Browsing by Subject "power engineering computing"
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Item AI based approach to identify compromised meters in data integrity attacks on smart grid(IET, 2017-10-02) Khanna, Kush; Panigrahi, Bijaya Ketan; Joshi, AnupamFalse data injection attacks can pose serious threats to the operation and control of power grid. The smarter the power grid gets, the more vulnerable it becomes to cyber attacks. Various detection methods of cyber attacks have been proposed in the literature in recent past. However, to completely alleviate the possibility of cyber threats, the compromised meters must be identified and secured. In this paper, we are presenting an Artificial Intelligence (AI) based identification method to correctly single out the malicious meters. The proposed AI based method successfully identifies the compromised meters by anticipating the correct measurements in the event of the cyber attack. NYISO load data is mapped with the IEEE 14 bus system to validate the proposed method. The efficiency of the proposed method is compared for Artificial Neural Network (ANN) and Extreme Learning Machine (ELM) based AI techniques. It is observed that both the techniques identify the corrupted meters with high accuracy.Item Bid Modification Attack in Smart Grid for Monetary Benefits(IEEE, 2016-12-19) Khanna, Kush; Joshi, Anupam; Panigrahi, Bijaya KetanIn the quest for reliability and automation, the entire smart grid operation and control depends on the communication infrastructure. This reliance on the information and communication technologies has also opened up possibilities of cyber intrusions. In this paper, a bid modification attack on the power exchange server is presented with the aim of gaining monetary benefits in the real-time power market. The attack is modelled for PJM 5 bus and IEEE 14 bus test system. The minimum number of load bids required to be changed for launching the attack is obtained and impacts on real time locational marginal prices (LMPs) are presented.