Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6859240 | International Journal of Electrical Power & Energy Systems | 2018 | 15 Pages |
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.
Keywords
SGSLUDneighborhood area networkWANLSEDERNILMADFTLSMLRAmIMDMsELMLinear programmingAnomaly detectionNon-Technical LossesnanDecision treeExtreme learning machineMultiple linear regressionAdvanced metering infrastructureLinear system of equationsSmart GridsWide area networkTechnical LossesInformation and Communication TechnologyICTSVMSupport vector machineNon-intrusive load monitoringSmart metersDistributed energy resourcelow voltageSMSkWhUPS
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Sook-Chin Yip, Wooi-Nee Tan, ChiaKwang Tan, Ming-Tao Gan, KokSheik Wong,