Dynamic State Estimation for Sustainable Distribution Systems Considering Data Correlation and Noise Adaptiveness

The integration of distributed renewable energy sources into distribution networks is a key approach to achieving sustainable and low-carbon power systems. However, high renewable penetration significantly increases the volatility and uncertainty of distribution systems, posing challenges to renewable energy accommodation and reliable operation. To address these challenges, active control of distribution networks is required, which in turn relies on accurate system states. In practice, the limited number and accuracy of measurement devices in distribution networks make dynamic state estimation a critical technology for sustainable distribution systems. In this paper, a novel dynamic state estimation method for sustainable distribution systems is proposed, incorporating spatiotemporal data correlation and adaptiveness to process and measurement noise. A CNN-BiGRU-Attention model is developed to reconstruct high-accuracy real-time pseudo-measurements, compensating for insufficient sensing infrastructure. Furthermore, a noise adaptive dynamic state estimation method is proposed based on an improved unscented Kalman filter. An amplitude modulation factor (AMF) is applied to track time-varying process noise, while an evaluation method based on robust Mahalanobis distance (RMD) is embedded to deal with non-Gaussian measurement noise. Finally, simulation studies on the IEEE 33-bus three-phase unbalanced distribution network demonstrate the effectiveness and robustness of the proposed method.