Interval State Estimation Adopted Multi-Source Measurement Data in Distribution System

In the active distribution network, there are multi-source measurement data with varying time scales and measurement accuracies. The full fusion of multi-source measurement data to accurately estimate the state of a distribution network is an essential prerequisite for its efficient operation. In this paper, a novel interval state estimation method for multi-source measurement data fusion is proposed. Firstly, the measurement bias caused by inconsistent timestamps of the data was quantified and a multi-source interval dataset was constructed. On this basis, a method for multi-source data fusion was proposed, which incorporates the temporal scale characteristics of the measurement data. Secondly, an adaptive adjustment technique based on the innovation variance was employed to dynamically adjust the weights assigned to the measurements, aiming to achieve accurate state estimation. Finally, the feasibility and effectiveness of the proposed method were demonstrated through case studies.