TY - JOUR
T1 - Statistical fault detection in photovoltaic systems
AU - Garoudja, Elyes
AU - Harrou, Fouzi
AU - Sun, Ying
AU - Kara, Kamel
AU - Chouder, Aissa
AU - Silvestre, Santiago
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OSR-2015-CRG4-2582
Acknowledgements: We would like to thank the reviewers of this article for their insightful comments, which helped us to greatly improve its quality. This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582. The authors (Elyes Garoudja and Kamel Kara) thank the SET Laboratory, Department of Electronics, Faculty of Technology, University of Blida 1, Algeria, for continuous support during the study.
PY - 2017/5/8
Y1 - 2017/5/8
N2 - Faults in photovoltaic (PV) systems, which can result in energy loss, system shutdown or even serious safety breaches, are often difficult to avoid. Fault detection in such systems is imperative to improve their reliability, productivity, safety and efficiency. Here, an innovative model-based fault-detection approach for early detection of shading of PV modules and faults on the direct current (DC) side of PV systems is proposed. This approach combines the flexibility, and simplicity of a one-diode model with the extended capacity of an exponentially weighted moving average (EWMA) control chart to detect incipient changes in a PV system. The one-diode model, which is easily calibrated due to its limited calibration parameters, is used to predict the healthy PV array's maximum power coordinates of current, voltage and power using measured temperatures and irradiances. Residuals, which capture the difference between the measurements and the predictions of the one-diode model, are generated and used as fault indicators. Then, the EWMA monitoring chart is applied on the uncorrelated residuals obtained from the one-diode model to detect and identify the type of fault. Actual data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria, are used to assess the performance of the proposed approach. Results show that the proposed approach successfully monitors the DC side of PV systems and detects temporary shading.
AB - Faults in photovoltaic (PV) systems, which can result in energy loss, system shutdown or even serious safety breaches, are often difficult to avoid. Fault detection in such systems is imperative to improve their reliability, productivity, safety and efficiency. Here, an innovative model-based fault-detection approach for early detection of shading of PV modules and faults on the direct current (DC) side of PV systems is proposed. This approach combines the flexibility, and simplicity of a one-diode model with the extended capacity of an exponentially weighted moving average (EWMA) control chart to detect incipient changes in a PV system. The one-diode model, which is easily calibrated due to its limited calibration parameters, is used to predict the healthy PV array's maximum power coordinates of current, voltage and power using measured temperatures and irradiances. Residuals, which capture the difference between the measurements and the predictions of the one-diode model, are generated and used as fault indicators. Then, the EWMA monitoring chart is applied on the uncorrelated residuals obtained from the one-diode model to detect and identify the type of fault. Actual data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria, are used to assess the performance of the proposed approach. Results show that the proposed approach successfully monitors the DC side of PV systems and detects temporary shading.
UR - http://hdl.handle.net/10754/625023
UR - http://www.sciencedirect.com/science/article/pii/S0038092X17303377
UR - http://www.scopus.com/inward/record.url?scp=85018445854&partnerID=8YFLogxK
U2 - 10.1016/j.solener.2017.04.043
DO - 10.1016/j.solener.2017.04.043
M3 - Article
SN - 0038-092X
VL - 150
SP - 485
EP - 499
JO - Solar Energy
JF - Solar Energy
ER -