Abstract
The increasing use of information and communication technology (ICT) in electricity grid infrastructures facilitates improved energy generation, transmission, and distribution. However, smart grids are still in their infancy with a disparate regional role out. Due to the involved costs utility providers are only embedding ICT in selected parts of the grid, thereby creating only partial smart grid infrastructures. We argue that using the data provided by these partial smart grid deployments can still be beneficial in solving various issues such as energy theft detection. In this paper, we focus on various data-driven techniques to detect energy theft in power networks. These data-driven detection techniques (at the smart meter as well as the aggregated level) can indicate various forms of energy theft (e.g. through clandestine connections or meter tampering). This paper also presents two case studies to show the effectiveness of these approaches.
Highlights
I N recent years, electrical energy grids have undergone a major modernization process leading to improved energy generation, transmission, and distribution
In this paper we argue that, even in partial smart grid deployment scenarios, data-driven techniques can be useful for tackling the problem of energy theft detection
Our case studies demonstrated the potential of identifying energy theft, a major challenge across the globe that could be intelligently confronted by employing data-driven schemes in conjunction with smart meters/Advanced Metering Infrastructure (AMI) that exist in a modern grid infrastructure
Summary
I N recent years, electrical energy grids have undergone a major modernization process leading to improved energy generation, transmission, and distribution. Whilst the maturity of smart grid deployments in developed nations has made significant progress in the past few years [4], developing countries still rely on less sophisticated infrastructures These typically make use of information and communication technology (ICT) at just the consumption level with some automation in the transmission and distribution level. In this paper we argue that, even in partial smart grid deployment scenarios, data-driven techniques (based on the collection of data at only a single level) can be useful for tackling the problem of energy theft detection. The major contributions of the study presented in this paper are summarized as follows: the different viewpoints on data-driven techniques used for theft detection in smart grids are presented, highlighting the usefulness of such techniques.
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