Biblio
Filters: Author is Kobayashi, Koichi [Clear All Filters]
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2021. On Detection of False Data Injection Attacks in Distributed State Estimation of Power Networks. 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE). :472—473.
In power networks, it is important to detect a cyber attack. In this paper, we propose a detection method of false data injection (FDI) attacks. FDI attacks cannot be detected from the estimation error in power networks. The proposed method is based on the distributed state estimation, and is used the tentative estimated state. The proposed method is demonstrated by a numerical example on the IEEE 14-bus system.
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2021. Sensor Scheduling-Based Detection of False Data Injection Attacks in Power System State Estimation. 2021 IEEE International Conference on Consumer Electronics (ICCE). :1—4.
In state estimation of steady-state power networks, a cyber attack that cannot be detected from the residual (i.e., the estimation error) is called a false data injection attack. In this paper, to enforce security of power networks, we propose a method of detecting a false data injection attack. In the proposed method, a false data injection attack is detected by randomly choosing sensors used in state estimation. The effectiveness of the proposed method is presented by two numerical examples including the IEEE 14-bus system.
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2020. Stochastic Model Predictive Control of Energy Management Systems with Human in the Loop. 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE). :60–61.
In this paper, we propose a method of stochastic model predictive control for energy management systems including human-in-the-loop. Here, we consider an air-conditioning system consisting of some rooms. Human decision making about the set temperature is modeled by a discrete-time Markov chain. The finite-time optimal control problem solved in the controller is reduced to a mixed integer linear programming problem.