Design of Distribution Transformers – e-lesson #21 – Optimum design with differential evolutions and genetic algorithms

This is the 21th lesson in the Design of distribution transformers course. The course is authored and presented live by Mario Salano, and the lesson is hosted on the Master's level. Here you can save your seat.

This lesson delves into advanced optimization techniques for transformer design, focusing on the application of differential evolution and genetic algorithms to achieve the ideal transformer design. Mario Salana, the lesson's author, emphasizes the complexity of transformer optimization due to various objectives like cost, efficiency, and size reduction. He introduces three primary optimization methodologies: traditional frameworks, Monte Carlo simulations, and genetic algorithms, with a specific focus on genetic algorithms for their adaptability to complex, multi-objective scenarios. The session explains that transformers require design consideration across multiple parameters, including core material, conductor type, and structural configuration. Key aspects of transformer optimization are minimizing losses, reducing material volume, and managing costs, particularly given the fluctuating prices of materials like copper and iron. The lesson details the concept of the Power Transformer Optimal Design (PTOD), which seeks to enhance power density and reduce the transformer’s physical footprint by using optimization techniques. Genetic algorithms, inspired by biological evolution, are presented as powerful tools in this design context. They leverage mutation, crossover, and selection processes to identify optimal design solutions from numerous variable combinations. The genetic algorithm process is highlighted as particularly advantageous for large-scale transformers, where optimization can lead to significant cost savings and enhanced efficiency. In conclusion, the lesson underscores the role of artificial intelligence and advanced algorithms, such as neural networks and swarm intelligence, as transformative tools for the future of transformer design. These methodologies can adapt to complex, multi-variable engineering challenges, providing refined solutions that meet evolving industry standards and regulations. This foundational lesson sets up a comprehensive framework for future advancements in transformer optimization.