Fault Diagnosis in Microgrids with Integration of Solar Photovoltaic Systems: A Review

被引：11

作者：

Jadidi, Saeedreza ^{[1
]}

Badihi, Hamed ^{[1
,2
]}

Zhang, Youmin ^{[1
]}

机构：

[1] Concordia Univ, Dept Mech Ind & Aerosp Engn, Montreal, PQ, Canada

[2] Nanjing Univ Aeronaut & Astronaut NUAA, Coll Automat Engn, Nanjing, Jiangsu, Peoples R China

来源：

IFAC PAPERSONLINE | 2020年 / 53卷 / 02期

关键词：

microgrid; microgrid protection; fault detection and diagnosis; solar photovoltaic systems; DETECTION ALGORITHM; PERFORMANCE; STRATEGY; ARRAYS;

D O I：

10.1016/j.ifacol.2020.12.763

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Microgrids are essential components to help create the future electric grid which features a significant penetration of renewable and clean energy resources. However, a critical challenge in the protection of microgrids is the fault detection and diagnosis process, particularly in the presence of high uncertainties and varying topologies of microgrids. Faults in microgrids can cause instabilities, inefficient power generation, and other losses. Therefore, not only does it matter to understand various fault/failure modes and their root causes and effects, but it is also essential to develop real-time automated diagnosis tools to capture early signatures of fault evolution and enable proper mitigating actions. Given the significance of this issue, the present paper starts with a review of different failure modes occurring in various components of grid-connected photovoltaic systems, before offering a deeper review of the state of the art of fault diagnosis techniques specifically applied to solar photovoltaic systems in microgrids. Copyright (C) 2020 The Authors.

引用

页码：12091 / 12096

页数：6

共 42 条

[1] Fault detection in a grid-connected photovoltaic system using adaptive thresholding method [J].

Ammiche, Mustapha ;

Kouadri, Abdelmalek ;

Halabi, Laith M. ;

Guichi, Amar ;

Mekhilef, Saad .

SOLAR ENERGY, 2018, 174 :762-769

[2] AI-Driven Intelligent Fault Detection and Diagnosis in a Hybrid AC/DC Microgrid [J].

Badihi, Hamed ;

Jadidi, Saeedreza ;

Zhang, Youmin ;

Su, Chun-Yi ;

Xie, Wen-Fang .

2019 1ST INTERNATIONAL CONFERENCE ON INDUSTRIAL ARTIFICIAL INTELLIGENCE (IAI 2019), 2019,

[3] Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification [J].

Belaout, A. ;

Krim, E. ;

Mellit, A. ;

Talbi, B. ;

Arabi, A. .

RENEWABLE ENERGY, 2018, 127 :548-558

[4] Sensors fault diagnosis and fault tolerant control for grid connected PV system [J].

Ben Youssef, Fatma ;

Sbita, Lasaad .

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2017, 42 (13) :8962-8971

[5] Modeling and fault diagnosis of a photovoltaic system [J].

Chao, K. -H. ;

Ho, S-H. ;

Wang, M. -H. .

ELECTRIC POWER SYSTEMS RESEARCH, 2008, 78 (01) :97-105

[6]

Chen WQ, 2013, IEEE ENER CONV, P4559, DOI 10.1109/ECCE.2013.6647311

[7] Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics [J].

Chen, Zhicong ;

Wu, Lijun ;

Cheng, Shuying ;

Lin, Peijie ;

Wu, Yue ;

Lin, Wencheng .

APPLIED ENERGY, 2017, 204 :912-931

[8] A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks [J].

Chine, W. ;

Mellit, A. ;

Lughi, V. ;

Malek, A. ;

Sulligoi, G. ;

Pavan, A. Massi .

RENEWABLE ENERGY, 2016, 90 :501-512

[9] Fault detection method for grid-connected photovoltaic plants [J].

Chine, W. ;

Mellit, A. ;

Pavan, A. Massi ;

Kalogirou, S. A. .

RENEWABLE ENERGY, 2014, 66 :99-110

[10] Metaheuristic optimization based fault diagnosis strategy for solar photovoltaic systems under non-uniform irradiance [J].

Das, Saborni ;

Hazra, Abhik ;

Basu, Mousumi .

RENEWABLE ENERGY, 2018, 118 :452-467

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