Single iteration conditional based DSSE considering spatial and temporal correlation. (May 2019)

• Record Type: Journal Article
• Authors: Shafiei, Mehdi; Nourbakhsh, Ghavameddin; Arefi, Ali; Ledwich, Gerard; Pezeshki, Houman
• Is Part Of: International journal of electrical power & energy systems. Volume 107(2019)
• Page Start: 644
• Page End: 655
• Publication Date: 2019-05
• Subjects: Distribution system -- State estimation -- Conditional multivariate complex Gaussian distribution -- Spatial-temporal correlation -- Measurement errors; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2018.12.025
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Updating pseudo data considering the impact of spatial-temporal correlation. Studying the impact of load aggregation and PV penetration on spatial-temporal correlation. Developing a non-iterative computationally efficient DSSE algorithm. Proposing a time series state estimation framework suitable for unbalanced LV distribution networks. Abstract: The increasing complexity of distribution networks calls for advancement in distribution system state estimation (DSSE) to monitor the operating conditions more accurately. A sufficient number of measurement devices is imperative for a reliable and accurate state estimation. The limitation on the measurement devices is generally tackled with using the so-called pseudo measured data. However, the errors in pseudo data by current techniques are quite high leading to a poor DSSE. As customer loads in distribution networks show high cross-correlation in various locations and over successive time steps, it is plausible that deploying the spatial-temporal dependencies can improve the pseudo data accuracy and estimation. Although the role of spatial dependencies in DSSE has been addressed in the literature, one can hardly find an efficient DSSE framework capable of incorporating temporal dependencies present in customer loads. Consequently, to obtain a more efficient and accurate state estimation, we propose a new non-iterative DSSE framework to involve spatial-temporal dependencies together. The spatial-temporal dependencies are modeled by conditional multivariate complex Gaussian distributions and are studied for both static and real-time state estimations, where information at preceding time steps are employed to increase the accuracy of DSSE. The efficiency of the proposed approach is verified based on the quality and accuracy of the indices, standard deviation and computational time. The method applied to a combination of residential and industrial customer loads in three different balanced medium voltage (MV), and one unbalanced low voltage (LV) distribution case studies for evaluations.