Multi-Objective Design Optimization for HVDC-LCC Converter Transformers: Analytical and FEA-Based Comparison - Marcos V. Czernorucki; Mauricio Barbosa Camargo De Salles; Sergio Luciano Avila; Felipe Alves Sobrinho; Wilerson W. Calil; Jose Roberto Cardoso (IEEE Access, 2023)
The emerging HVDC technology has been used for long-distance power transmission, increasing flexibility to the power systems, handling asynchronous interconnections, crossing long-distance submarine cables, unusual loading, and generation profiles, and improving energy market relations. The HVDC converter transformers are designed based on system parameters, that directly affect the core and windings geometries, thus the weight and operating losses. In a standard design process, the designer adjusts the active part dimensions, until the constructive and specified aspects, moreover, the technical standard restrictions, are satisfied. The main contribution is to formulate analytic equations, in a way that losses and weight can be obtained for several options of core and windings geometry. However, weight and losses are opposite objectives in the search for an optimal solution. Understanding this compromise between opposing goals is relevant to the equipment learning process. In this context, another significant contribution of the work is to carry out a formal optimization process through the analytical formulation developed. To minimize the weight and operating losses, and subject to IEC standards and constructive restrictions, the multi-objective Genetic Algorithm has been used to search for the Pareto Frontier. Far beyond the chosen solution, the non-dominated frontier obtained for each transformer design, allows the designer to learn about the equipment and its operation, leading to a continuous improvement of the proposed methodology. The analytical formulation is validated by an alternative numerical methodology for winding harmonic losses and short-circuit impedance verification, providing meaningful confidence for the applied method.