Statistical fault detection in photovoltaic systems 1

Faults in photovoltaic (PV) systems, which can result in energy loss, system shutdown or even 13 serious safety breaches, are often difficult to avoid. Fault detection in such systems is imperative 14 to improve their reliability, productivity, safety and efficiency. Here, an innovative model-based 15 fault-detection approach for early detection of shading of PV modules and faults on the direct 16 current (DC) side of PV systems is proposed. This approach combines the flexibility, and simplicity 17 of a one-diode model with the extended capacity of an exponentially weighted moving average 18 (EWMA) control chart to detect incipient changes in a PV system. The one-diode model, which is 19 easily calibrated due to its limited calibration parameters, is used to predict the healthy PV array’s 20 maximum power coordinates of current, voltage and power using measured temperatures and 21 irradiances. Residuals, which capture the difference between the measurements and the 22 predictions of the one-diode model, are generated and used as fault indicators. Then, the EWMA 23 monitoring chart is applied on the uncorrelated residuals obtained from the one-diode model to 24 detect and identify the type of fault. Actual data from the grid-connected PV system installed at 25 the Renewable Energy Development Center, Algeria, are used to assess the performance of the 26 proposed approach. Results show that the proposed approach successfully monitors the DC side of 27 PV systems and detects temporary shading. 28