Abstract: With the large-scale integration of distributed photovoltaic power into distribution networks, issues such as frequent changes in power flow direction and increasingly complex topological structures have emerged. Meanwhile, existing intelligent algorithms suffer from drawbacks such as being prone to getting stuck in local optima and having poor iterative performance. To address these challenges, an improved Zebra Optimization Algorithm (IZOA) is proposed, in which Latin hypercube sampling is employed for the initialization of the zebra population, a dynamic perturbation strategy is introduced during the foraging stage, and a golden sine search mechanism is incorporated in the defense stage, thereby enhancing the algorithm's ability to escape local optima and improve optimization accuracy. Subsequently, a fault current encoding method, switch function, and objective function suitable for multi-source bidirectional power flow are constructed, transforming the fault location problem into an optimization problem. Finally, an improved IEEE33-node distribution network model is established, and the effectiveness of the proposed algorithm is verified through simulation experiments and comparative analysis. The results demonstrate that the IZOA algorithm can rapidly and accurately locate single-point and multi-point fault sections. Compared with other algorithms, it achieves a 12.57% improvement in average accuracy and a 51.5% increase in iteration speed. The algorithm exhibits fault tolerance for minor distortions in fault information, showing significant performance advantages.

Key words: distributed photovoltaics, active distribution network, fault location, zebra optimization algorithm, dynamic disturbance, golden sine search