DEEP LEARNING APPROACHES FOR CYBER-ATTACK DETECTION IN ELECTRICITY DISTRIBUTION NETWORKS FOR SAFEGUARDING AGAINST TAMPERING AND THEFT

Abstract

Electricity theft represents a pressing problem that has brought enormous financial losses to electric utility companies worldwide. In the United States alone, $6 billion worth of electricity is stolen annually. Traditionally, electricity theft is committed in the consumption domain via physical attacks that includes line tapping or meter tampering. The smart grid paradigm opens the door to new forms of electricity theft attacks. First, electricity theft can be committed in a cyber manner. With the advanced metering infrastructure (AMI), smart meters are installed at the customers’ premises and regularly report the customers’ consumption for monitoring and billing purposes. In this context, malicious customers can launch cyber-attacks on the smart meters to manipulate the readings in a way that reduces their electricity bill. Second, the smart grid paradigm enables customers to install renewable-based distributed generation (DG) units at their premises to generate energy and sell it back to the grid operator and hence make a profit. Therefore, this project evaluating performance of various deep learning algorithms such as deep feed forward neural network (DNN), and recurrent neural network with gated recurrent unit (RNN-GRU) for electricity cyber-attack detection. Now-a-days in advance countries solar plates are used to generate electricity and these users can sale excess energy to other needy users and they will be maintained two different meters which will record consumption and production details.