Neural-Network-Based Signature Recognition for Harmonic Source Identification
Abstract
This paper proposes a neural-network (NN)-based approach to nonintrusive harmonic source identification. In this approach, NNs are trained to extract important features from the input current waveform to uniquely identify various types of devices using their distinct harmonic "signatures". Such automated, noninvasive device identification will be critical in future power-quality monitoring and enhancement systems. Several NN-based classification models including multilayer perceptron (MLP), radial basis function (RBF) network, and support vector machines (SVM) with linear, polynomial, and RBF kernels were developed for signature extraction and device identification. These models were trained and tested using spike train data gathered from the Fourier analysis of the input current waveform in the presence of multiple devices. The performance of these models was compared in terms of their accuracy, generalization ability, and noise tolerance limits. The results showed that MLPs and SVM were both able to determine the presence of devices based on their harmonic signatures with high accuracy. MLP was found to be the best signature identification method because of its low computational requirements and ability to extract the information necessary for highly accurate device identification.
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Promising ML approaches to energy disaggregation include hidden Markov models, sparse coding, harmonic analysis, and deep neural networks.
“Promising ML approaches to this problem include hidden Markov models [445], sparse coding algorithms for structured prediction [443], harmonic analysis that picks out the "signatures" of individual appliances [750], and deep neural networks [423].”
From Tackling climate change with machine learning (2023) · cites this paper as [750]
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Is disaggregation the holy grail of energy efficiency? The case of electricity
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Tackling climate change with machine learning
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Review of Ai Applications in Harmonic Analysis in Power Systems
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- Authors
- Srinivasan, D., Ng, W. S., & Liew, A. C.
- Affiliations
- National University of Singapore (SG)
- Venue
- IEEE Transactions on Power Delivery
- Published
- 2006-01
- Volume
- 21
- Issue
- 1
- Pages
- 398-405
- ISSN
- 0885-8977 (ISSN-L)
- OpenAlex
- W2131634249
- Cited by
- 453
- References
- 19
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 16 · 2023: 18 · 2022: 20 · 2021: 30 · 2020: 37 · 2019: 46 · 2018: 41 · 2017: 45 · 2016: 31 · 2015: 34 · 2014: 32 · 2013: 28 · 2012: 24
- FWCI
- 1.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Power Delivery (publishedVersion)
- National University of Singapore (submittedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Power Quality and Harmonics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Electrical Measurement Techniques
- Keywords
- harmonic source identification, non-intrusive load monitoring, device identification, multilayer perceptron, support vector machine, radial basis function neural network, power quality monitoring
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