A complete state estimation algorithm for a three-phase four-wire low voltage distribution system with high penetration of solar PV

Chaminda Bandara, Wele Gedara, Almeida, Dilini, Godaliyadda, Roshan Indika, Ekanayake, Mervyn Parakrama and Ekanayake, Janaka ORCID: https://orcid.org/0000-0003-0362-3767 2021. A complete state estimation algorithm for a three-phase four-wire low voltage distribution system with high penetration of solar PV. International Journal of Electrical Power and Energy Systems 124 , 106332. 10.1016/j.ijepes.2020.106332

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Abstract

Low Voltage Distribution Grids (LVDGs) become highly unbalanced due to the advent of single-phase solar PV plants. As a result, the voltage and current levels of the neutral conductor show a significant increase. Therefore, monitoring of the entire state of the network is essential. However, the existing state estimation algorithms estimate voltage states of the phase conductors while ignoring the state of the neutral conductor. This paper presents a novel approach to estimate the complete state of the LVDGs. A novel state reduction method was introduced to model the three-phase four-wire feeder line using a admittance matrix, which incorporates the neutral coupling effect on phase conductors. Next, the reduced admittance matrix together with the linear approximations of active and reactive power functions were combined to formulate the Low Voltage-Linear State Estimation (LV-LSE) algorithm. Finally, the performance of LV-LSE algorithm was analyzed for different measurement uncertainties, scales of line lengths of the network, and data-loss conditions. Results show that, for all the cases, LV-LSE algorithm together with the proposed reduction method can estimate voltage states with an average maximum voltage magnitude error of less than pu and current states with an average maximum current magnitude error of less than pu.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0142-0615
Date of Acceptance:	27 June 2020
Last Modified:	07 Nov 2022 11:15
URI:	https://orca.cardiff.ac.uk/id/eprint/135048

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