A proposed graphical electrical signatures supervision method to study PV module failures

Basri, Y. El; Bressan, Michaël; Séguier, Lionel; Al-Awadhi, Hanan T.; Alonso, Corinne

doi:10.1016/j.solener.2015.02.048

Cited by 19 publications

(9 citation statements)

References 18 publications

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“…Apart from power losses, the presence of localized shading on PV modules leads to an overheating of the shaded PV cells despite the presence of bypass diodes. The IR thermography such in [5,6] proves the presence of hot spot zones of the shaded PV cells. The temperature increase in these zones reduces considerably the PV module lifetime and can damage the shaded cells [7].…”

Section: Introductionmentioning

confidence: 99%

“…The study in [14] detects the activation of bypass diodes indicating the presence of shading, by calculating the first and the second derivatives of (I-V). In [6], the detection of the shading is done through the comparison of I-V curves in normal and shaded operations. The methods presented in [6,14] are simple and can detect shading faults, but they are not able to identify and classify the type of shading patterns.…”

Section: Introductionmentioning

confidence: 99%

“…In [6], the detection of the shading is done through the comparison of I-V curves in normal and shaded operations. The methods presented in [6,14] are simple and can detect shading faults, but they are not able to identify and classify the type of shading patterns. The shading faults can also be detected with artificial neural network (ANN) and fuzzy classifier system as described in [15,16].…”

Section: Introductionmentioning

confidence: 99%

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Data-driven approach for isolated PV shading fault diagnosis based on experimental I-V curves analysis

Fadhel

Migan²,

Delpha

et al. 2018

2018 IEEE International Conference on Industrial Technology (ICIT)

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This paper deals with a data-driven fault diagnosis method for photovoltaic (PV) system. The proposed method is based on the Principal Component Analysis (PCA) to detect and identify different shading types. The PCA uses the current-voltage (I-V) curves that are experimentally determined for a monocrystalline PV module of 250Wc. The experimental tests were carried out for several shading patterns covering the PV cells. For the diagnosis process, three features (current, voltage and power of PV module) are extracted for each test to build the database which is then analyzed through the PCA algorithm. Simulation results using the experimental data, prove the efficiency of the proposed method in terms of discrimination. The healthy data are clearly separated from the faulty ones despite sudden irradiation variations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-driven approach for isolated PV shading fault diagnosis based on experimental I-V curves analysis

Fadhel

Migan²,

Delpha

et al. 2018

2018 IEEE International Conference on Industrial Technology (ICIT)

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“…Moreover, the presence of localized shading on PV modules leads to an overheating of the shaded cells despite the presence of bypass diodes. Using the infrared thermography (IRT) many studies prove the presence of hot spot zones on the shaded PV cells (Basri et al, 2015;Tsanakas et al, 2016). Thus, the temperature increase in these zones leads to a thermal power dissipation (Bressan et al, 2016), reduces considerably the PV module lifetime and can damage the shaded cells (Brooks et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

PV shading fault detection and classification based on I-V curve using principal component analysis: Application to isolated PV system

et al. 2019

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Health monitoring and diagnosis of photovoltaic (PV) systems is becoming crucial to maximise the power production, increase the reliability and life service of PV power plants. Operating under faulty conditions, in particular under shading, PV plants have remarkable shape of current-voltage (I-V) characteristics in comparison to reference condition (healthy operation). Based on real electrical measurements (I-V), the present work aims to provide a very simple, robust and low cost Fault Detection and Classification (FDC) method for PV shading faults. At first, we extract the features for different experimental tests under healthy and shading conditions to build the database. The features are then analysed using Principal Component Analysis (PCA). The accuracy of the data classification into the PCA space is evaluated using the confusion matrix as a metric of class separability. The results using experimental data of a 250 Wp PV module are very promising with a successful classification rate higher than 97% with four different configurations. The method is also cost effective as it uses only electrical measurements that are already available. No additional sensors are required.

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“…These cases are called "faults", they are either system-based or environmental factors based [2,3]. Because these types of conditions, which can be called as bad working conditions, can seriously affect the produced energy yield, system efficiency, as well as the security and reliability of the entire system in case of them not being detected and corrected [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic Fault Detection and Classification Using Common Vector Approach Based on I-V Curve

Turhal

Onal

2020

Acta Phys. Pol. A

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Photovoltaic systems are subject to various faults. These faults result in significant degradation in photovoltaic system performances. These degradations result in a significant reduction in system efficiency as a result of increase in operating costs and times of exclusion of the photovoltaic system. Therefore, health monitoring and high accuracy error detection are extremely important for a photovoltaic system. In this study, fault detection and classification in photovoltaic systems are performed by using common vector approach which is first proposed in the literature for speech recognition and then is applied for face recognition. The faulty conditions discussed in the study are partial shading and series resistance degradation. In both fault detection situations, the common vector approach method is used to determine the type of fault using simulation data obtained under healthy operation and faulty operation conditions and very high fault detection rates are obtained. In order to evaluate the accuracy of the proposed method, the data obtained are also evaluated with the principal component analysis method, which is previously presented for photovoltaic system fault detection in the literature. According to the results obtained, principal component analysis method completely fails in case of serial resistance degradation fault. However, by using common vector approach method proposed in this study, a very high fault detection rate such as 97.5% can be obtained in serial resistance degradation fault. Likewise, in the case of partial shading, higher fault detection rate is achieved in common vector approach method (99.6%) compared to principal component analysis method (95.4%).

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A proposed graphical electrical signatures supervision method to study PV module failures

Cited by 19 publications

References 18 publications

Data-driven approach for isolated PV shading fault diagnosis based on experimental I-V curves analysis

Data-driven approach for isolated PV shading fault diagnosis based on experimental I-V curves analysis

PV shading fault detection and classification based on I-V curve using principal component analysis: Application to isolated PV system

Photovoltaic Fault Detection and Classification Using Common Vector Approach Based on I-V Curve

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