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Detection of Unauthorized Electricity Consumption using Machine Learning

Bo Tang, Ph.D.Department of Electrical and Computer

EngineeringMississippi State University


Outline• Advanced Metering Infrastructure (AMI) in Smart Grid

• Unauthorized Electricity Consumption (UEC)

• Detection of UEC using Machine Learning Algorithms

• Preliminary Simulation Results

• Future Work and Challenges


Overview of Advanced Metering Infrastructure

• AMI:• Comprised of state‐of‐the‐art

electronic/digital hardware and software.

• Enable detailed, time‐based data measurements and their transmissions.

• Benefits:• System operation benefits• Customer service benefits• Financial benefits

Source: Electric Power Research Institute


Unauthorized Electricity Consumption• Ways of unauthorized electricity consumption (energy theft)

• Taking connections directly from distribution line• Grounding the neutral wire• Inserting some disc to stop rotating of the coil• Hitting the meter to damage the rotating coil• Interchanging input output connections

• Difficult to check these issues with AMI.• It is estimated that utility companies lose more than $25 billion every

year due to energy theft around the world.


Techniques for UEC Detection• Statistical approaches

• Hypothesis test• Learning‐based Approaches

• SVM, Neural Networks, Fuzzy classification, ARMA‐GLRT • State‐based approaches

• Sensor monitoring, RFID monitoring, Mutual inspection, State estimation‐based.

• Game theory‐based approaches


Detection of UEC with Machine Learning

Fig. 1 Basic procedure for learning‐based energy‐theft detection


Anomaly Detection• Anomaly is a pattern that does not conform to the expected behavior.• Also referred to outliers, exceptions, surprises, novelty, etc.

• General Steps for Anomaly Detection:• Build a profile (or pattern) of the normal behavior• Use the normal profile to detect anomalies

• Anomalies are observations whose characteristics differ significantly from normal profile.


Proposed Method• Clustering the normal data to build multi‐modal profiles• K‐mean clustering algorithms• Silhouette (cohesion and separation) measure is

to used to determine the number of patterns (clusters)

• Apply distance‐based (neighbors‐based) anomaly detection approaches.

p


Relative Density-based Outlier Score (RDOS)

• Local Kernel Density Estimation


Relative Density-based Outlier Score (RDOS)

• Relative Density‐based Outlier Scores

• Anomalies detections p


RDOS: Theoretical Properties


Experimental Results• Datasets:

• Smart energy data from the Irish Smart Energy Trial, including hourly electricity usage reports of Irish homes in 2009 and 2010.

• Synthetic unauthorized energy consumption• Seven types of energy theft are generated.


Experimental Results


Experimental Results• Seven distance‐based anomaly detection algorithms:

• Relative Density‐based Outlier Score (RDOS) • Local Outlier Factor (LOF)• Local Density Factor (LDF)• Flexible Kernel Density Estimates (KDEOS)• Influenced Outlierness (INFLO)• Mutual k‐nearest neighbor (MNN) • Indegree Number(ODIN)


Experimental Results• Top 3 anomaly detection algorithms: AUC (area under ROC curve)

Theft‐Type 1st 2nd 3rd

1 RDOS(0.88) LDF(0.84) INFLO(0.80)

2 LDF(0.79) RDOS(0.76) ODIN(0.72)

3 RDOS(0.93) MNN(0.86) ODIN(0.81)

4 RDOS(0.94) ODIN(0.86) INFLO(0.85)

5 RDOS(0.96) LDF(0.90) INFLO(0.88)

6 LDF(0.95) RDOS(0.93) ODIN(0.88)

7 RDOS(0.94) LDF(0.89) KDEOS(0.8)

OA RDOS(0.92) LDF(0.85) INFLO(0.80)Example of ROC curve


Future Work• Unauthorized energy consumption detection

• Advanced detection methods• Real‐life data sets

• Privacy preservation in AMI• Cloud computing and big data


Questions?

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