Transforming Distribution Network Fault Location into a Nonlinear Global Optimization Problem via Mathematical Modeling and Fault Overcurrent Analysis

In modern solutions for combinatorial optimization problems, Ant Colony Optimization (ACO) represents a relatively new general-purpose heuristic approach. Its most distinctive features include positive feedback mechanisms, distributed computation capabilities, and constructive greedy heuristic search strategies. Through the establishment of appropriate mathematical models, ACO transforms fault location in distribution networks based on fault overcurrent into a nonlinear global optimization problem. In algorithmic implementation, simulated ants deposit pheromone trails during the search process, where higher pheromone concentrations attract more ants through probability selection functions, thereby facilitating the discovery of globally optimal solutions. The core algorithm typically involves two main phases: pheromone update rules (evaporation and reinforcement) and state transition probability calculations. Furthermore, ACO enables control over the search process through strategic parameter adjustments such as pheromone decay coefficients and exploration-exploitation balance parameters, allowing better adaptation to diverse problem scenarios. Parameter tuning methods often involve empirical studies or automated optimization techniques to enhance convergence performance. In summary, Ant Colony Optimization presents a highly promising methodology for addressing combinatorial optimization challenges, particularly effective for problems requiring distributed computation and adaptive search strategies.