AI-Controlled Wind Turbine Systems: Integrating IoT and Machine Learning for Smart Grids

^* Associate Professor, Scool of Business and Management, Christ university, Yeshwanthpur Campus, Bengaluru .
^† Department of Management Uttaranchal Institute of Management, Uttaranchal University, Dehradun-248007, India
^‡ Assistant Professor, Department of ECE, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai - 127
^§ Department of Computer Science & Engineering, IES College of Technology, IES University, Madhya Pradesh 462044 India, Bhopal .
^** The Islamic university, Najaf, Iraq
⁶ Engineering Manager, Altimetrik India Pvt Ltd, India anishdhablia@gmail.com, Pune, Maharashtra

^* Corresponding Author :madeswaran.a@christ university.in
^† Deepabisht.bisht28@gmail.com
^‡ S.Yuvaraj_ece@psvpec.in
^§ research@iesbpl.ac.in
^** kassem.alattabi@iunajaf.edu.iq

Abstract

Advances in renewable energy technologies are pivotal in addressing the challenges posed by the depletion of traditional energy sources and their associated environmental impacts. Among these, wind energy stands out as a promising avenue, with wind turbine farms proliferating globally. However, the unpredictable nature of wind and intricate interplay between turbines necessitate innovative solutions for efficient operation and maintenance. This paper reviews advancements in intelligent control systems, notably those proposed by Smart Wind technologies. These systems leverage a network of sensors and IoT devices to gather real-time data, such as wind speed, temperature, and humidity, to optimize turbine performance. A significant focus is on turbines employing doubly-fed induction generators, which offer benefits like adjustable speed and consistent frequency operation. Their integration into smart grids introduces challenges concerning power system dynamics’ security and reliability. This review delves into the dynamics, characteristics, and potential instabilities of such integrations, emphasizing the uncertainties in wind and nonlinear load predictions. A noteworthy finding is the rising prominence of artificial intelligence, particularly machine and deep learning, in predictive diagnostics. These methodologies offer costeffective, accurate, and efficient solutions, holding potential for enhancing power system stability and accuracy in the smart grid context.