Machine-Learning-Driven Identification of Electrical Phases in Low-Sampling-Rate Consumer Data

Machine-Learning-Driven Identification of Electrical Phases in Low-Sampling-Rate Consumer Data

Accurate electrical phase identification (PI) is essential for efficient grid management, yet existing research predominantly focuses on high-frequency smart meter data, not adequately addressing phase identification with low sampling rates using energy consumption data. This study addresses this ga...

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Bibliographic Details
Main Authors: Dilan C. Hangawatta, Ameen Gargoom, Abbas Z. Kouzani
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Energies
Subjects:
phase identification
low sampling rate
energy consumption data
fully connected neural network
recursive feature elimination
generative adversarial network
Online Access:https://www.mdpi.com/1996-1073/18/1/128
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