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

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.