Markov Jump Linear System Analysis Of Microgrid Stability
In a typical microgrid, the power generation capacity is similar to the maximum total load. The low inertia of the system provides little margin for error in the power balance, both active and reactive, and requires rapid control response to load changes. In the present work, a microgrid is modeled as a Markov jump linear system (MJLS). An MJLS is a dynamic system with continuous states governed by one of a set of linear systems, and a continuous-time Markov process that determines which linear system is active. When the discrete state of the Markov process changes, there is a 'jump' in the dynamics of the continuous states. In addition, the jump may be impulsive. The present work first explores impulsive MJLS stability. Conservative bounds on the expected value of the state are determined from a combination of the Markov process parameters, the dynamics of each linear system, and the magnitude of the impulses. Then the microgrid model is cast into the MJLS framework and stability analysis is performed. The conclusions are verified with detailed simulations.
M. Rasheduzzaman et al., "Markov Jump Linear System Analysis Of Microgrid Stability," Proceedings of the American Control Conference (2014, Portland, OR), pp. 5062-5066, Institute of Electrical and Electronics Engineers (IEEE), Jun 2014.
The definitive version is available at https://doi.org/10.1109/ACC.2014.6859040
American Control Conference (2014: Jun. 4-6, Portland, OR)
Electrical and Computer Engineering
National Science Foundation (U.S.)
Keywords and Phrases
Electric Power Distribution; Linear Systems; Almost Sure Stability; Continuous-Time Markov Process; Markov Jump Linear Systems; Micro Grid; Microgrid Modeling; Microgrid Stability; Power Generation Capacities; Stability Analysis; Markov Processes; Markov Jump Linear System; Microgrid; Microgrids; Stability Analysis; Switches; Power System Stability; Markov Processes; Linear Systems; Eigenvalues And Eigenfunctions; Power System Dynamic Stability; Continuous Time Systems; Discrete Time Systems; Distributed Power Generation; Linear Systems; Power Generation Control
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Article - Conference proceedings
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01 Jun 2014