Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 52, Issue 9, Pages 9943-9948Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2021.3062672
Keywords
Power system stability; Markov processes; Hidden Markov models; Control systems; Circuit faults; Stability criteria; Linear matrix inequalities; normalsize Asynchronous control; hidden Markov model; linear matrix inequality (LMI); power systems
Categories
Funding
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2016R1A6A1A03013567, NRF-2018R1A2A2A14023632]
- Korea Institute of Energy Technology Evaluation and Planning (KETEP)
- Ministry of Trade, Industry, and Energy (MOTIE) of the Republic of Korea [20194030202300]
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This article studies the stabilization problem of discrete-time power systems subject to random abrupt changes using asynchronous control. A hidden Markov model technique is used to characterize the nonsynchronization between the control and system, and sufficient conditions are obtained by constructing mode-dependent stochastic Lyapunov function, ensuring the stochastic stability of the resulting hidden Markov jump systems and the existence of the desired control. Simulation examples show the efficiency of the designed control law.
In this article, the stabilization problem of discrete-time power systems subject to random abrupt changes is studied via asynchronous control. In this regard, the transient faults in the power lines, and subsequent switching of associated circuit breakers are modeled as a Markov chain. Based on this, the power systems are described as discrete-time Markov jump systems. The focus is mainly to design the control for Markov jump-based power systems (MJPSs) when modes of the control asynchronously run with the modes of power systems. To do this, a hidden Markov model technique is used to characterize the nonsynchronization between the control and system. By constructing the mode-dependent stochastic Lyapunov function, the sufficient conditions are acquired in the form of linear matrix inequalities (LMIs), which ensure not only the stochastic stability of the resulting hidden MJPSs but also the existence of the desired control. Finally, the simulation example reveals the efficiency of the designed control law.
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