Smart Meter Data Analysis for Power Theft Detection

Nikovski, Daniel; Wang, Zhenhua; Esenther, Alan; Sun, Hong‐Bo; Sugiura, Keisuke; Muso, Toru; Tsuru, Kaoru

doi:10.1007/978-3-642-39712-7_29

Cited by 45 publications

(25 citation statements)

References 3 publications

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“…A novel way for estimating technical losses was proposed in [4]. The work assumed that the utility received data from every smart meter and DT every 30 minutes.…”

Section: Power Theft Detection Based On a Technical Loss Modelmentioning

confidence: 99%

“…= − (2) The method in [4] approximates the distribution circuit downstream of the DT to be radial in nature as shown in Fig. 1 [9]. Since it assumed the resistances in the network to be constant, it is referred to as the Constant Resistance Technical Loss Model (CRTLM).…”

Section: Power Theft Detection Based On a Technical Loss Modelmentioning

confidence: 99%

“…A very common approach involves bypassing the meter completely [4]. This is done by directly connecting the mains of the house to the low voltage grid.…”

Section: Introductionmentioning

confidence: 99%

“…This work is an extension of the work mentioned in [4]. A predictive model to calculate TL was developed in [4] without using the actual topology information of the network.…”

Section: Introductionmentioning

confidence: 99%

“…A predictive model to calculate TL was developed in [4] without using the actual topology information of the network. We have improved the predictive model to estimate technical loss in a branch of the distribution network by taking into consideration the temperature dependency of resistances in the circuit.…”

Section: Introductionmentioning

confidence: 99%

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Electricity theft detection using smart meter data

Sahoo¹,

Nikovski

Muso

et al. 2015

2015 IEEE Power &Amp; Energy Society Innovative Smart Grid Technologies Conference (ISGT)

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Electricity theft is a major concern for the utilities. With the advent of smart meters, the frequency of collecting household energy consumption data has increased, making it possible for advanced data analysis, which was not possible earlier. We have proposed a temperature dependent predictive model which uses smart meter data and data from distribution transformer to detect electricity theft in an area. The model was tested for varying amounts of power thefts and also for different types of circuit approximations. The results are encouraging and the model can be used for real world application. IEEE PES Innovative Smart Grid Technologies Conference (ISGT)This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.

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“…A novel way for estimating technical losses was proposed in [4]. The work assumed that the utility received data from every smart meter and DT every 30 minutes.…”

Section: Power Theft Detection Based On a Technical Loss Modelmentioning

confidence: 99%

Section: Power Theft Detection Based On a Technical Loss Modelmentioning

confidence: 99%

“…A very common approach involves bypassing the meter completely [4]. This is done by directly connecting the mains of the house to the low voltage grid.…”

Section: Introductionmentioning

confidence: 99%

“…This work is an extension of the work mentioned in [4]. A predictive model to calculate TL was developed in [4] without using the actual topology information of the network.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations