Quadrature Current Compensation in Non-Sinusoidal Circuits Using Geometric Algebra and Evolutionary Algorithms

Abstract

Non-linear loads in circuits cause the appearance of harmonic disturbances both in voltage and current. In order to minimize the effects of these disturbances and, therefore, to control the flow of electricity between the source and the load, passive or active filters are often used. Nevertheless, determining the type of filter and the characteristics of their elements is not a trivial task. In fact, the development of algorithms for calculating the parameters of filters is still an open question. This paper analyzes the use of genetic algorithms to maximize the power factor compensation in non-sinusoidal circuits using passive filters, while concepts of geometric algebra theory are used to represent the flow of power in the circuits. According to the results obtained in different case studies, it can be concluded that the genetic algorithm obtains high quality solutions that could be generalized to similar problems of any dimension.

Optimization of the Contracted Electric Power by Means of Genetic Algorithms

“…Evolutionary algorithms have shown to be efficient methods, and are probably the most commonly used since they are problem-solving procedures that include evolutionary processes as the key design elements, such that a population of individuals is continually and selectively evolved until a termination criteria is fulfilled. Genetic algorithms (GAs) [12] are possibly the most widely used evolutionary techniques for solving a large variety of problems in the field of electrical systems [13,14]. As it can be seen in Figure 3, a genetic algorithm mimics natural selection by evolving over time a population of individual solutions to the problem at hand until a termination condition is fulfilled and the best individual is returned as result of the algorithm.…”

Analysis of Power Flow Under Non-Sinusoidal Conditions in the Presence of Harmonics and Interharmonics Using Geometric Algebra

“…Some specific applications in power systems have been already found, such as passive filtering [21]. In order to validate the theory presented in this paper, three cases of study are proposed where the circuit theory based on GA is applied to linear and non-linear loads.…”

Reactive Power Optimization of Large-Scale Power Systems: A Transfer Bees Optimizer Application

“…In the past decades, artificial intelligence (AI) [8][9][10][11][12][13][14][15][16][17][18] has been widely used as an effective alternative because of its high independence from an accurate system model and strong global optimization ability. Inspired by nectar gathering of bees in wild nature, the artificial bee colony (ABC) [19] has been applied to optimal distributed generation allocation [8], global maximum power point (GMPP) tracking [20], multi-objective UC [21], and so on, and has the merits of simple structure, high robustness, strong universality, and efficient local search.…”

“…However, the ABC mainly depends on a simple collective intelligence without self-learning or knowledge transfer, which is a common weakness of AI algorithms such as genetic algorithm (GA) [9], particle swarm optimization (PSO) [10], group search optimizer (GSO) [11], ant colony system (ACS) [12], interactive teaching-learning optimizer (ITLO) [13], grouped grey wolf optimizer (GGWO) [14], memetic salp swarm algorithm (MSSA) [15], dynamic leader-based collective intelligence (DLCI) [16], and evolutionary algorithms (EA) [17]. Thus, a relatively low search efficiency may result, particularly while considering a new optimization task of a complex industrial system [22], e.g., the optimization of a large-scale power system with different complex new tasks.…”