Wind turbines are pivotal in the transition towards renewable energy. The operational conditions of these machines are continuously monitored through sensors that measure key indicators of efficiency and performance, including yaw angles, rotational speed, and vibrations. However, sensors are subjected to wear, degradation and consequent reduction in data reliability over time, which provides scope for developing a consistent and effective method to detect misinterpretation of turbine operating conditions caused by faulty measurements. This research presents a novel method that integrates Computational Fluid Dynamics (CFD) simulations into a Digital Twin (DT) model to detect and correct yaw misalignment caused by faulty wind direction readings. Yaw error is estimated by interpolation across CFD-based performance data using live sensor measurements. The novel DT-based method was validated through experimental testing on a small-scale horizontal-axis wind turbine. The results provide scope for a significant improvement in the resilience of wind turbines under conditions of sensor malfunctions, without the need for human intervention or supervision. The proposed method is intended to be adaptable, enabling analysis of diverse failure modes under varying operational conditions. This work also advances condition monitoring and sustainable asset management, offering potential for a larger adoption across different turbomachinery applications.