Identification of Multi-Sinusoidal Signal Models: Application to Non-Intrusive Industrial Load Monitoring

This paper proposes a new Non-Intrusive Load Monitoring (NILM) approach to deal with industrial settings. The goal is to supervise the states of a set of appliances connected to the same electrical network. To do so, the proposed method makes use of characteristic frequencies, thus implying the need of an algorithm capable of estimating signal frequencies and magnitudes. Moreover, prior information about the appliances is to be obtained through a feature extraction step. Some classification algorithms will be tested and compared to each other.

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Published
2024-11-03
DOI
https://doi.org/10.1109/icsc63929.2024.10928733
Primary Topic
Structural Health Monitoring Techniques
Type
article
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article

Identification of Multi-Sinusoidal Signal Models: Application to Non-Intrusive Industrial Load Monitoring

Afzal Chamroo, Sébastien Cauët, Antonin Rivier
Structural Health Monitoring Techniques
article

Identification of Multi-Sinusoidal Signal Models: Application to Non-Intrusive Industrial Load Monitoring

Afzal Chamroo, Sébastien Cauët, Antonin Rivier
article en

Abstract

This paper proposes a new Non-Intrusive Load Monitoring (NILM) approach to deal with industrial settings. The goal is to supervise the states of a set of appliances connected to the same electrical network. To do so, the proposed method makes use of characteristic frequencies, thus implying the need of an algorithm capable of estimating signal frequencies and magnitudes. Moreover, prior information about the appliances is to be obtained through a feature extraction step. Some classification algorithms will be tested and compared to each other.

Université de Poitiers (FR), Laboratoire d'Informatique et d'Automatique pour les Systèmes (FR)
Openalex Percentile: Top 68%
Structural Health Monitoring Techniques
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Identification of Multi-Sinusoidal Signal Models: Application to Non-Intrusive Industrial Load Monitoring — Afzal Chamroo, Sébastien Cauët, et al. · (2024) | TGRS Research Map | TGRS