A fault diagnosis method for photovoltaic arrays based on fault parameters identification

Li, Yuanliang; Ding, Kun; Zhang, Jingwei; Chen, Fudong; Chen, Xiang; Wu, Jiabing

doi:10.1016/j.renene.2019.04.147

Cited by 49 publications

(25 citation statements)

References 26 publications

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“…The continuous lines in the Figures 6-10 are the estimated SDM parameters provided by ANN; they refer to the healthy conditions and put into evidence the intrinsic relationships among SDM parameters with the environmental conditions for the PV panel under test. It is worth noting that, apart from I ph , which is almost linear with G and practically insensitive with respect to the temperature, the behavior of the remaining parameters is completely different from the cases analyzed in [26,30]. This result is not very surprising, given that the relationships among parameters and the environmental conditions change significantly from panel to panel.…”

Section: Ann Identification Results For Healthy Conditionsmentioning

confidence: 77%

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Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network

2021

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In this paper, an artificial neural network (ANN) is used for isolating faults and degradation phenomena occurring in photovoltaic (PV) panels. In the literature, it is well known that the values of the single diode model (SDM) associated to the PV source are strictly related to degradation phenomena and their variation is an indicator of panel degradation. On the other hand, the values of parameters that allow to identify the degraded conditions are not known a priori because they can be different from panel to panel and are strongly dependent on environmental conditions, PV technology and the manufacturing process. For these reasons, to correctly detect the presence of degradation, the effect of environmental conditions and fabrication processes must be properly filtered out. The approach proposed in this paper exploits the intrinsic capability of ANN to map in its architecture two effects: (1) the non-linear relations existing among the SDM parameters and the environmental conditions, and (2) the effect of the degradation phenomena on the I−V curves and, consequently, on the SDM parameters. The ANN architecture is composed of two stages that are trained separately: one for predicting the SDM parameters under the hypothesis of healthy operation and the other one for degraded condition. The variation of each parameter, calculated as the difference of the output of the two ANN stages, will give a direct identification of the type of degradation that is occurring on the PV panel. The method was initially tested by using the experimental I−V curves provided by the NREL database, where the degradation was introduced artificially, later tested by using some degraded experimental I−V curves.

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Section: Ann Identification Results For Healthy Conditionsmentioning

confidence: 77%

“…Some examples are reported in [29]. In [30], the authors highlight that the fitting curve, which maps each SDM parameter as a function of the environmental condition, also varies along the seasons, due to the influence of the variable weather and environmental conditions.…”

Section: Description Of the Proposed Ann Architecturementioning

confidence: 99%

Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network

2021

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“…The current-voltage characteristics (I-V curves) measured from a faulty photovoltaic (PV) module or array (from now on, termed as faulty I-V curve) contain valuable information on the health status (Fadhel et al, 2019;Li et al, 2019). For a single module or small-scale PV string, one I-V tracer device can measure I-V curves under various meteorological conditions (Sarikh et al, 2020;Zhu and Xiao, 2020).…”

Section: Introductionmentioning

confidence: 99%

Evaluation and improvement of IEC 60891 correction methods for I-V curves of defective photovoltaic panels

Migan‐Dubois

Delpha

2021

Solar Energy

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“…The second category is the diagnosis methods based on monitoring the current-voltage (I-V) curves of the PV array via the inverters or other DC-DC converters [10][11][12][13][14][15][16][17][18]. In [10], the kernelbased extreme learning machine is used to build the fault diagnosis model and to extract fault information from measured I-V curves.…”

Section: Introductionmentioning

confidence: 99%

“…In [17], the features of I – V curves under different partial shadings are extracted and analysed by the principal component analysis for fault classification. In [18], the I – V curves of PV array are measured and the fault parameters are extracted by optimising the fault simulation model. The measured I – V curves contain more comprehensive information to indicate the operating status of the PV array and can be used to diagnose some common faults or abnormalities, e.g.…”

Section: Introductionmentioning

confidence: 99%

Fault detection of photovoltaic array based on Grubbs criterion and local outlier factor

Ding

Zhang

Ding

et al. 2020

IET Renewable Power Generation

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Aiming at improving the effectiveness and real-time performance of the fault detection of the photovoltaic (PV) array under different outdoor conditions, a novel method based on Grubbs criterion and local outlier factor (G-LOF) method is proposed in this study. The simulation model of the PV array is built to obtain the reference current of each PV string. The local outlier factor (LOF) is a ratio of the average local reachability density of sample neighbourhoods relative to its local reachability density. In this study, the LOF is used to quantify the abnormality by comparing the measured current and the simulated reference current. To reduce the false alarm probability of LOF, the Grubbs criterion is utilised as the test criterion to detect the abnormality of PV strings and it is merged with the LOF to derivate the G-LOF. The experiments of partial shading, open-circuit fault and short-circuit fault of PV modules are implemented in sunny and cloudy days. Experimental results verify that the proposed G-LOF method can sensitively detect above abnormalities, especially can detect the slight performance reduction caused by the partial shading or faults.

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A fault diagnosis method for photovoltaic arrays based on fault parameters identification

Cited by 49 publications

References 26 publications

Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network

Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network

Evaluation and improvement of IEC 60891 correction methods for I-V curves of defective photovoltaic panels

Fault detection of photovoltaic array based on Grubbs criterion and local outlier factor

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