The Application of Ant Colony Algorithms to Improving the Operation of Traction Rectifier Transformers

Abstract

In this paper, we discuss a technical issue occurring in electric traction. Tram traction may use DC voltage; this is obtained by rectifying AC voltage supplied by the power grid. In the simplest design-one which is commonly used-only diode uncontrolled rectifiers are used. The rectified voltage is not smooth; it always contains a pulsating (AC) component. The amount of pulsation varies. It depends, among other factors, on the design of the transformer-rectifier set. In the 12-pulse system, we use a three-winding transformer, consisting of one primary winding and two secondary windings: one is delta-connected and the other is star-connected. The unbalance of secondary windings is an extra factor increasing the pulsation of DC voltage. To equalize secondary side voltages, a tap changer may be used. The setting of the tap changer is the question resolved in this paper; it is optimized by application of the ACO (ant colony optimization algorithm). We have analyzed different supply voltage variants, and in particular, distorted voltage containing 5th and 7th harmonics. The results of ant colony optimization application are described in this paper. The General Issue: Applying the Swarm Intelligence Algorithm to Optimization ProblemsEngineering nowadays often requires the numerical optimization of many and diverse problems. New optimization algorithms are invented all the time; among them, we may distinguish different types of metaheuristic algorithms. The heuristic approach may be termed practical; it may not be optimal in the mathematical sense of the word, but it is quite often sufficient enough from engineering viewpoint. Usually, its main advantage lies in its speed-a satisfactory solution may be found more quickly than in cases when more elaborate numerical algorithms are used.Among the metaheuristic algorithms, we find lots of procedures based on swarm intelligence. They are inspired by the behaviour of a large population of individuals belonging to one species. The performance of such a group seems to be co-ordinated, even though we cannot identify a leader, i.e., some sort of central control. Every individual in a swarm sends information and receives information from other members of the group, in a way that is peculiar to a given species. It must be stressed that information on the optimum way of acting (e.g., the shortest way to food source) is also exchanged. The entire swarm (system) progresses dynamically. When some individuals find the most effective course of action, the others (receivers of this information) concentrate in the regions of search areas that have proven to be most successful from the viewpoint of the desired goal (e.g., the source of food). Thus, the solution to the problem is attained [1]. Apart from searching for food, other behaviour patterns such as communication, task allotment, decision making, the localization of HQ, choice of partners, mating, and propagation have been the source of inspiration for population algorithms.Ant colony optimization [2][3][4][5] is one such algorithm. T...

Hybrid Genetic and Spotted Hyena Optimizer for Flow Shop Scheduling Problem

“…These algorithms are inspired by the collective behavior of social organisms such as birds, fish, and insects, and their ability to solve complex problems through decentralized and self-organizing processes. Some widely used swarm intelligence algorithms for solving the FSSP include Particle Swarm Optimization (PSO) [5], Ant Colony Optimization (ACO) [6], Whale Swarm Algorithm (WOA) [7] and Rat Swarm Optimization [8].…”