Automating power plant and power electronic controller tuning for enhanced grid stability

This paper presents the development of an automated model for tuning power plants and power electronic controllers in electrical power systems, addressing challenges posed by the integration of renewable energy sources and the resulting reduction in grid inertia. The traditional manual tuning process, complicated by proprietary black-box models from Original Equipment Manufacturers (OEMs), is time-consuming and requires high expertise and, as of September 2022, it is no longer allowed by the National Grid. This work proposes a generic open-source model that emulates OEM systems, facilitating grid code compliance through automated tuning. The model integrates PowerFactory simulations with Python scripting and a Windows Forms interface, optimising control parameters such as proportional gain (Kp) and integral gain (Ki) using machine learning algorithms. The methodology includes a detailed literature review, robust research design, and validation of a model power system. Results demonstrate significant improvements in tuning efficiency and system response, offering a scalable solution for various power plants, enhancing the integration of renewable energy, and promoting grid stability.