Bounded Region Optimization of PID Gains for Grid Forming Inverters with Genetic Algorithms

Abstract

Tuning conventional controllers could be a difficult task when experimental methodologies are implemented. Moreover, nowadays, Microgrids (MGs) require specific operation responses that could be achieved if the conventional controllers are correctly tuned. As a result, an optimization methodology that gets the correct parameters of conventional controller can improve the performance of the (MGs). This paper proposes the tuning of the conventional controllers used in a Grid Forming Inverters (GFMI) two voltage PID control loops, two current PID control loops, and the frequency PID controller. In a conventional control architecture of a GFMI. In GFMIs that act as voltage sources within a MG system, an incorrect tuning would harm the regulation of the dispatched voltage and frequency values to the linked electrical loads. Previously, optimization methods have been used for tuning conventional controllers, however, this is usually done in a grid-connected configuration. This work delimits the possible gain values to a desired controlled system response, by then optimizing over the controller requirements using genetic algorithms. In addition, a complete study of the tuning process under different genetic algorithm parameters (population and mutation) is presented.

This research is a product of the Project 266632 “Laboratorio Binacional para la Gestión Inteligente de la Sustentabilidad Energética y la Formación Tecnológica” (“Bi-National Laboratory on Smart Sustainable Energy Management and Technology Training”), funded by the CONACYT (Consejo Nacional de Ciencia y Tecnología) SENER (Secretaría de Energía) Fund for Energy Sustainability (Agreement S0019201401).