ \hline \begin{tabular}{c} Self-learning \\ \end{tabular} & Possible. & Natural. & Possible. \\ \hline \begin{tabular}{c} Handling unclear cases \\ is Possible. \\ \end{tabular} & Possible. & Natural. & Natural. \\ \hline \begin{tabular}{c} Robustness \\ \end{tabular} & \begin{tabular}{c} Non-critical and easy to \\ ensure. \\ \end{tabular} & Difficult to ensure. & \begin{tabular}{c} Non-critical and easy to \\ ensure. \\ \end{tabular} \\ \hline \begin{tabular}{c} Setting a relay \\ \end{tabular} & Convenient. & \begin{tabular}{c} A large number of simulations \\ are required. \\ \end{tabular} & \
\begin{tabular}{c} Convenient. Both knowledge \\ and simulation are used. \\ \end{tabular} \\ \hline
\begin{tabular}{c} Computations \\ \end{tabular} & Extensive. & Dedicated hardware. & Moderate. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different AI approaches based on features [48; 49].  
Derived from a basic model of population genetics, the genetic algorithm comprises the following components:  
* Individuals are represented by chromosomes encoding the variables.
* An initial population of individuals is established.
* An evaluation function acts as the environment, ranking individuals based on their fitness or survival ability.
* Genetic operators dictate the formation of a new population from the previous one through a defined procedure.
* Parameters for the genetic algorithm are predefined.  
Applications in power systems encompass various areas:
* Planning tasks include wind turbine placement, reactive power optimization, network feeder routing, and capacitor positioning.
* Operational aspects include hydro-thermal plant coordination, maintenance scheduling, loss reduction, load management, and control of flexible alternating current transmission systems (FACTS).
* Analytical functions include reducing harmonic distortion, designing filters, controlling load frequency, and performing load flow analysis.  
Since the survival of the fittest is the foundation of GAs, several strategies can be suggested to improve the effectiveness of power system operations and boost power output. The most effective strategy among these can be chosen using genetic algorithms, since it is the way that best withstands all restrictions (survival of the fittest) [52].