Neural-network-based signature recognition for harmonic source identification

By Dipti Srinivasan, W. S. Ng, A. Liew. IEEE Transactions on Power Delivery, 2006

447 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/7ef3f88b656d1df120ebb465288721a601dc2774

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Muhammad Ali Imran
2. Ahmed Zoha
3. Alexander D. Gluhak
4. Sutharshan Rajasegarar
5. Demis Hassabis
6. Alexandra Sasha Luccioni
7. Lynn H. Kaack
8. Kurt Roth

Citations in the corpus

• Tackling Climate Change with Machine Learning (2019, methodology): “Promising ML approaches to this problem include hidden Markov models [278], sparse coding algorithms for structured prediction [279], harmonic analysis that picks out the “signatures” of individual appliances [280], and deep neural networks [281].”
• Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey (2012, methodology): “(a) Harmonic signature of monitor where black bars show fluctuations, (From [17]); (b) Schematic diagram of two unit graph, (From [8]).”
• Nonintrusive appliance load monitoring: Review and outlook (2011, methodology): “This approach can be viewed as a pattern recognition approach with such techniques as nnearest-neighbor [30], [36], [37], Bayes classifier [39] and neural networks [27], [34].”