Algorithm

Control reconfiguration

Ant colony optimization algorithms

Embedded system

Distribution (mathematics)

Computer science

Mathematics

Ant colony

Mathematical analysis

Geometry

Reduction (mathematics)

Electric Power Systems Research

This paper introduces an ant colony search algorithm (ACSA) to solve the optimal network reconfiguration problem for power loss reduction. The ACSA is a relatively new and powerful intelligence evolution method for solving optimization problems. It is a population-based approach that uses exploration of positive feedback as well as greedy search. The ACSA was inspired from natural behavior of the ant colonies on how they find the food source and bring them back to their nest by building the unique trail formation. By applying the ACSA, the near-optimal solution to the network reconfiguration problem can be effectively achieved. The ACSA applies the state transition rule, local pheromone-updating rule, and global pheromone-updating rule to facilitate the computation. The network reconfiguration problem of one three-feeder distribution system from the literature and one practical distribution network of Taiwan Power Company (TPC) are, respectively, solved using the proposed ACSA method, the genetic algorithm (GA), and the simulated annealing (SA). Numerical results show that the proposed method is better than the other two methods.

Distribution network reconfiguration for loss reduction by ant colony search algorithm