Energy consumption analysis of power grid distribution transformers based on an improved genetic algorithm

Area of Science:

• Electrical Engineering
• Power Systems
• Optimization Algorithms

Background:

• Rising electrical power utilization necessitates improved efficiency and dependability of power apparatus.
• Unequal load distribution in transformer networks increases energy consumption and failure risk.

Purpose of the Study:

• To optimize transformer energy utilization in electricity transmission grids.
• To dynamically redistribute loads among transformers based on operational status.

Main Methods:

• Proposing a methodology based on genetic algorithms for load optimization.
• Dynamically redistributing transformer loads using operational status monitoring.
• Experimenting with three distinct approaches to enhance energy efficiency.

Main Results:

• The genetic algorithm approach achieves optimal power levels faster.
• Overall energy consumption during transformer operation is diminished.
• Monitoring time is reduced by 27%, and energy consumption by 11.81%.

Conclusions:

• Genetic algorithms offer an effective solution for optimizing transformer energy usage.
• The proposed method enhances grid responsiveness to power demand fluctuations.
• Genetic algorithms have potential applications in power equipment management and energy optimization.