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TNT Loss: A Technical and Nontechnical Generative Cooperative Energy Loss Detection System

This paper describes an electricity technical/nontechnical loss detection method capable of loss type identification, classification, and location. Several technologies are implemented to obtain that goal: (i) an architecture of three generative cooperative AI modules and two additional non-cooperat...

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Autores principales: Calamaro, Netzah, Levy, Michael, Ben-Melech, Ran, Shmilovitz, Doron
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9501005/
https://www.ncbi.nlm.nih.gov/pubmed/36146349
http://dx.doi.org/10.3390/s22187003
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author Calamaro, Netzah
Levy, Michael
Ben-Melech, Ran
Shmilovitz, Doron
author_facet Calamaro, Netzah
Levy, Michael
Ben-Melech, Ran
Shmilovitz, Doron
author_sort Calamaro, Netzah
collection PubMed
description This paper describes an electricity technical/nontechnical loss detection method capable of loss type identification, classification, and location. Several technologies are implemented to obtain that goal: (i) an architecture of three generative cooperative AI modules and two additional non-cooperative AI modules for data knowledge sharing is proposed, (ii) new expert consumption-based knowledge of feature collaboration of the entire consumption data are embedded as features in an AI classification algorithm, and (iii) an anomaly pooling mechanism that enables one-to-one mapping of signatures to loss types is proposed. A major objective of the paper is an explanation of how an exact loss type to signature mapping is obtained simply and rapidly, (iv) the role of the reactive energy load profile for enhancing signatures for loss types is exemplified, (v) a mathematical demonstration of the quantitative relationship between the features space to algorithm performance is obtained generically for any algorithm, and (vi) a theory of “generative cooperative modules” for technical/nontechnical loss detection is located and mapped to the presented system. The system is shown to enable high-accuracy technical/nontechnical loss detection, especially differentiated from other grid anomalies that certainly exist in field conditions and are not tagged in the universal datasets. The “pooling” architecture algorithm identifies all other loss types, and a robotic process automation module obtains loss type localization. The system feeds from the entire smart metering data, not only the energy load profile. Other solutions, such as a stand-alone algorithm, have difficulty in obtaining low false positive in field conditions. The work is tested experimentally to demonstrate the matching of experiment and theory.
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spelling pubmed-95010052022-09-24 TNT Loss: A Technical and Nontechnical Generative Cooperative Energy Loss Detection System Calamaro, Netzah Levy, Michael Ben-Melech, Ran Shmilovitz, Doron Sensors (Basel) Article This paper describes an electricity technical/nontechnical loss detection method capable of loss type identification, classification, and location. Several technologies are implemented to obtain that goal: (i) an architecture of three generative cooperative AI modules and two additional non-cooperative AI modules for data knowledge sharing is proposed, (ii) new expert consumption-based knowledge of feature collaboration of the entire consumption data are embedded as features in an AI classification algorithm, and (iii) an anomaly pooling mechanism that enables one-to-one mapping of signatures to loss types is proposed. A major objective of the paper is an explanation of how an exact loss type to signature mapping is obtained simply and rapidly, (iv) the role of the reactive energy load profile for enhancing signatures for loss types is exemplified, (v) a mathematical demonstration of the quantitative relationship between the features space to algorithm performance is obtained generically for any algorithm, and (vi) a theory of “generative cooperative modules” for technical/nontechnical loss detection is located and mapped to the presented system. The system is shown to enable high-accuracy technical/nontechnical loss detection, especially differentiated from other grid anomalies that certainly exist in field conditions and are not tagged in the universal datasets. The “pooling” architecture algorithm identifies all other loss types, and a robotic process automation module obtains loss type localization. The system feeds from the entire smart metering data, not only the energy load profile. Other solutions, such as a stand-alone algorithm, have difficulty in obtaining low false positive in field conditions. The work is tested experimentally to demonstrate the matching of experiment and theory. MDPI 2022-09-15 /pmc/articles/PMC9501005/ /pubmed/36146349 http://dx.doi.org/10.3390/s22187003 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Calamaro, Netzah
Levy, Michael
Ben-Melech, Ran
Shmilovitz, Doron
TNT Loss: A Technical and Nontechnical Generative Cooperative Energy Loss Detection System
title TNT Loss: A Technical and Nontechnical Generative Cooperative Energy Loss Detection System
title_full TNT Loss: A Technical and Nontechnical Generative Cooperative Energy Loss Detection System
title_fullStr TNT Loss: A Technical and Nontechnical Generative Cooperative Energy Loss Detection System
title_full_unstemmed TNT Loss: A Technical and Nontechnical Generative Cooperative Energy Loss Detection System
title_short TNT Loss: A Technical and Nontechnical Generative Cooperative Energy Loss Detection System
title_sort tnt loss: a technical and nontechnical generative cooperative energy loss detection system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9501005/
https://www.ncbi.nlm.nih.gov/pubmed/36146349
http://dx.doi.org/10.3390/s22187003
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