Fault Diagnosis Method Based on An Improved KNN Algorithm for PV strings

Lina Wang; Hongcheng Qiu; Pu Yang; Jihong Gao

doi:10.1109/ACEEE51855.2021.9575060

Fault Diagnosis Method Based on An Improved KNN Algorithm for PV strings

Lina Wang
, Hongcheng Qiu
, Pu Yang
, Jihong Gao

School of Automation Science and Electrical Engineering

Beihang University
Ltd.

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

5 Scopus citations

Abstract

Rapid and accurate fault diagnosis under limited data has become one of the most important abilities of photovoltaic (PV) power generation systems. This study proposes an improved K-Nearest-Neighbor(KNN) method, which is based on the current at the maximum power point, voltage at the maximum power point and weather data. By using this method, short circuit, open circuit and shading of a PV string can be diagnosed quickly based on data obtained by inverters and weather monitor. Finally, a large number of data was obtained through a credible model whose data is proved to be consistent with the measured data greatly. Diagnosis result of the method was evaluated through these data.

Original language	English
Title of host publication	2021 4th Asia Conference on Energy and Electrical Engineering, ACEEE 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	91-98
Number of pages	8
ISBN (Electronic)	9781665418959
DOIs	https://doi.org/10.1109/ACEEE51855.2021.9575060
State	Published - 10 Sep 2021
Event	4th Asia Conference on Energy and Electrical Engineering, ACEEE 2021 - Virtual, Bangkok, Thailand Duration: 10 Sep 2021 → 12 Sep 2021

Publication series

Name	2021 4th Asia Conference on Energy and Electrical Engineering, ACEEE 2021

Conference

Conference	4th Asia Conference on Energy and Electrical Engineering, ACEEE 2021
Country/Territory	Thailand
City	Virtual, Bangkok
Period	10/09/21 → 12/09/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Fault diagnosis
K-Nearest-Neighbor
Photovoltaic
PV model
PV string

Access to Document

10.1109/ACEEE51855.2021.9575060

Cite this

Wang, L., Qiu, H., Yang, P., & Gao, J. (2021). Fault Diagnosis Method Based on An Improved KNN Algorithm for PV strings. In 2021 4th Asia Conference on Energy and Electrical Engineering, ACEEE 2021 (pp. 91-98). (2021 4th Asia Conference on Energy and Electrical Engineering, ACEEE 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACEEE51855.2021.9575060

@inproceedings{3c3b3b06a89f4a42ba64992adab75cb6,

title = "Fault Diagnosis Method Based on An Improved KNN Algorithm for PV strings",

abstract = "Rapid and accurate fault diagnosis under limited data has become one of the most important abilities of photovoltaic (PV) power generation systems. This study proposes an improved K-Nearest-Neighbor(KNN) method, which is based on the current at the maximum power point, voltage at the maximum power point and weather data. By using this method, short circuit, open circuit and shading of a PV string can be diagnosed quickly based on data obtained by inverters and weather monitor. Finally, a large number of data was obtained through a credible model whose data is proved to be consistent with the measured data greatly. Diagnosis result of the method was evaluated through these data.",

keywords = "Fault diagnosis, K-Nearest-Neighbor, Photovoltaic, PV model, PV string",

author = "Lina Wang and Hongcheng Qiu and Pu Yang and Jihong Gao",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 4th Asia Conference on Energy and Electrical Engineering, ACEEE 2021 ; Conference date: 10-09-2021 Through 12-09-2021",

year = "2021",

month = sep,

day = "10",

doi = "10.1109/ACEEE51855.2021.9575060",

language = "英语",

series = "2021 4th Asia Conference on Energy and Electrical Engineering, ACEEE 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "91--98",

booktitle = "2021 4th Asia Conference on Energy and Electrical Engineering, ACEEE 2021",

address = "美国",

}

Wang, L, Qiu, H, Yang, P & Gao, J 2021, Fault Diagnosis Method Based on An Improved KNN Algorithm for PV strings. in 2021 4th Asia Conference on Energy and Electrical Engineering, ACEEE 2021. 2021 4th Asia Conference on Energy and Electrical Engineering, ACEEE 2021, Institute of Electrical and Electronics Engineers Inc., pp. 91-98, 4th Asia Conference on Energy and Electrical Engineering, ACEEE 2021, Virtual, Bangkok, Thailand, 10/09/21. https://doi.org/10.1109/ACEEE51855.2021.9575060

Fault Diagnosis Method Based on An Improved KNN Algorithm for PV strings. / Wang, Lina; Qiu, Hongcheng; Yang, Pu et al.
2021 4th Asia Conference on Energy and Electrical Engineering, ACEEE 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 91-98 (2021 4th Asia Conference on Energy and Electrical Engineering, ACEEE 2021).

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

TY - GEN

T1 - Fault Diagnosis Method Based on An Improved KNN Algorithm for PV strings

AU - Wang, Lina

AU - Qiu, Hongcheng

AU - Yang, Pu

AU - Gao, Jihong

PY - 2021/9/10

Y1 - 2021/9/10

N2 - Rapid and accurate fault diagnosis under limited data has become one of the most important abilities of photovoltaic (PV) power generation systems. This study proposes an improved K-Nearest-Neighbor(KNN) method, which is based on the current at the maximum power point, voltage at the maximum power point and weather data. By using this method, short circuit, open circuit and shading of a PV string can be diagnosed quickly based on data obtained by inverters and weather monitor. Finally, a large number of data was obtained through a credible model whose data is proved to be consistent with the measured data greatly. Diagnosis result of the method was evaluated through these data.

AB - Rapid and accurate fault diagnosis under limited data has become one of the most important abilities of photovoltaic (PV) power generation systems. This study proposes an improved K-Nearest-Neighbor(KNN) method, which is based on the current at the maximum power point, voltage at the maximum power point and weather data. By using this method, short circuit, open circuit and shading of a PV string can be diagnosed quickly based on data obtained by inverters and weather monitor. Finally, a large number of data was obtained through a credible model whose data is proved to be consistent with the measured data greatly. Diagnosis result of the method was evaluated through these data.

KW - Fault diagnosis

KW - K-Nearest-Neighbor

KW - Photovoltaic

KW - PV model

KW - PV string

UR - https://www.scopus.com/pages/publications/85119293976

U2 - 10.1109/ACEEE51855.2021.9575060

DO - 10.1109/ACEEE51855.2021.9575060

M3 - 会议稿件

AN - SCOPUS:85119293976

T3 - 2021 4th Asia Conference on Energy and Electrical Engineering, ACEEE 2021

SP - 91

EP - 98

BT - 2021 4th Asia Conference on Energy and Electrical Engineering, ACEEE 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th Asia Conference on Energy and Electrical Engineering, ACEEE 2021

Y2 - 10 September 2021 through 12 September 2021

ER -

Fault Diagnosis Method Based on An Improved KNN Algorithm for PV strings

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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