Smart Tuning of Control System Parameters for a Grid Connected Converter: A Robust Artificial Neural Network Approach

Abstract

The reliable and effective operation of modern power grids is highly dependent on accurately adjusting the control system parameters of power converters. Traditional approaches to parameter tuning often depend on analytical models and offline optimization, which may not fully describe the intricate dynamics and nonlinearities seen in real-world modern power grids. This paper presents an innovative method for intelligently adjusting the control system settings of converters in a modern power grid. The proposed approach utilizes machine learning methods, particularly robust artificial neural networks, to tune the converter control parameters and improve the overall modern power grid performance. This intelligent tuning system can obtain ideal parameters for stable, economical, and resilient modern power grid operation under different operating circumstances and disturbances by training the neural network models robustly using detailed simulation data and real-time measurements. This study provides a comprehensive description of the intricate structure of the intelligent tuning framework, including the neural network models and the robust methods. The proposed approach’s usefulness in enhancing the modern power grid frequency control, active power regulation, and transient response is validated via comprehensive case studies in comparison to existing parameter tuning approaches. The performed simulation and laboratory real-time experiments indicate that the smart tuning system is adaptable and resilient, making it a potential alternative for improving the stability and performance of modern power grids.