A KRR-UKF robust state estimation method for distribution networks

State estimation is an integral component of energy management systems. Employing a state estimation methodology that is both accurate and resilient is essential for facilitating informed decision-making processes. However, the complex scenarios (unknown noise, low data redundancy, and reconfiguration) of the distribution network pose new challenges for state estimation. In the context of this study, we introduce a state estimation technique known as the kernel ridge regression and unscented Kalman filter. In normal conditions, the non-linear correlation among data and unknown noise increases the difficulty of modeling the distribution network. Thence, kernel ridge regression is developed to map the data into high-dimensional space that transforms the non-linear problem into linear formulations base on the data rather the complicate grid model, which improves model generalization performance and filters out unknown noises. In addition, with the unique prediction correction mechanism of the Kalman method, the kernel ridge regression-mapped state value can be revised by the measurement, which further enhances model accuracy and robustness. During abnormal operating conditions and taking into account the presence of faulty data within the measurement system, we initiate the use of a long short-term memory network and combined convolutional neural network (CNN) model, referred to as the ATT-CNN-GRU. This model is utilized for the prediction of pseudo-measurements. Subsequently, we use an outlier detection method known as ordering points to identify the clustering structure to effectively identify and substitute erroneous data points. Cases on the IEEE-33 bus system and 109-bus system from a city in China show that the method has superior accuracy and robustness.

Automated summary

This article presents a new method for state estimation in energy management systems. The combination of kernel ridge regression and unscented Kalman filter improves the accuracy and robustness of the model in both normal and abnormal conditions.