State Estimation using DCGNN-CLO Approach for IoT Enabled Wind Energy Conversion System

Wind farms, which comprise of multiple wind turbines, play an important role in renewable energy generation. However, network connectivity issues and data transmission delays in the Internet of Things (IoT) infrastructure can affect monitoring and decision-making, re-ducing the accuracy and responsiveness of state estimation. This manuscript proposes a hy-brid methodology for state estimation in an IoT-enabled wind energy conversion system (WECS), with the primary objective of enhancing system efficiency and reliability. The pro-posed method combines Density Clustering and Graph Neural Network (DCGNN) and Clouded Leopard Optimization (CLO) and is named as DCGNN-CLO approach. The DCGNN model effectively predicts the operational state of the wind turbine by applying ad-vanced graph-based learning. To improve the prediction accuracy and model robustness, the weight parameters of DCGNN are fine-tuned using the CLO method. The performance of the proposed method is evaluated using MATLAB and compared with several existing tech-niques such as Space Vector Pulse Width Modulation (SVPWM) Algorithm, Fractional Order Darwinian Particle Swarm Optimization (FODPSO) and Convolutional Neural Network (CNN). From the findings, the proposed method achieved a low THD of 3.26% with a short-er response time of 0.23 s, minimizing harmonic distortions while ensuring rapid system adaptation. Additionally, with a low RMSE of 0.112 and a correlation coefficient of 0.997, the proposed method outperforms existing approaches by achieving higher prediction accu-racy and stronger correlation with actual turbine states.