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<p>The performance of state estimation algorithms in dynamical systems, such as wind power systems, is significantly affected by measurement noise. Such noises may arise from sensor inaccuracies, environmental disturbances and communication errors. To address these challenges, this article proposes an Adaptive Measurement Noise Covariance-based Unscented Kalman Filter (AMnC-UKF) for robust and accurate state estimation in grid-connected wind power systems in the presence of measurement noise. The adaptive approach dynamically adjusts the measurement noise covariance matrix to accommodate varying noise levels, ensuring improved estimation accuracy. The adaption law has been designed by considering an intermediate step of UKF, named as <i>observation index</i> which goes out of bound when estimation error comes in picture. The proposed scheme has been rigorously tested under step and turbulent wind speed conditions, with different measurement noise cases characterized by varying signal-to-noise ratio (SNR) and outlier. Also, its investigation in modified Western System Coordinated Council (WSCC) 3-machine 9-bus power system confirms the effectiveness of the proposed estimation. The results demonstrate that the proposed method accurately estimates the system states, effectively handling noise across all scenarios. Furthermore, the estimation time is found to be satisfactory, making the approach suitable for real-time applications.</p>

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Adaptive Measurement Noise Covariance Based Unscented Kalman Filter for Robust Dynamic State Estimation of Wind Power Systems

  • 期刊论文

  • 发表日期：

    2026年2月23日

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  • Vikas Bhalla,
  • Ganesh P. Prajapat

摘要

The performance of state estimation algorithms in dynamical systems, such as wind power systems, is significantly affected by measurement noise. Such noises may arise from sensor inaccuracies, environmental disturbances and communication errors. To address these challenges, this article proposes an Adaptive Measurement Noise Covariance-based Unscented Kalman Filter (AMnC-UKF) for robust and accurate state estimation in grid-connected wind power systems in the presence of measurement noise. The adaptive approach dynamically adjusts the measurement noise covariance matrix to accommodate varying noise levels, ensuring improved estimation accuracy. The adaption law has been designed by considering an intermediate step of UKF, named as observation index which goes out of bound when estimation error comes in picture. The proposed scheme has been rigorously tested under step and turbulent wind speed conditions, with different measurement noise cases characterized by varying signal-to-noise ratio (SNR) and outlier. Also, its investigation in modified Western System Coordinated Council (WSCC) 3-machine 9-bus power system confirms the effectiveness of the proposed estimation. The results demonstrate that the proposed method accurately estimates the system states, effectively handling noise across all scenarios. Furthermore, the estimation time is found to be satisfactory, making the approach suitable for real-time applications.