Browsing by Subject "Mathematical model"
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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.Item Feasibility and mitigation of false data injection attacks in smart grid(IEEE, 2016-10-06) Khanna, Kush; Joshi, AnupamThe power grid is evolving rapidly. With the addition of micro-grids and renewable energy resources, and increasing automation in decision-making enabled by sensors, the grid has become very complex. Research in the area of smart grids shows that the grid is vulnerable to cyber-attacks. In particular, recent studies reveals how false data injection could lead to variety of problems in the smart grid operation. A well-crafted attack can pass the bad data detection systems during state estimation and affect the operation and control of the power grid. In this paper, we build on prior efforts in this space to describe how false data injection attacks can be alleviated using conventional techniques by protecting certain critical sensors in the power system. The feasibility of false data injection attacks with incomplete network knowledge is explained in this paper considering IEEE 14 bus test system. The assumptions for defining the attacking region are also validated with the help of different case studies. This paper depicts the importance of securing the power grid against cyber-attacks.Item Inferring Relations in Knowledge Graphs with Tensor Decompositions(IEEE, 2017-02-06) Padia, Ankur; Kalpakis, Kostantinos; Finin, TimMulti-relational data, like knowledge graphs, are generated from multiple data sources by extracting entities and their relationships. We often want to include inferred, implicit or likely relationships that are not explicitly stated, which can be viewed as link-prediction in a graph. Tensor decomposition models have been shown to produce state-of-the-art results in link-prediction tasks. We describe a simple but novel extension to an existing tensor decomposition model to predict missing links using similarity among tensor slices, as opposed to an existing tensor decomposition models which assumes each slice to contribute equally in predicting links. Our extended model performs better than the original tensor decomposition and the non-negative tensor decomposition variant of it in an evaluation on several datasets.Item Modeling phase noise in high-power photodetectors(IEEE, 2019-08-22) Mahabadi, Seyed Ehsan Jamali; Carruthers, Thomas F.; Menyuk, CurtisWe describe the simulation model that we use to calculate the impulse response and phase noise in a modified unitraveling carrier (MUTC) photodetector using the drift-diffusion equations while avoiding computationally expensive Monte Carlo simulations.