Situation Awareness Technology for Distribution Network Based on Multi-Source Measurement Data

With the introduction of new measurement equipment, edge computing devices and other digital equipment, the traditional distribution network is gradually transforming into a new distribution system. Phasor measurement unit (D-PMU), as the representative of measurement equipment, promotes the improvement of the operation state perception ability of the new distribution system, but as we look to the future, the emerging distribution frameworks are expected to continue integrating a diverse array of measurement data streams. This research outlines an innovative approach to synthesizing data from multiple origins, with a particular focus on the temporal attributes that define the scale of the measurements and measurement error is proposed for the distribution network with multi-source measurement data coexistence. Different types of measurement data are fused separately to improve the estimation accuracy. A regularized residual bad data identification method considering transformed measurements and zero injection constraints is proposed. The transformation relationship between the covariance elements of the original measurements and the transformed measurements is established, and the bad data identification of the original measurements is realized.