Detection of Unauthorized Electricity Consumption using Machine Learning

Transcription

1 Detection of Unauthorized Electricity Consumption using Machine Learning Bo Tang, Ph.D. Department of Electrical and Computer Engineering Mississippi State University

2 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

3 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

4 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.

5 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

6 Detection of UEC with Machine Learning Fig. 1 Basic procedure for learning based energy theft detection

7 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.

8 Proposed Method Clustering the normal data to build multimodal 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

9 Relative Density-based Outlier Score (RDOS) Local Kernel Density Estimation

10 Relative Density-based Outlier Score (RDOS) Relative Density based Outlier Scores Anomalies detections p

11 RDOS: Theoretical Properties

12 Experimental Results Datasets: Smart energy data from the Irish Smart Energy Trial, including hourly electricity usage reports of Irish homes in 2009 and Synthetic unauthorized energy consumption Seven types of energy theft are generated.

13 Experimental Results

14 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)

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

16 Future Work Unauthorized energy consumption detection Advanced detection methods Real life data sets Privacy preservation in AMI Cloud computing and big data

17 Questions?