An anomaly detection framework for identifying energy theft and defective meters in smart grids

Sook Chin Yip, Wooi Nee Tan, Chia Kwang Tan, Ming Tao Gan, Kok Sheik Wong

Research output: Contribution to journalArticleResearchpeer-review

101 Citations (Scopus)

Abstract

Smart meters are progressively deployed to replace its antiquated predecessor to measure and monitor consumers’ consumption in smart grids. Although smart meters are equipped with encrypted communication and tamper-detection features, they are likely to be exposed to multiple cyber attacks. These meters may be easily compromised to falsify meter readings, which increases the chances and diversifies the types of energy theft. To thwart energy fraud from smart meters, utility providers are identifying anomalous consumption patterns reported to operation centers by leveraging on consumers’ consumption data collected from advanced metering infrastructure. In this paper, we put forward a new anomaly detection framework to evaluate consumers’ energy utilization behavior for identifying the localities of potential energy frauds and faulty meters. Metrics known as the loss factor and error term are introduced to estimate the amount of technical losses and capture the measurement noise, respectively in the distribution lines and transformers. The anomaly detection framework is then enhanced to detect consumers’ malfeasance and faulty meters even when there are intermittent cheating and faulty equipment, improving its robustness. Results from both simulations and test rig show that the proposed framework can successfully locate fraudulent consumers and discover faulty smart meters.

Original languageEnglish
Pages (from-to)189-203
Number of pages15
JournalInternational Journal of Electrical Power and Energy Systems
Volume101
DOIs
Publication statusPublished - Oct 2018

Keywords

  • AMI
  • Anomaly detection
  • Linear programming
  • Non-technical losses
  • Smart grids
  • Technical losses

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