ADAPTIVE HYBRID DIFFERENTIAL EVOLUTION PARTICLE SWARM OPTIMIZATION ALGORITHM FOR OPTIMIZATION DISTRIBUTED GENERATION IN DISTRIBUTION NETWORKS
DOI:
https://doi.org/10.11113/jurnalteknologi.v87.19769Keywords:
Differential Evolution, Particle Swarm Optimization, Data Optimization, DG Optimization, Power LossAbstract
The incorporation of Distributed Generation (DG) into the Radial Distribution System (RDS) aids in resolving power system issues such as power loss. However, finding the ideal location and size for DG is challenging yet crucial for maximizing these advantages. Inappropriate location and sizing of DG can negatively impact its benefits, highlighting the need for effective optimization methods. To address these challenges, metaheuristic methods like Particle Swarm Optimization (PSO), Differential Evolution (DE), and Firefly algorithms are particularly useful. This research aims to determine the best location and size of DG to minimize active power losses in the RDS. Despite the success of DE and PSO in previous works, a gap remains in achieving optimal convergence performance and computational efficiency. Building on the success of DE and PSO, this study presents a hybrid optimization approach, Adaptive Hybrid Differential Evolution and Particle Swarm Optimization (AHDEPSO), to reduce power loss in RDS. This approach combines the strengths of DE and PSO, enhancing exploration and exploitation capabilities while improving convergence performance. The effectiveness of this approach is demonstrated through testing on the IEEE 33 and IEEE 69 bus systems using MATLAB software, showing that AHDEPSO can achieve optimal computational time and best fitness function, being 38.3% faster than other algorithms. This hybrid approach offers a significant improvement over traditional methods, filling the research gap and providing a more efficient solution.
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