- Title
- An extreme learning machine based adaptive VISMA for stability enhancement of renewable rich power systems
- Creator
- Setiadi, Herlambang; Shah, , Rakibuzzaman; Islam, Md Rabiul; Asfani, Dimas; Nasution, Tigor; Abdillah, Muhammad; Megantoro, Prisma; Krismanto, Awan
- Date
- 2022
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/185239
- Identifier
- vital:16641
- Identifier
-
https://doi.org/10.3390/electronics11020247
- Identifier
- ISBN:2079-9292 (ISSN)
- Abstract
- Maintaining power system stability in renewable-rich power systems can be a challenging task. Generally, the renewable-rich power systems suffer from low and no inertia due to the integration of power electronics devices in renewable-based power plants. Power system oscillatory stability can also be affected due to the low and no inertia. To overcome this problem, additional devices that can emulate inertia without adding synchronous machines can be used. These devices are referred to as virtual synchronous machines (VISMA). In this paper, the enhancement of oscillatory stability of a realistic representative power system using VISMA is proposed. A battery energy storage system (BESS) is used as the VISMA by adding an additional controller to emulate the inertia. The VISMA is designed by using Fruit Fly Optimization. Moreover, to handle the uncertainty of renewable-based power plants, the VISMA parameters are designed to be adaptive using the extreme learning machine method. Java Indonesian Power Grid has been used as the test system to investigate the efficacy of the proposed method against the conventional POD method and VISMA tuning using other methods. The simulation results show that the proposed method can enhance the oscillatory stability of the power system under various operating conditions. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
- Publisher
- MDPI
- Relation
- Electronics (Switzerland) Vol. 11, no. 2 (2022), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2022 by the authors
- Rights
- Open Access
- Subject
- 4009 Electronics, sensors and digital hardware; Clean energy technology; Extreme learning machine; Fruit fly optimization; Photovoltaic; Renewable energy; Virtual synchronous machine (VISMA); Wind power plant
- Full Text
- Reviewed
- Funder
- This research is funded by Universitas Airlangga, Institut Teknologi Sepuluh Nopember and Universitas Sumatra Utara for funding this research through Program Penelitian Kolaborasi Indonesia (PPKI) grant (grant number: 170/UN3.15/PT/2021).
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