Payback analysis in identification and monitoring of commercial losses in distribution networks

Evaldt, Maicon Coelho; Santos, João A.; Figueiredo, Rodrigo Marques de; Silva, Leandro T. da; Stracke, Marcio R.

doi:10.1109/eem.2012.6254694

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…Monitoring units in the system consist of an IED with costs determined in [35] with two current transducers [36] to measure the current magnitude on each phase of the transformer. Labour costs assume 1 h for monitor installation and use Level C engineering salary [37].…”

Section: Appendixmentioning

confidence: 99%

Economic assessment of phase reconfiguration to mitigate the unbalance due to plug-in electric vehicles charging

Gray¹,

Morsi²

2016

Electric Power Systems Research

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Section: Appendixmentioning

confidence: 99%

Economic assessment of phase reconfiguration to mitigate the unbalance due to plug-in electric vehicles charging

Gray¹,

Morsi²

2016

Electric Power Systems Research

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“…Other research related to observer meters or mathematical approaches include [22]- [26], [60]- [64]. The papers [23], [25] construct a binary tree that uses the meters as the lead nodes.…”

Section: Related Workmentioning

confidence: 99%

NFD: Non-technical loss fraud detection in Smart Grid

Han¹,

Xiao²

2017

Computers & Security

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Utility companies consistently suffer from the harassing of Non-Technical Loss (NTL) frauds globally. In the traditional power grid, electricity theft is the main form of NTL frauds. In Smart Grid, smart meters thwart electricity theft in some ways but cause more problems, e.g., intrusions, hacking, and malicious manipulation. Various detectors have been proposed to detect NTL frauds, but they either rely on user behavior analysis which requires a large amount of historical data or needs a lot of extra devices which are expensive. In this paper, a detector, named NFD (NTL Fraud Detection), is proposed to detect NTL frauds with only a small amount of data and one additional device. NFD is based on Lagrange polynomial interpolation to model an adversary's behaviors, and detect a tampered meter by comparing the difference between the results. Different from existing detectors, our detector knows adversaries better than adversaries themselves. By building mathematical models of these adversaries, we can predict their behaviors which they may not be aware of by themselves. NFD makes it practical to detect NTL frauds both online and offline in Smart Grid. NFD can facilitate real-world applications before Smart Grid is fully deployed since it can serve the traditional power grid and Smart Grid simultaneously. Experimental results show the effectiveness of NFD. It can detect multiple tampered meters and multiple adversaries, as well as a single tampered meter and a single adversary. We also study how to tune the parameters used in NFD to further guide its practical usage.

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On the Use of Light Sensors and Pattern Recognition Techniques for Automated Detection of Street Lighting Lamps

Soares

Almeida

Mendes

et al. 2015

Smart Sensors, Measurement and Instrumentation

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Payback analysis in identification and monitoring of commercial losses in distribution networks

Cited by 6 publications

References 4 publications

Economic assessment of phase reconfiguration to mitigate the unbalance due to plug-in electric vehicles charging

Economic assessment of phase reconfiguration to mitigate the unbalance due to plug-in electric vehicles charging

NFD: Non-technical loss fraud detection in Smart Grid

On the Use of Light Sensors and Pattern Recognition Techniques for Automated Detection of Street Lighting Lamps

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