NDLI: Markov chain-based wind power time series modelling method considering the influence of the state duration on the state transition probability

Content Provider	IET Digital Library
Author	Zhu, Chenxi Zhang, Yan Yan, Zheng Zhu, Jinzhou
Abstract	Due to the inherent uncertainties of wind power, its large-scale integration strongly impacts the planning and operation of power systems. To investigate these impacts, a stochastic model is required to more accurately capture the wind power's characteristics. This study proposes an improved Markov chain (MC)-based time series (TS) modelling method for the stochastic generation of synthetic wind power TS. First, a self-adaptive state division strategy is proposed to objectively classify historical data into several typical states. This strategy combines a state optimisation clustering model with a random-variable-modelling-oriented filter parameter optimisation method. Then, a three-dimensional state transition probability matrix (STPM) is proposed and constructed to generate synthetic wind power state TS. In contrast to the previous STPMs, the proposed STPM can capture the changing pattern of the transition probability against the state duration. Finally, the fluctuation quantity and noise are separately and sequentially added to the generated state TS, as an improvement over previous fluctuation characteristic addition methods, to obtain the final synthetic wind power TS. The results show that the proposed method outperforms previous MC-based TS modelling methods in reproducing historical characteristics, such as the transition and fluctuation characteristics, and does not increase the STPM construction algorithm's time complexity.
Starting Page	2051
Ending Page	2061
Page Count	11
ISSN	17521416
Volume Number	13
e-ISSN	17521424
Issue Number	Issue 12, Sep (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rpg/13/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2019.0064
Journal	IET Renewable Power Generation
Publisher Date	2019-05-22
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Final Synthetic Wind Power TS Fluctuation Characteristic Addition Method Improved Markov Chain-based Time Series Modelling Method Markov Chain-based Wind Power Time Series Modelling Method Markov Processes Matrix Algebra MC-based TS Optimisation Optimisation Technique Power System Power System Economics Power System Managemen Power System Operation Power System Planning Power System Planning And Layout Power System State Estimation Random-variable-modelling-oriented Filter Parameter Optimisation Method Self-adaptive State Division Strategy STPM Construction Algorithm Synthetic Wind Power State TS Three-dimensional State Transition Probability Matrix Time Series Wind Power Plant
Content Type	Text
Resource Type	Article
Subject	Renewable Energy, Sustainability and the Environment

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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