Self-Learning Wind Turbine Optimization Using Quantum‎Inspired Algorithms for Renewable Energy Efficiency

  • Authors

    • Dr. F Rahman Assistant Professor, Department of CS & IT, Kalinga University, Raipur, India
    • Ghorpade Bipin Shivaji Research Scholar, Department of CS & IT, Kalinga University, Raipur, India
    • Ishwari Datt Suyal Assistant Professor, New Delhi Institute of Management, New Delhi, India
    https://doi.org/10.14419/6jb9ay30

    Received date: May 2, 2025

    Accepted date: May 29, 2025

    Published date: October 31, 2025

  • Quantum-Inspired Optimization; Adaptive Machine Learning; Wind Turbine Efficiency; Fault Detection and Recovery; IoT-Enabled Data ‎Acquisition.

  • Abstract

    Detailed considerations are required to optimize wind turbine efficiency and increase the demand for renewable energy solutions. In this ‎research, the Quantum-Inspired Self-Learning Optimization (QISLO) framework, which combines quantum-inspired algorithms with ‎adaptive machine learning, is proposed to enhance the performance of wind turbines. To dynamically optimize rotor blade pitch ‎angles, yaw control, and generator torque, thereby achieving enhanced energy capture under varying wind conditions, the proposed system ‎utilizes a Quantum-Inspired Genetic Algorithm (QIGA). Additionally, an Adaptive Reinforcement Learning (ARL) model maintains optimal ‎control settings by modeling both historical and real-time performance of the turbine. The Quantum-Inspired Differential Evolution (QIDE) ‎is employed to detect faults and recover them, minimizing downtime and mechanical stress to enhance the system's resilience. A Quantum-Inspired Fuzzy Logic Controller (QIFLC) is used to maintain turbine operations within a controlled range when operating in turbulent ‎conditions. Combined with the integration of an IoT-enabled data acquisition system, this gives real-time environmental data. The ‎experimental results of QISLO also demonstrate a 20-30% improvement in energy efficiency and a 40% reduction in mechanical wear ‎compared to conventional optimization techniques. It is an innovative solution to the limitations of traditional turbine control systems, ‎offering increased reliability of energy production, enhanced wind farm sustainability, and improved overall operational efficiency. Future ‎work will also scale the system to large-scale offshore wind farms and grid integration‎.

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  • How to Cite

    Rahman, D. F. ., Shivaji , G. B. ., & Suyal , I. D. . (2025). Self-Learning Wind Turbine Optimization Using Quantum‎Inspired Algorithms for Renewable Energy Efficiency. International Journal of Basic and Applied Sciences, 14(SI-1), 352-358. https://doi.org/10.14419/6jb9ay30