Partial shading fault diagnosis in PV system with discrete wavelet transform (DWT)

Abstract

The homeowners PV system want to maximize the total energy harvesting from their installed resident PV system (RPS), especially if there is a feed-in tariff (FIT) in play. In this regard, fault detection in solar photovoltaic (PV) arrays allows to expedite PV system recovery time. On the other hand, one of the most common fault in RPS is partial shading and fault diagnosis effort and interpreting this type of fault is motivated us to propose this paper. I-V curve of shaded string included notched and steeped points which are related to signature of shaded cells. In fact, the value of the vertical height in I-V curve represented of percentage of shading. Likewise, the horizontal or voltage distance from Voc of the notched point is related to the number of cells strings within modules those have been bypassed by diodes (Number of bypassed modules). In this work, for first time discrete wavelet transform (dwt) is used to interpret traced I-V curve of PV system and defining these coordinated points in related diagnosis effort.

Monitoring, Diagnosis, and Power Forecasting for Photovoltaic Fields: A Review

“…All the techniques listed so far are based on the comparison of the instantaneous power with a set yield target. An alternative method, which can be found in [39], proposes plotting the whole -curve of a single string using information from the inverter. Indeed, the inverter control needs to measure instantaneous voltage and current in order to track the maximum power point.…”

Deep Convolutional Neural Network for Automatic Detection of Damaged Photovoltaic Cells

“…The detection of anomalies is done by collecting and analysing each subsystems data (Daliento et al, 2017). Another possibility in monitoring and diagnosis is based on electrical parameters such as Performance Ratio considering comparison between arrays (Bizzarri et al, 2015), or strings by comparing currents (Baba et al, 2013), inferential tool, returning information about the operation of the PV field (Cristaldi et al, 2015), dataset of observed string currents and voltages and their respective low-pass-filtered time derivatives (Ben-Menahem and Yang, 2012), or I-V curve of a single string using information from the inverter (Davarifar et al, 2014), etc. A common problem of all these monitoring techniques is the large amount of data to be analyzed.…”

Mismatch Based Diagnosis of PV Fields Relying on Monitored String Currents

“…The optimal width of the confidence interval in terms of is still debated; as an example, in [10] the ineffectiveness of the 3 rule, usually adopted to recognize outliers strings, was evidenced. Alternative methods were proposed in [11,12], where the plot of the whole I-V curve of individual strings was used to recognize six categories of faults, including shadow effects and bypass diode fault.…”