Distributed generator with virtual inertia using intelligent controller for grid-connected microgrid

Faa Jeng Lin; Kuang Hsiung Tan; Cheng Ming Shih

doi:10.1109/FUZZ48607.2020.9177857

Distributed generator with virtual inertia using intelligent controller for grid-connected microgrid

Faa Jeng Lin, Kuang Hsiung Tan, Cheng Ming Shih

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

A grid-connected microgrid with virtual inertia using master/slave control is proposed to overcome the drawback of conventionally inverter-based distributed generators (DGs) for lacking of inertia. In this study, the microgrid consists of a storage system, a photovoltaic (PV) system and a three-phase resistive load. The storage system and PV system are considered as the master unit and the slave unit respectively in the microgrid. Moreover, since the grid codes demand the DGs to have the low-voltage ride through (LVRT) ability to help sustaining the stability of power system especially during the grid faults, the reactive power control should respond immediately during the grid faults according to the grid codes. Thus, for the purpose of improving the reactive power control in grid-connected microgrid and the transient response of microgrid during the switching between the grid-connected mode and islanding mode, an online trained recurrent wavelet fuzzy neural network (RWFNN) is proposed to replace the conventional proportional-integral (PI) controller in the storage system. Excellent control performance of the grid-connected microgrid with virtual inertia using the proposed intelligent RWFNN controller can be verified via some simulation results.

Original language	English
Title of host publication	2020 IEEE International Conference on Fuzzy Systems, FUZZ 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728169323
DOIs	https://doi.org/10.1109/FUZZ48607.2020.9177857
State	Published - Jul 2020
Event	2020 IEEE International Conference on Fuzzy Systems, FUZZ 2020 - Glasgow, United Kingdom Duration: 19 Jul 2020 → 24 Jul 2020

Publication series

Name	IEEE International Conference on Fuzzy Systems
Volume	2020-July
ISSN (Print)	1098-7584

Conference

Conference	2020 IEEE International Conference on Fuzzy Systems, FUZZ 2020
Country/Territory	United Kingdom
City	Glasgow
Period	19/07/20 → 24/07/20

Keywords

Fuzzy neural network
Low-voltage ride through
Microgrid
Virtual inertia
Wavelet function

Access to Document

10.1109/FUZZ48607.2020.9177857

Research of High-Performance Operation and Power Conditiong in Ac Microgrid( II )
Lin, F.-J. (PI)
1/01/20 → 31/03/21
Project: Research

Cite this

Lin, F. J., Tan, K. H., & Shih, C. M. (2020). Distributed generator with virtual inertia using intelligent controller for grid-connected microgrid. In 2020 IEEE International Conference on Fuzzy Systems, FUZZ 2020 - Proceedings Article 9177857 (IEEE International Conference on Fuzzy Systems; Vol. 2020-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FUZZ48607.2020.9177857

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abstract = "A grid-connected microgrid with virtual inertia using master/slave control is proposed to overcome the drawback of conventionally inverter-based distributed generators (DGs) for lacking of inertia. In this study, the microgrid consists of a storage system, a photovoltaic (PV) system and a three-phase resistive load. The storage system and PV system are considered as the master unit and the slave unit respectively in the microgrid. Moreover, since the grid codes demand the DGs to have the low-voltage ride through (LVRT) ability to help sustaining the stability of power system especially during the grid faults, the reactive power control should respond immediately during the grid faults according to the grid codes. Thus, for the purpose of improving the reactive power control in grid-connected microgrid and the transient response of microgrid during the switching between the grid-connected mode and islanding mode, an online trained recurrent wavelet fuzzy neural network (RWFNN) is proposed to replace the conventional proportional-integral (PI) controller in the storage system. Excellent control performance of the grid-connected microgrid with virtual inertia using the proposed intelligent RWFNN controller can be verified via some simulation results.",

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Lin, FJ, Tan, KH & Shih, CM 2020, Distributed generator with virtual inertia using intelligent controller for grid-connected microgrid. in 2020 IEEE International Conference on Fuzzy Systems, FUZZ 2020 - Proceedings., 9177857, IEEE International Conference on Fuzzy Systems, vol. 2020-July, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE International Conference on Fuzzy Systems, FUZZ 2020, Glasgow, United Kingdom, 19/07/20. https://doi.org/10.1109/FUZZ48607.2020.9177857

Distributed generator with virtual inertia using intelligent controller for grid-connected microgrid. / Lin, Faa Jeng; Tan, Kuang Hsiung; Shih, Cheng Ming.
2020 IEEE International Conference on Fuzzy Systems, FUZZ 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9177857 (IEEE International Conference on Fuzzy Systems; Vol. 2020-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - A grid-connected microgrid with virtual inertia using master/slave control is proposed to overcome the drawback of conventionally inverter-based distributed generators (DGs) for lacking of inertia. In this study, the microgrid consists of a storage system, a photovoltaic (PV) system and a three-phase resistive load. The storage system and PV system are considered as the master unit and the slave unit respectively in the microgrid. Moreover, since the grid codes demand the DGs to have the low-voltage ride through (LVRT) ability to help sustaining the stability of power system especially during the grid faults, the reactive power control should respond immediately during the grid faults according to the grid codes. Thus, for the purpose of improving the reactive power control in grid-connected microgrid and the transient response of microgrid during the switching between the grid-connected mode and islanding mode, an online trained recurrent wavelet fuzzy neural network (RWFNN) is proposed to replace the conventional proportional-integral (PI) controller in the storage system. Excellent control performance of the grid-connected microgrid with virtual inertia using the proposed intelligent RWFNN controller can be verified via some simulation results.

AB - A grid-connected microgrid with virtual inertia using master/slave control is proposed to overcome the drawback of conventionally inverter-based distributed generators (DGs) for lacking of inertia. In this study, the microgrid consists of a storage system, a photovoltaic (PV) system and a three-phase resistive load. The storage system and PV system are considered as the master unit and the slave unit respectively in the microgrid. Moreover, since the grid codes demand the DGs to have the low-voltage ride through (LVRT) ability to help sustaining the stability of power system especially during the grid faults, the reactive power control should respond immediately during the grid faults according to the grid codes. Thus, for the purpose of improving the reactive power control in grid-connected microgrid and the transient response of microgrid during the switching between the grid-connected mode and islanding mode, an online trained recurrent wavelet fuzzy neural network (RWFNN) is proposed to replace the conventional proportional-integral (PI) controller in the storage system. Excellent control performance of the grid-connected microgrid with virtual inertia using the proposed intelligent RWFNN controller can be verified via some simulation results.

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Distributed generator with virtual inertia using intelligent controller for grid-connected microgrid

Abstract

Publication series

Conference

Keywords

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Projects

Research of High-Performance Operation and Power Conditiong in Ac Microgrid( II )

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