An Electricity Theft Cyber-Attacks’ Detection System for Future IoT-Based Smart Electric Meters in Renewable Distributed Generation

Abstract

More than any other source of energy, electricity today needs to be used efficiently. Power theft is a leading cause of nontechnical losses in distribution networks. Electricity providers around the world are facing a major financial burden due to this issue. New methods of electricity theft are made possible by the smart grid paradigm. To begin, cybercriminals can now steal electricity from a distance. Smart meters placed as part of the AMI collect data that may be used to track and bill clients for their energy usage in real time. A malicious client could launch a cyber-attack on the smart meters in an effort to reduce their electricity cost. Another perk of the smart grid model is that consumers can make money by producing their own electricity from renewable sources and selling it back to the grid operators. Methods for connecting renewable DG units to the grid, such as the net metering system and the Feed-in Tariffs policy, are discussed in this article. Through the net metering system, customers are able to “bank” the DG’s excess generation for later consumption. Consumers who sell all of the electricity they create back to the grid receive compensation under the FIT scheme, often known as clean energy pay back. The financial incentives provided by FIT schemes significantly increase the rate at which renewable energy sources are adopted. To take part in FIT, a client needs two meters: one to track energy generated by DG unit and injected into grid and another to track the energy consumed within the home. Both production and consumption-based energy pricing models benefit from this. Therefore, the aim of this research is to compare the performance of various deep learning algorithms include DNN, RNN-GRU, and CNN for spotting power-grid attacks.