Virtual Inertia Adaptive Control of a Doubly Fed Induction Generator (DFIG) Wind Power System with Hydrogen Energy Storage


This paper presents a doubly fed induction generator (DFIG) wind power system with hydrogen energy storage, with a focus on its virtual inertia adaptive control. Conventionally, a synchronous generator has a large inertia from its rotating rotor, and thus its kinetic energy can be used to damp out fluctuations from the grid. However, DFIGs do not provide such a mechanism as their rotor is disconnected with the power grid, owing to the use of back-to-back power converters between the two. In this paper, a hydrogen energy storage system is utilized to provide a virtual inertia so as to dampen the disturbances and support the grid’s stability. An analytical model is developed based on experimental data and test results show that: (1) the proposed method is effective in supporting the grid frequency; (2) the maximum power point tracking is achieved by implementing this proposed system; and, (3) the DFIG efficiency is improved. The developed system is technically viable and can be applied to medium and large wind power systems. The hydrogen energy storage is a clean and environmental-friendly technology, and can increase the renewable energy penetration in the power network.

Publication DOI:
Divisions: College of Engineering & Physical Sciences
Additional Information: © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( Funding: National Key Research and Development Plan of China (2017YFB0903500), National Natural Science Foundation of China (51577163) and Key Research Project of State Grid Corporation of China (5230HQ16016U).
Uncontrolled Keywords: DFIGs,energy storage,virtual inertia adaptive control,wind power
Full Text Link: http://www.mdpi ... 6-1073/11/4/904
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PURE Output Type: Article
Published Date: 2018-04-12
Accepted Date: 2018-04-10
Authors: Yuan, Tiejiang
Wang, Jinjun
Guan, Yuhang
Liu, Zheng
Song, Xinfu
Che, Yong
Cao, Wenping (ORCID Profile 0000-0002-8133-3020)



Version: Published Version

License: Creative Commons Attribution

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