Comparative Analysis of Conventional and Intelligent Control Technique Schemes for Microgrid

Microgrids provide an efficient and sustainable solution to meet the growing energy demands of modern society offering enhanced reliability, flexibility and integration of renewable energy resources. However, they pose significant challenges in operation and control, particularly when operating in islanded mode. Conventional controllers often face challenges like reduced speed, more response time, less accuracy and reduced robustness. Implementing Artificial Intelligence (AI) techniques is an innovative approach to enhance control operations in modern microgrid environments. This paper reviews conventional control methods and AI based techniques in a dynamic microgrid environment. Conventional methods rely on accurate mathematical model and predefined strategies for designing a closed loop system which can be more complex in the presence of system uncertainties. The AI based adaptive control systems often address these shortcomings by providing faster, more accurate and robust control. To address the growing electricity demand in the future, a variety of distributed generation (DG) units will be incorporated into microgrid systems. It is essential to design converters appropriately to meet future ride-through requirements. This paper presents a comparative analysis of Proportional Integral (PI) control and Artificial Neural Network (ANN) control for a boost converter.