Electricity Theft Detection Using Deep Reinforcement Learning in Smart Power Grids

In smart power grids, meters (SMs) are deployed at the end side of customers to report fine-grained consumption readings periodically utility for energy management and load monitoring. However, electricity theft cyber-attacks can be launched by fraudulent through compromising their SMs false pay less usage. These attacks harmfully affect sector since they cause substantial financial loss degrade grid performance because used management. Supervised machine learning approaches have been in literature detect attacks, but best our knowledge, use reinforcement (RL) has not investigated yet. RL better than existing it adapt more efficiently with dynamic nature patterns due its capability learn exploration exploitation mechanisms deciding optimal actions. this article, a deep (DRL) approach is proposed as promising solution problem. The samples real dataset employed an environment rewards given based on detection errors made during training. particular, presented four different scenarios. First, global model constructed using Q network (DQN) double (DDQN) architectures neural networks. Second, detector build customized new achieve high accuracy while preventing zero-day attacks. Third, changing pattern taken into consideration third scenario. Fourth, challenges defending against newly addressed fourth Extensive experiments conducted, results demonstrate that DRL boost cyberattacks, patterns, changes customers, cyber-attacks.