Robust Multiarea Distribution System State Estimation Based on Structure-Informed Graphic Network and Multitask Gaussian Process

• Published in: IEEE transactions on industrial informatics Vol. 20; no. 8; pp. 10599 - 10612
• Main Authors: Hu, Jiaxiang, Hu, Weihao, Cao, Di, Li, Sichen, Chen, Jianjun, Huang, Yuehui, Chen, Zhe, Blaabjerg, Frede
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Distribution system state estimation (DSSE); Estimation; Feature extraction; Gaussian process; graph attention learning; Graphical representations; interval state estimation; Learning systems; multiarea state estimation; multitask Gaussian process (MTGP); Normal distribution; Real time; Real-time systems; robust state estimation; Robustness; State estimation; State variable; Topology; Training; Uncertainty
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2024.3394553

This article proposes a robust multiarea distribution system state estimation method for interval estimation of state variables based on a physics-informed decentralized graphical representation network and Gaussian process (GP)-aided multiarea state estimators. The real-time and pseudomeasurements are first cast to a graph with tree topology and a graph attention-based representation network is employed to capture the structural information between measurements from the historical data. A centralized pretraining and distributed inference framework is developed to extract essential global information from historical data and extend it to various subregions. Then, the robust nodal features extracted by the graphical network are fed into the GP with a multitask kernel for multiarea state estimation. The adopted kernel can find relevance between tasks for different subregions that are useful for the multiarea state estimation. The embedding of structural information in the representation network enables the proposed method to achieve robustness in the presence of outliers. The adopted kernel further allows us to reduce the reliance on network communication and achieve accurate multiarea state estimation. It also offers the ability to quantify the uncertainty of state variables, yielding more valuable estimation outcomes. Experimental results demonstrate the effectiveness of the proposed method in handling abnormal data and accurately quantifying the uncertainty of state variables.