Optimizing Distributed Generator Placement in Distribution Networks Using Flower Pollination Algorithm with Self-Healing Integration

This study aims to optimize power flow in distribution network systems using artificial intelligence, specifically the Flower Pollination Algorithm (FPA), integrated with a self-healing system to improve the reliability and efficiency of power distribution. The research focuses on mitigating common power distribution issues such as under-voltage and over-voltage by strategically placing Distributed Generators (DG) within the network. A modified IEEE 15-bus system is used as a test case to evaluate the proposed method. The FPA algorithm is chosen for its effectiveness in solving multi-objective optimization problems, incorporating both global and local pollination strategies. The objective function considers total power losses and voltage deviations to ensure optimal DG placement. Simulation results demonstrate that the combined application of FPA and self-healing mechanisms significantly reduces power losses and voltage fluctuations, enhancing overall network stability. This research provides a robust and efficient solution for modern power distribution systems, improving their adaptability to dynamic operational conditions.