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A new dynamic state estimation method for distribution networks based on modified SVSF considering photovoltaic power prediction

Authors :
Huiqiang Zhi
Xiao Chang
Jinhao Wang
Rui Mao
Rui Fan
Tengxin Wang
Jinge Song
Guisheng Xiao
Source :
Frontiers in Energy Research, Vol 12 (2024)
Publication Year :
2024
Publisher :
Frontiers Media S.A., 2024.

Abstract

The fluctuations brought by the renewable energy access to the distribution network make it difficult to accurately describe the state space model of the distribution network’s dynamic process, which is the basis of the existing dynamic state estimation methods such as the Kalman filter. The inaccurate state space model directly causes an error of dynamic state estimation results. This paper proposed a new dynamic state estimation method which can mitigates the impact of renewable energy fluctuation by considering PV power prediction in establishing distribution network state space model. Firstly, the proposed method mitigates the impact of renewable energy fluctuation by considering PV power prediction in establishing distribution network state space model. Secondly, SVSF filter is introduced to achieve more accurate estimation under noise. The case study and evaluations are carried out based on MATLAB simulation. The results prove that the smooth variable structure filter with photovoltaic power prediction has a better dynamic state estimation effect under the fluctuation of the distribution network compared with the existing Kalman filter.

Details

Language :
English
ISSN :
2296598X
Volume :
12
Database :
Directory of Open Access Journals
Journal :
Frontiers in Energy Research
Publication Type :
Academic Journal
Accession number :
edsdoj.b87e82efcee14827a7ab654621ab28ae
Document Type :
article
Full Text :
https://doi.org/10.3389/fenrg.2024.1421555