Self-Learning Wind Turbine Optimization Using Quantum-Inspired Algorithms for Renewable Energy Efficiency
Year: 2025
Type: article
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.
DOI: 10.14419/6jb9ay30
Authors: Dr. F Rahman, Ghorpade Bipin Shivaji, Ishwari Datt Suyal
Institution: Kalinga University
Language: English
Topic: Wind Turbine Control Systems
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4415764189