Line-line fault detection and classification for photovoltaic systems using ensemble learning model based on I-V characteristics

被引:63
作者
Eskandari, Aref [1 ]
Milimonfared, Jafar [1 ]
Aghaei, Mohammadreza [2 ,3 ]
机构
[1] Amirkabir Univ Technol, Dept Elect Engn, Tehran, Iran
[2] Eindhoven Univ Technol, Dept Mech Engn, Eindhoven, Netherlands
[3] Univ Freiburg, Solar Energy Engn, Dept Microsyst Engn IMTEK, Freiburg, Germany
关键词
Ensemble learning model; Fault detection and classification; Feature selection; Line-Line faults; Machine learning; Photovoltaic monitoring; MULTIRESOLUTION SIGNAL DECOMPOSITION; PROTECTION CHALLENGES; FEATURE-SELECTION; DIAGNOSIS; ALGORITHM;
D O I
10.1016/j.solener.2020.09.071
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The fault diagnosis of photovoltaic (PV) arrays aims to increase the reliability and service life of PV systems. Line Line (LL) faults may remain undetected under low mismatch level and high impedance due to low currents faults, resulting in power losses and fire potential disaster. This paper proposes a novel and intelligent fault diagnosis method based on an ensemble learning model and Current-Voltage (I-V) characteristics to detect and classify LL faults at the DC side of PV systems. For this purpose, first, the key features are extracted via analyzing I-V characteristics under various LL fault events and normal operation. Second, a feature selection algorithm has been applied to select the best features for each learning algorithm in order to reduce the amount of data required for the learning process. Third, an ensemble learning model is developed that combines several learning algorithms based on the probabilistic strategy to achieve superior diagnostic performance. Here, we find an excellent agreement between simulation and experimental results that the proposed method can obtain higher accuracy in detecting and classifying the LL faults, even under low mismatch levels and high fault impedances. addition, the comparison results demonstrate that the performance of the proposed method is better than individual machine learning algorithms, so that the proposed method precisely detects and classifies LL faults on PV systems under the different conditions with an average accuracy of 99% and 99.5%, respectively.
引用
收藏
页码:354 / 365
页数:12
相关论文
共 37 条
  • [1] Aghaei M., 2020, P 37 EUR PHOT SOL EN
  • [2] Aghaei M., 2015, POWER ENG ADV CHAL B, P56
  • [3] Innovative Automated Control System for PV Fields Inspection and Remote Control
    Aghaei, Mohammadreza
    Grimaccia, Francesco
    Gonano, Carlo A.
    Leva, Sonia
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2015, 62 (11) : 7287 - 7296
  • [4] Modeling and Health Monitoring of DC Side of Photovoltaic Array
    Akram, Mohd Nafis
    Lotfifard, Saeed
    [J]. IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2015, 6 (04) : 1245 - 1253
  • [5] A Comprehensive Review of Catastrophic Faults in PV Arrays: Types, Detection, and Mitigation Techniques
    Alam, Mohammed Khorshed
    Khan, Faisal
    Johnson, Jay
    Flicker, Jack
    [J]. IEEE JOURNAL OF PHOTOVOLTAICS, 2015, 5 (03): : 982 - 997
  • [6] Atsu D., 2020, RENEW ENERGY
  • [7] Fault detection and diagnosis based on C4.5 decision tree algorithm for grid connected PV system
    Benkercha, Rabah
    Moulahoum, Samir
    [J]. SOLAR ENERGY, 2018, 173 : 610 - 634
  • [8] Feature selection in machine learning: A new perspective
    Cai, Jie
    Luo, Jiawei
    Wang, Shulin
    Yang, Sheng
    [J]. NEUROCOMPUTING, 2018, 300 : 70 - 79
  • [9] A survey on feature selection methods
    Chandrashekar, Girish
    Sahin, Ferat
    [J]. COMPUTERS & ELECTRICAL ENGINEERING, 2014, 40 (01) : 16 - 28
  • [10] Quickest Fault Detection in Photovoltaic Systems
    Chen, Leian
    Li, Shang
    Wang, Xiaodong
    [J]. IEEE TRANSACTIONS ON SMART GRID, 2018, 9 (03) : 1835 - 1847