nikovski powertheftdetection mldm july2013 v2

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MITSUBISHI ELECTRIC RESEARCH LABORATORIES

Cambridge, Massachusetts

D. Nikovski, Z. Wang, A. Esenther, H. Sun

* * *

Smart Meter Data Analysis for Power Theft Detection

MERL

. , . , .

(* Mitsubishi Electric Corporation)

9th International Conference on Machine Learning and Data Mining MLDM 2013July 23, 2013, New York


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Outline Problem definition

Algorithm overview and main idea

Sources of technical losses in distribution networks

Estimation of technical losses:

equivalent circuit model

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ne res s ance es ma on

Power theft detector: choice of detection threshold

Experimental testbed:

simulator

test system

Experimental results


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Power Theft Detection A major source of losses for electrical utilities

In some markets (e.g. Southeast Asia), it can be as

high as 30%-50%

Smart meters are expected to lead to much better

tracking, and hopefully recovery of revenue

However, energy balance methods (between

transformer and individual meters cannot distin uish

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well between technical losses (e.g. resistance) andnon-technical losses (theft).

Proposed method:

Use a learning method to build a predictive model for

technical losses

Estimate technical losses at run time using the

model

Use an anomaly detection algorithm to detect theft


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Power Theft Detection Procedure

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We assume technical losses are mainly ohmic, and due to the resistance of the

distribution lines:

Load variation changes losses, for the same amount of consumption:

Sources of Technical Losses and Their Estimation

Case 1: Suppose the line resistance is 1ohm, the current drawn by the load is 1 A and the load is

(3)

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Conslusion: it is not possible to estimate technical losses exactly, even if we

knew the actual line resistances, due to infrequent measurements (30 min)

Our estimates of technical losses will be random variables, and from there, our

estimates of non-technical losses will be random variables, too.5

active for one hour. The total loss caused by the transmission line will be:

Case 2: Suppose the current drawn by the load is doubled but the load is active for 0.5 hour.

The total loss caused by the transmission line will be:


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Equivalent Circuit Model Problem: we dont know the topology of the distribution network

Legal user 3

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We will assume that each load is on a separate branch (distribution line)

DT

Legal user 1 Legal user 2

Power theft

Legal user 3


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Line Resistance Estimation

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Line Resistance Estimation (2)

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Technical Loss EstimationUse exactly the same equation, with the line resistances estimated before, and

current (I) measurements for the latest period of time:

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Choice of Detection Threshold Technical loss is a random

variable with unknown distribution

Estimate its normal variation from

collected data, and put the

threshold at the maximal

observed variation for cases

without power theft

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Do not use the same data set that

was used for estimation of line

resistances! If we do that, the

threshold would be too low

Use a separate data set, e.g.

another period of time for the

same distribution network


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Experimental Testbed One branch of a distribution network

Ten loads attached to one distribution transformer

When there is theft, one of the loads is not measured (approx. 10% theft)

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Load Profiles Used in Experiments We start with a smooth load profile (aggregate for UK)

We then introduce stochastic variations, using an autoregressive process of

order one (AR(1)):

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Generation of Experimental Data Using Simulation Do full load-flow analysis every 10 seconds:

Voltage

Current

Power

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Line losses: replace integral with finite sum:

Aggregate energy consumption for each load and for the distribution transformer

over 30 minutes, as a smart meter would report it.


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Experimental Results Testing Protocol Generate data for 6 days, with 4 days of no theft, and 2 days of theft

From the first two days, estimate the line resistances and threshold

Test performance on the last four days

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Experimental Results Resistance Estimation Fairly accurate estimates of resistances:

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Experimental Results NTL Estimates Full separation is possible for 10% theft.

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Power Theft Estimates and Detection Accuracy

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Conclusion and Future Work We have proposed a method for power theft detection that learns a model of

technical losses and can distinguish between technical and non-technical losses

(theft).

Currently, the line resistances are assumed to be constant, but they depend on

air temperature, actually we plan to introduce temperature into the model

In practice, meter measurements are not synchronous does this affect the

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accuracy of detection?

Are unbalanced three-phase loads more difficult to process?

What if only some of the passengers have smart meters, and the others have

regular meters measured once per month?


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What If We Train on Data with Theft?

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nikovski powertheftdetection mldm july2013 v2

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