Artificial immune systems (GA-AIS) enabled power loss mitigation in distributed generation: X3PAIS optimization...

Area of Science:

• Electrical Engineering
• Artificial Intelligence
• Optimization Algorithms

Background:

• Industrialization increases energy demand, straining traditional power generation.
• Distributed Generation (DG) is essential but requires careful integration to prevent power system disruptions.
• Artificial Immune System (AIS) algorithms offer potential for optimization but need further development.

Purpose of the Study:

• To develop an improved Artificial Immune System (AIS) algorithm for optimizing power distribution systems.
• To address challenges in integrating multiple Distributed Generation (DG) units.
• To minimize power losses and enhance the stability of power distribution networks.

Main Methods:

• A novel hybridization strategy, X3PAIS, was developed by combining clonal selection with a three-parent crossover.
• X3PAIS was rigorously tested on diverse applications, including mechanical systems and mathematical problems, to validate its robustness.
• The X3PAIS algorithm was applied to optimize the placement and sizing of multiple DG units within a power distribution system.

Main Results:

• X3PAIS demonstrated robustness and versatility in pre-testing across various domains.
• Application in a power distribution system with four DGs resulted in a reduction of power losses exceeding 89%.
• The study confirmed X3PAIS's effectiveness in optimizing DG placement and sizing to match load profiles.

Conclusions:

• The X3PAIS algorithm is a promising approach for optimizing power distribution systems with multiple DG units.
• Further improvements, such as incorporating a three-parent multiple-point crossover, could further enhance power loss optimization.
• Effective DG integration using AI-driven methods is crucial for meeting growing energy demands and ensuring grid reliability.