@inproceedings{f607da355c3044718e3cfc43f2787456,
title = "Learning-Based Customer Voltage Visibility With Sparse High-Reporting-Rate Smart Meters",
abstract = "The conventional passive distribution network (DN) has evolved into an active DN enriched with distributed energy resources (DERs). The network's voltage profile becomes volatile and fluctuating, necessitating real-time high-granular voltage estimation for prompt decision-making in the network. This paper introduces an innovative real-time voltage estimation scheme on the customer side using sparsely but strategically deployed high-reporting-rate smart meters (HRRSMs). With a deep learning model capturing network and temporal correlations, the real-time voltage data reported by sparse HRRSMs can be leveraged to estimate the voltages at the terminals of all the unmeasured customers in high resolution and in real time. A clustering-based sparse HRRSM installation technique adhering to budgetary constraints and alleviates communication network burdens is also developed.",
keywords = "clustering, deep learning, low-voltage network, meter placement, power quality, smart meters, voltage estimation",
author = "Islam, {Md Zahidul} and Wentao Zhang and Yuzhang Lin",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE Power and Energy Society General Meeting, PESGM 2024 ; Conference date: 21-07-2024 Through 25-07-2024",
year = "2024",
doi = "10.1109/PESGM51994.2024.10688681",
language = "English (US)",
series = "IEEE Power and Energy Society General Meeting",
publisher = "IEEE Computer Society",
booktitle = "2024 IEEE Power and Energy Society General Meeting, PESGM 2024",
}