Browsing by Subject "advanced metering infrastructure"
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Item Bi-level Modelling of False Data Injection Attacks on Security Constrained Optimal Power Flow(IET, 2017-06-12) Khanna, Kush; Joshi, Anupam; Panigrahi, Bijaya KetanConventional power system was originally designed to provide efficient and reliable power. With the integration of information technology and advanced metering infrastructure, the power grid has become smart. The smart meters have allowed the system operators to continuously monitor the power system in real time and take necessary action to avoid system failures. Malicious actor, with access to the smart meters can modify sensor measurements to disrupt the operation of power system. To make the power system resilient to such cyber-attacks, it is important to study all possible outcomes of cyber-intrusions. In this paper, we present an attack on security constrained optimum power flow. We show with the help of case studies how an attacker, by injecting false data in load measurement sensors, can force system operator to change the dispatch and hence make the power system N–1 in-compliant. The attack is modeled as a bi-level optimization problem, aiming to find the minimum set of sensors required to launch the attack. From the system operator's perspective, critical lines and critical generators vulnerable to false data injection (FDI) attack are identified. IEEE 14 bus and 30 bus test systems are used to test the vulnerability of the power system against FDI attacks.Item Entropy-based electricity theft detection in AMI network(IET, 2018-08-31) Singh, Sandeep Kumar; Bose, Ranjan; Joshi, AnupamAdvanced metering infrastructure (AMI), one of the prime components of the smart grid, has many benefits like demand response and load management. Electricity theft, a key concern in AMI security since smart meters used in AMI are vulnerable to cyber attacks, causes millions of dollar in financial losses to utilities every year. In light of this problem, the authors propose an entropy-based electricity theft detection scheme to detect electricity theft by tracking the dynamics of consumption variations of the consumers. Relative entropy is used to compute the distance between probability distributions obtained from consumption variations. When electricity theft attacks are launched against AMI, the probability distribution of consumption variations deviates from historical consumption, thus leading to a larger relative entropy. The proposed method is tested on different attack scenarios using real smart-meter data. The results show that the proposed method detects electricity theft attacks with high detection probability.