Optimal Parameter Estimation of 3-Phase Transmission Line Using a Grey Wolf Optimization Algorithm

A optimization, selects the best option from a list of possible options to meet a predetermined set of criteria. In pursuit of the best and most optimal solution for a particular issue, this theory is relevant to virtually all areas of natural as well as social sciences, and it has applications across the board. It’s possible to utilize the optimization domain to either maximize or decrease the value of an actual function by selecting input values carefully and computing their values. A significant field of applied mathematics is devoted to the application of minimization and maximization with different restrictions on a wide variety of objective functions. Many different optimization methods may be used 314constructed on the cost function and restrictions that are existing. As a result, optimization methods are generally classified into the following categories: discrete and continuous optimization, Unconstrained and Constrained optimization, and none or multi-objective functions. Swarm intelligence is a kind of artificial intelligence (AI), which mimics the intelligent behavior of swarms, flocks, herds, and other groups of animals seen in nature. As a result, these algorithms make use of the collective behavior of animals’ groupings. The whale optimization algorithm (WOA), dolphin echolocation (DE), CS algorithm, bat algorithm (BA), Harmony Search (TS), artificial bee colony (ABC) algorithm, grey wolf optimizer (GWO), fruit fly optimization algorithm (FOA), the, hill-climbing, iterative local search, and other SI algorithms are available. (This chapter will cover the idea of optimization, as well as mathematical derivations and explanations of their applications in real-world situations). Following that, it will examine the specific application of optimization methods to one of the electrical engineering issues, namely, the parameter estimation of overhead transmission lines, in more detail. It is the transmission line, which is composed of inductance, capacitance, and resistance, that is the most critical component of the power system. During the transmission line design phase, these characteristics are very important. The overhead transmission line parameter is computed using a new optimization method known as gray wolf optimization, which is described in more detail in the literature. GWO is a meta-heuristic algorithm that was recently created based on natural inspiration. For testing, 3-phase transmission line test systems are used. The command structure and hunting technique of gray wolves served as inspiration for the suggested algorithm. The technique is used for several optimization functions that differ in size and a lot of search agents participating. The GWO algorithms provide optimal results that outperform those produced by other algorithms previously in use. For the vast majority of these statistically verified functions, the suggested method provided the most optimum solutions that could be found. Following the results, it has been discovered that the suggested method is more computationally efficient than the conventional algorithm and exceeds it in terms of accuracy, resilience, and convergence time. Moreover, the best optimal obtained using GWO for 3-phase capacitance and inductance are 0.22436, 0.022935, 0.65915, and 0.34938.