A recent study has identified the Cubature Kalman Filter (CKF) as a superior method for real-time monitoring and ensuring voltage and frequency stability in power grids heavily integrated with solar and wind energy. The research, published in Scientific Reports, rigorously evaluated three advanced state observers—Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and CKF—demonstrating CKF's significant advantages in accuracy, convergence speed, and classification performance, crucial for the evolving landscape of renewable-dominated smart grids.

The increasing integration of intermittent renewable energy sources, particularly solar photovoltaics and wind turbines, introduces substantial variability and nonlinearity into modern power systems. These fluctuations pose significant challenges to maintaining grid stability, often leading to rapid deviations in voltage and frequency, which can compromise power quality and trigger protective actions. Traditional linear state estimation methods are often inadequate for these dynamic, nonlinear environments, necessitating the exploration of more advanced techniques capable of handling real-time measurement noise and system complexities.

The study focused on assessing the performance of these filters under varying phasor measurement unit (PMU) sampling rates in solar and wind-integrated grids (SAWIG). PMUs provide high-resolution, synchronized data, which is essential for accurate state estimation in dynamic systems. The findings revealed that the CKF consistently outperformed its counterparts. Specifically, CKF achieved the lowest root mean square error (RMSE) of 0.005 at a 10 Hz sampling rate, demonstrating superior estimation accuracy compared to UKF (0.007) and EKF (0.010). This precision is vital for real-time operational decisions and grid control.

In terms of dynamic performance, CKF exhibited remarkable speed, stabilizing within just 0.1 seconds. This rapid convergence significantly outpaced UKF, which required 0.2 seconds, and EKF, which took 0.4 seconds. Such swift response times are paramount for mitigating fast-changing grid disturbances inherent to renewable energy integration. Furthermore, the classification evaluation underscored CKF's robustness, achieving the highest accuracy of 99.5%, with precision, recall, and F1-score of 99.2%, 99.3%, and 99.4% respectively. In contrast, UKF reported values of 98.8%, 98.5%, 98.7%, and 98.6%, while EKF recorded 97.6%, 96.9%, 97.1%, and 97.3%. Confusion matrix analysis further confirmed CKF's classification accuracy at 95%.

These results underscore CKF's potential to enhance the reliability and efficiency of state estimation in renewable-integrated smart grids. Its superior robustness, speed, and precision in monitoring voltage and frequency stability provide grid operators with a more reliable tool to manage the complexities introduced by high renewable energy penetration, ultimately supporting a more resilient and sustainable energy infrastructure.