Fault Prediction and Classification for a Doubly-Fed Induction Generator based Wind Turbine by using Random Forest Classifier

Shuo Zhang; Malabika Basu; Emma Robinson; Breiffni Fitzgerald; Biswajit Basu

doi:10.1049/icp.2021.1353

Fault Prediction and Classification for a Doubly-Fed Induction Generator based Wind Turbine by using Random Forest Classifier

Shuo Zhang, Malabika Basu, Emma Robinson, Breiffni Fitzgerald, Biswajit Basu

School of Electrical and Electronic Engineering

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

Abstract

A detailed holistic doubly fed induction generator (DFIG) based wind turbine model is developed by interfacing FAST with Simulink. The effects of power converter faults on the mechanical systems are investigated and the collected simulation dataset is then evaluated under fault-free, and different faulty, scenarios. Then, this paper considers Random Forest Classifier as an efficient faulty prognosis through examination of the dataset. This method allows power converter faults to be predicted, and classified, in advance of their occurrences.

Original language	English
Title of host publication	IET Conference Proceedings
Publisher	Institution of Engineering and Technology
Pages	149-154
Number of pages	6
Volume	2021
Edition	2
ISBN (Electronic)	9781839534300, 9781839535048, 9781839535741, 9781839535918, 9781839536045, 9781839536052, 9781839536069, 9781839536199, 9781839536366, 9781839536588, 9781839536793, 9781839536809, 9781839536816, 9781839536847, 9781839537035
DOIs	https://doi.org/10.1049/icp.2021.1353
Publication status	Published - 2021
Event	9th Renewable Power Generation Conference, RPG Dublin Online 2021 - Dublin, Virtual, Ireland Duration: 1 Mar 2021 → 2 Mar 2021

Conference

Conference	9th Renewable Power Generation Conference, RPG Dublin Online 2021
Country/Territory	Ireland
City	Dublin, Virtual
Period	1/03/21 → 2/03/21

Keywords

DFIG
FAST
POWER CONVERTER FAULTS
RANDOM FOREST CLASSIFIER

Access to Document

10.1049/icp.2021.1353

Cite this

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title = "Fault Prediction and Classification for a Doubly-Fed Induction Generator based Wind Turbine by using Random Forest Classifier",

abstract = "A detailed holistic doubly fed induction generator (DFIG) based wind turbine model is developed by interfacing FAST with Simulink. The effects of power converter faults on the mechanical systems are investigated and the collected simulation dataset is then evaluated under fault-free, and different faulty, scenarios. Then, this paper considers Random Forest Classifier as an efficient faulty prognosis through examination of the dataset. This method allows power converter faults to be predicted, and classified, in advance of their occurrences.",

keywords = "DFIG, FAST, POWER CONVERTER FAULTS, RANDOM FOREST CLASSIFIER",

author = "Shuo Zhang and Malabika Basu and Emma Robinson and Breiffni Fitzgerald and Biswajit Basu",

note = "Publisher Copyright: {\textcopyright} 2021 The Institution of Engineering and Technology.; 9th Renewable Power Generation Conference, RPG Dublin Online 2021 ; Conference date: 01-03-2021 Through 02-03-2021",

year = "2021",

doi = "10.1049/icp.2021.1353",

language = "English",

volume = "2021",

pages = "149--154",

booktitle = "IET Conference Proceedings",

publisher = "Institution of Engineering and Technology",

address = "United Kingdom",

edition = "2",

}

Zhang, S, Basu, M, Robinson, E, Fitzgerald, B & Basu, B 2021, Fault Prediction and Classification for a Doubly-Fed Induction Generator based Wind Turbine by using Random Forest Classifier. in IET Conference Proceedings. 2 edn, vol. 2021, Institution of Engineering and Technology, pp. 149-154, 9th Renewable Power Generation Conference, RPG Dublin Online 2021, Dublin, Virtual, Ireland, 1/03/21. https://doi.org/10.1049/icp.2021.1353

Fault Prediction and Classification for a Doubly-Fed Induction Generator based Wind Turbine by using Random Forest Classifier. / Zhang, Shuo; Basu, Malabika; Robinson, Emma et al.
IET Conference Proceedings. Vol. 2021 2. ed. Institution of Engineering and Technology, 2021. p. 149-154.

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

TY - GEN

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AU - Basu, Malabika

AU - Robinson, Emma

AU - Fitzgerald, Breiffni

AU - Basu, Biswajit

PY - 2021

Y1 - 2021

N2 - A detailed holistic doubly fed induction generator (DFIG) based wind turbine model is developed by interfacing FAST with Simulink. The effects of power converter faults on the mechanical systems are investigated and the collected simulation dataset is then evaluated under fault-free, and different faulty, scenarios. Then, this paper considers Random Forest Classifier as an efficient faulty prognosis through examination of the dataset. This method allows power converter faults to be predicted, and classified, in advance of their occurrences.

AB - A detailed holistic doubly fed induction generator (DFIG) based wind turbine model is developed by interfacing FAST with Simulink. The effects of power converter faults on the mechanical systems are investigated and the collected simulation dataset is then evaluated under fault-free, and different faulty, scenarios. Then, this paper considers Random Forest Classifier as an efficient faulty prognosis through examination of the dataset. This method allows power converter faults to be predicted, and classified, in advance of their occurrences.

KW - DFIG

KW - FAST

KW - POWER CONVERTER FAULTS

KW - RANDOM FOREST CLASSIFIER

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U2 - 10.1049/icp.2021.1353

DO - 10.1049/icp.2021.1353

M3 - Conference contribution

AN - SCOPUS:85174653991

VL - 2021

SP - 149

EP - 154

BT - IET Conference Proceedings

PB - Institution of Engineering and Technology

T2 - 9th Renewable Power Generation Conference, RPG Dublin Online 2021

Y2 - 1 March 2021 through 2 March 2021

ER -

Fault Prediction and Classification for a Doubly-Fed Induction Generator based Wind Turbine by using Random Forest Classifier

Abstract

Conference

Keywords

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Cite this