Hybrid state-estimation in combined heat and electric network using SCADA and AMI measurements

Srinivas, Vedantham Lakshmi, Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602, Singh, Bhim and Mishra, Sukumar 2023. Hybrid state-estimation in combined heat and electric network using SCADA and AMI measurements. International Journal of Electrical Power & Energy Systems 156 , 109726. 10.1016/j.ijepes.2023.109726

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Abstract

State-estimation plays a vital role to monitor, observe and understand the combined heat and electric network. In this paper, a hybrid framework is presented to accurately estimate the system states of electric distribution network and heat network, using the limited non-redundant measurements obtained from supervisory control and data acquisition and advanced metering infrastructure systems. The presented hybrid framework involves two steps, namely, the state-forecasting and the state-estimation. The state-forecasting uses a deep neural network to forecast the system states at every fifteen minutes interval, while these forecasted states are further used by the hybrid estimator, which uses a robust extended Kalman filter to estimate the system states with help of both datasets corresponding to supervisory control and data acquisition and advanced metering infrastructure systems, at hourly interval. The proposed framework does not completely rely on the system model at different instants. The effectiveness of the method is validated through thorough comparisons with simulation studies carried out using the Barry Island test system, United Kingdom. Satisfactory performance is observed even with the presence of bad data in the measurements.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/, Start Date: 2023-12-12
Publisher:	Elsevier
ISSN:	0142-0615
Date of First Compliant Deposit:	2 January 2024
Date of Acceptance:	12 December 2023
Last Modified:	02 Jan 2024 16:50
URI:	https://orca.cardiff.ac.uk/id/eprint/165145

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