Fault Detection and Classification of CIGS ThinFilm PV Modules Using an Adaptive NeuroFuzzy Inference Scheme
Abstract
:1. Introduction
2. Methodology
3. Collection of Input Datasets
4. Features Extraction Techniques
4.1. Statistical Parameters of IR images
4.2. Mathematical Parameters of IR Images
4.3. Electrical Parameters of IV Measurements
5. ANFIS Fault Classification Technique
5.1. ANFIS Structure
5.2. Application of ANFIS for Classification of Faults
5.3. Analysis of ANFIS Results
6. Estimation of Operating Power Ratio
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
 Feldman, R.; Wu, K.; Margolis, R. Solar Industry Update. NREL/PR7A4080427; 2021. Available online: https://www.nrel.gov/docs/fy21osti/80427.pdf (accessed on 12 April 2022).
 Haque, A.; Bharath, K.V.S.; Khan, M.A.; Jaffery, Z.A. Fault diagnosis of photovoltaic modules. Energy Sci. Eng. 2019, 7, 622–644. [Google Scholar] [CrossRef] [Green Version]
 Ibrahim, K.; Eltuhamy, R.; Rady, M.; Mahmoud, H. Failure Mode and Effects Analysis of CIGS Thin Film PV Modules Using Thermography Analysis and IV Measurements. Int. J. Energy Convers. (IRECON) 2021, 9, 17–28. [Google Scholar] [CrossRef]
 Köntges, M.; Kurtz, S.; Packard, C.; Jahn, U.; Berger, K.A.; Kato, K.; Friesen, T.; Liu, H.; Iseghem, M.V. Review of Failures of Photovoltaic Modules; Report IEAPVPS T1301:2014; International Energy Agency: Paris, France, 2014.
 Buerhop, C.; Schlegel, D.; Niess, M.; Vodermayer, C.; Weißmann, R.; Brabec, C. Reliability of IRimaging of PVplants under operating conditions. Sol. Energy Mater. Sol. Cells 2012, 107, 154–164. [Google Scholar] [CrossRef]
 Wang, P.; Zheng, S. Fault Analysis of Solar PV Array Based on Infrared Image. Acta Energ. Sol. Sin. 2010, 31, 197–202. [Google Scholar]
 Patel, H.; Agarwal, V. MATLABBased Modeling to Study the Effects of Partial Shading on PV Array Characteristics. IEEE Trans. Energy Convers. 2008, 23, 302–310. [Google Scholar] [CrossRef]
 Kumar, A.; Pachauri, R.K.; Chauhan, Y.K. Experimental analysis of SP/TCT PV array configurations under partial shading conditions. In Proceedings of the 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), Delhi, India, 4–6 July 2016; pp. 1–6. [Google Scholar] [CrossRef]
 Bechouat, M.; Younsi, A.; Sedraoui, M.; Soufi, Y.; Yousfi, L.; Tabet, I.; Touafek, K. Parameters identification of a photovoltaic module in a thermal system using metaheuristic optimization methods. Int. J. Energy Environ. Eng. 2017, 8, 331–341. [Google Scholar] [CrossRef] [Green Version]
 Lydia, M.; Sindhu, K.; Gugan, K. Analysis on Solar Panel Crack Detection Using Optimization Techniques. J. Nano Electron. Phys. 2017, 9, 02004. [Google Scholar] [CrossRef]
 Titri, S.; Larbes, C.; Toumi, K.Y.; Benatchba, K. A new MPPT controller based on the Ant colony optimization algorithm for Photovoltaic systems under partial shading conditions. Appl. Soft Comput. 2017, 58, 465–479. [Google Scholar] [CrossRef]
 Natarajan, K.; Pala, P.K.; Sampath, V. Fault Detection of Solar PV system using SVM and Thermal Image Processing. Int. J. Renew. Energy Res. 2020, 10, 967–977. [Google Scholar]
 Madeti, S.R.; Singh, S.N. Modeling of PV system based on experimental data for fault detection using kNN method. Sol. Energy 2018, 173, 139–151. [Google Scholar] [CrossRef]
 Shin, J.H.; Kim, J.O. OnLine Diagnosis and Fault State Classification Method of Photovoltaic Plant. Energies 2020, 13, 4584. [Google Scholar] [CrossRef]
 Zhao, Y.; Yang, L.; Lehman, B.; de Palma, J.F.; Mosesian, J.; Lyons, R. Decision treebased fault detection and classification in solar photovoltaic arrays. In Proceedings of the TwentySeventh Annual IEEE Applied Power Electronics Conference and Exposition (APEC), Orlando, FL, USA, 5–9 February 2012; pp. 93–99. [Google Scholar] [CrossRef]
 Sun, J.; Sun, F.; Fan, J.; Liang, Y. Fault Diagnosis Model of Photovoltaic Array Based on Least Squares Support Vector Machine in Bayesian Framework. Appl. Sci. 2017, 7, 1199. [Google Scholar] [CrossRef] [Green Version]
 Pierdicca, R.; Malinverni, E.S.; Piccinini, F.; Paolanti, M.; Felicetti, A.; Zingaretti, P. Deep convolutional neural network for automatic detection of damaged photovoltaic cells. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2018, XLII2, 893–900. [Google Scholar] [CrossRef] [Green Version]
 Jaffery, Z.A.; Dubey, A.K.; Irshad; Haque, A. Scheme for predictive fault diagnosis in photovoltaic modules using thermal imaging. Infrared Phys. Technol. 2017, 83, 182–187. [Google Scholar] [CrossRef]
 Belaout, A.; Krim, F.; Mellit, A.; Talbi, B.; Arabi, A. Multiclass adaptive neurofuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification. Renew. Energy 2018, 127, 548–558. [Google Scholar] [CrossRef]
 Lin, H.; Chen, Z.; Wu, L.; Lin, P.; Cheng, S. Online Monitoring and Fault Diagnosis of PV Array Based on BP Neural Network Optimized by Genetic Algorithm. MultiDiscip. Trends Artif. Intell. 2015, 9426, 102–112. [Google Scholar] [CrossRef]
 Kurukuru, V.S.B.; Haque, A.; Khan, M.A.; Tripathy, A.K. Fault classification for Photovoltaic Modules Using Thermography and Machine Learning Techniques. In Proceedings of the International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia, 3–4 April 2019; pp. 1–6. [Google Scholar]
 Dunderdale, C.; Brettenny, W.; Clohessy, C.; Dyk, E.E. Photovoltaic defect classification through thermal infrared imaging using a machine learning approach. Prog. Photovolt. Res. Appl. 2020, 28, 177–188. [Google Scholar] [CrossRef]
 Niazi, K.; Akhtar, W.; Khan, H.A.; Sohaib, S.; Nasir, A.K. Binary Classification of Defective Solar PV Modules Using Thermography. In Proceedings of the 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC), Waikoloa, HI, USA, 10–15 June 2018; pp. 753–757. [Google Scholar] [CrossRef] [Green Version]
 Leotta, G.; Pugliatti, P.; Di Stefano, A.; Aleo, F.; Bizzarri, F. Post processing technique for thermographic images provided by drone inspections. In Proceedings of the 31st European Photovoltaic Solar Energy Conference and Exhibition (EU PVSEC), Hamburg, Germany, 14–18 September 2015. [Google Scholar] [CrossRef]
 Rasch, R.; Behrens, G.; Hamelmann, F.; Hantelmann, S.; Dreimann, R.; Weicht, J. Automated Thermal Imaging for Fault Detection on PVSystems. In Proceedings of the 31st European Photovoltaic Solar Energy Conference and Exhibition (EU PVSEC), Hamburg, Germany, 14–18 September 2015. [Google Scholar] [CrossRef]
 Le, M.; Luong, V.S.; Nguyen, D.K.; Dao, V.D.; Vu, N.H.; Vu, H.H.T. Remote anomaly detection and classification of solar photovoltaic modules based on deep neural network. Sustain. Energy Technol. Assess. 2021, 48, 101545. [Google Scholar] [CrossRef]
 Fadhel, S.; Delpha, C.; Diallo, D.; Bahri, I.; Migan, A.; Trabelsi, M.; Mimouni, M. PV shading fault detection and classification based on IV curve using principal component analysis: Application to isolated PV system. Sol. Energy 2018, 179, 1–10. [Google Scholar] [CrossRef] [Green Version]
 Samara, S.; Natsheh, E. Intelligent PV Panels Fault Diagnosis Method Based on NARX Network and Linguistic Fuzzy RuleBased Systems. Sustainability 2020, 12, 2011. [Google Scholar] [CrossRef] [Green Version]
 Ul Haq, A.; Sindi, H.; Gul, S.; Jalal, M. Modeling and Fault Categorization in ThinFilm and Crystalline PV Arrays through Multilayer Neural Network Algorithm. IEEE Access 2020, 8, 102235–102255. [Google Scholar] [CrossRef]
 Aziz, F.; Haq, A.U.; Ahmad, S.; Mahmoud, Y.; Jalal, M.; Ali, U. A Novel Convolutional Neural Network Based Approach for Fault Classification in Photovoltaic Arrays. IEEE Access 2020, 8, 41889–41904. [Google Scholar] [CrossRef]
 Huang, J.M.; Wai, R.J.; Gao, W. Newly Designed Fault Diagnostic Method for Solar Photovoltaic Generation System Based on IVCurve Measurement. IEEE Access 2019, 7, 70919–70932. [Google Scholar] [CrossRef]
 Wu, Y.; Chen, Z.; Wu, L.; Lin, P.; Cheng, S.; Lu, P. An Intelligent Fault Diagnosis Approach for PV Array Based on SARBF Kernel Extreme Learning Machine. Energy Procedia 2017, 105, 1070–1076. [Google Scholar] [CrossRef]
 Mekki, H.; Mellit, A.; Salhi, H. Artificial neural networkbased modelling and fault detection of partial shaded photovoltaic modules. Simul. Model. Pract. Theory 2016, 67, 1–13. [Google Scholar] [CrossRef]
 Bonsignore, L.; Davarifar, M.; Rabhi, A.; Tina, G.M.; Elhajjaji, A. NeuroFuzzy Fault Detection Method for Photovoltaic Systems. Energy Procedia 2014, 62, 431–441. [Google Scholar] [CrossRef] [Green Version]
 Lazzaretti, A.E.; Da Costa, C.H.; Rodrigues, M.P.; Yamada, G.D.; Lexinoski, G.; Moritz, G.L.; Oroski, E.; De Goes, R.E.; Linhares, R.R.; Stadzisz, P.C.; et al. A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants. Sensors 2020, 20, 4688. [Google Scholar] [CrossRef]
 Hussain, M.; Dhimish, M.; Titarenko, S.; Mather, P. Artificial neural network based photovoltaic fault detection algorithm integrating two bidirectional input parameters. Renew. Energy 2020, 155, 1272–1292. [Google Scholar] [CrossRef]
 Dhimish, M.; Badran, G. Photovoltaic HotSpots Fault Detection Algorithm Using Fuzzy Systems. IEEE Trans. Device Mater. Reliab. 2019, 19, 671–679. [Google Scholar] [CrossRef] [Green Version]
 Eltuhamy, R.A.; Rady, M.; Ibrahim, K.H.; Mahmoud, H.A. Novel features extraction for fault detection using thermography characteristics and IV measurements of CIGS thinfilm module. Instrum. Mes. Métrologie 2020, 19, 311–325. [Google Scholar] [CrossRef]
 International Electrotechnical Commission. Photovoltaic (PV) Systems—Requirements for Testing, Documentation and Maintenance—Part 3: Photovoltaic Modules and Plants—Outdoor Infrared Thermography; IEC TS 624463: 2007; International Electrotechnical Commission (IEC): Geneva, Switzerland, 2017. [Google Scholar]
 Jang, J.S.R. ANFIS: Adaptivenetworkbased fuzzy inference system. IEEE Trans. Syst. Man Cybern. 1993, 23, 665–685. [Google Scholar] [CrossRef]
Methodology  Reference  Fault Classification  Accuracy of Classification  Remarks 

(a) using thermography techniques.  
Texture feature extraction (TFE) and support vector machine (SVM)  [12]  Cracks, hot spots due to shading and soiling. Categorize solar modules into defective and nondefective.  97% 

Knearest neighbor (KNN  [21]  Categorize solar modules into defective and nondefective.  80.3%  
Support vector machine (SVM)  56.8%  
Neural network  92.8  
Support vector machine (SVM)  [22]  91.2%  
Deeplearning convolutional neural network (CNN)  [22]  89.5%  
n Bayes: a binary class densitybased classifier  [23]  98.4%  
The automated edge detection technique  [24,25]  Defective solder junctions, short circuits, and bypassed substrings.  Not reported  
Deep learning neural network  [26]  Cracks, shadowing, diode, soiling, hotspots, and offline module.  Classify 12 anomaly types with an average of 86%  
(b) with input datasets from PV modules electrical IV characteristics.  
Multiclass adaptive neurofuzzy classifier  [19]  Partial shading, increased series resistance, bypass diode shortcircuited, bypass diode impedance, PV module shortcircuited.  65–100% depending on fault type 

Principal component analysis (PCA)  [27]  Shading faults.  97% 

AI nonlinear autoregressive exogenous neural network (NARX)  [28]  Open and shortcircuit degradation, faulty MPPT, partial shading (PS).  98.2% 

Multilayer neural network with a scaled conjugate gradient algorithm (SCG)  [29]  Short circuits, aging, shading faults, and bypass diode faults.  99.6% 

Convolutional neural networks (CNN)  [30]  Partial Shading (PS), high impedance, low location mismatch, maximum power point tracking (MPPT).  73.53% 

Multiclass adaptive boosting (AdaBoost) algorithm, using multiclass exponential (SAMME) loss function based on the classification and regression tree (CART)  [31]  Shortcircuit faults (SCF), partial shading with the bypassdiode on (PSBO), partial shading with the bypassdiode reversed (PSBR), and abnormal aging faults (AAF).  99.4% 

Radial basis function (RBF) kernel extreme learning machine (ELM) optimized by simulated annealing algorithm,  [32]  Short circuits, shading faults, and aging.  Shadows 91.55%  Need real outdoor experiments. 
Short circuits 93.64%  
Aging 90.91%  
Artificial neural network  [33]  Partial shading  Not reported  A single type of fault. 
Multiclass adaptive neurofuzzy classifier (MCNFC) and ANN  [19]  Partial shading, high series resistance, bypass diode impedance and short circuits.  Not reported  The MCNFC outperforms the ANNclassifier. 
(c) with input datasets from PV modules electrical IV characteristics and environmental conditions.  
Backward propagation NN optimized by genetic algorithm  [20]  Short circuits, local material aging, shading.  78% for short circuits, 97% for aging, 100% shadows 

Neurofuzzy and simulation  [34]  Upper and lower earth faults, diode shortcircuit faults, partial shading.  Not reported  Limited number of PV module circuit faults. 
Cursive linear model and an ANN  [35]  Short circuits, open circuits, partial shading, and degradation.  92.64%  Limited number of PV module circuit faults. 
ANNs  [36]  Disconnected modules.  97% 

(d) with input datasets from thermography analysis and PV modules electrical IV characteristics.  
Statistical features extraction and electrical measurements characteristics  [3]  Cracks, delamination, burn marks, PID, soiling, and open strings.  Not reported  Applied for CIGS PV modules. 
Fuzzy inference system (FIS) using Mamdanitype fuzzy controller  [37]  Identify the six main types of hotspots that influence PV modules.  96.7%  Inability to detect hot spots when there is a lot of partial shading. 
Novel feature extraction based on mathematical parameters  [38]  Cracks, delamination, burn marks, PID, soiling, and open strings.  Not reported  Detect all types of CIGS thinfilm PV modules, detect modules with multifaults. 
Category/Type  Description 

A  Soiling 
B  Cracking and soiling 
C  Cracks, burn marks, and soiling 
D  Potentialinduced degradation (PID) 
E  PID and cracks 
F  PID, cracks, and delamination 
G  Open strings (HM) 
H  Dead modules 
Item  Number of 

Nodes  1078 
Linear parameters  2048 
Nonlinear parameters  48 
Training data pairs  36 
Checking data pairs  27 
Fuzzy rules  512 
Type of Feature Extraction (FE) Methods  Type of Membership Function  Accuracy 

Statistical (FE)  Triangle  83.33% 
IV measurement (FE)  Gaussian  100% 
Mathematical parameter (FE)  All type  100% 
Fault Type  Regression Model  Rsq %  pValue 

A  ${\mathrm{P}}_{\mathrm{r}}=0.70350.1428{\gamma}_{1}$  62.34  0.062 
B  ${\mathrm{P}}_{\mathrm{r}}=0.42270.07026{\gamma}_{1}$  34.83  0.056 
C  ${\mathrm{P}}_{\mathrm{r}}=0.592919.28\mathrm{FDM}$  99.99  0.06 
D  ${\mathrm{P}}_{\mathrm{r}}=0.4062+0.007860\mathrm{pp}$  48.67  0.012 
E  ${\mathrm{P}}_{\mathrm{r}}=5.292+6.747\mathsf{\omega}$  57.41  0.029 
F  ${\mathrm{P}}_{\mathrm{r}}=0.5444+0.6615\mathrm{FDM}$  77.43  0.315 
G  ${\mathrm{P}}_{\mathrm{r}}=3.3320.0187\mathrm{pp}$  69.24  0.005 
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Eltuhamy, R.A.; Rady, M.; Almatrafi, E.; Mahmoud, H.A.; Ibrahim, K.H. Fault Detection and Classification of CIGS ThinFilm PV Modules Using an Adaptive NeuroFuzzy Inference Scheme. Sensors 2023, 23, 1280. https://doi.org/10.3390/s23031280
Eltuhamy RA, Rady M, Almatrafi E, Mahmoud HA, Ibrahim KH. Fault Detection and Classification of CIGS ThinFilm PV Modules Using an Adaptive NeuroFuzzy Inference Scheme. Sensors. 2023; 23(3):1280. https://doi.org/10.3390/s23031280
Chicago/Turabian StyleEltuhamy, Reham A., Mohamed Rady, Eydhah Almatrafi, Haitham A. Mahmoud, and Khaled H. Ibrahim. 2023. "Fault Detection and Classification of CIGS ThinFilm PV Modules Using an Adaptive NeuroFuzzy Inference Scheme" Sensors 23, no. 3: 1280. https://doi.org/10.3390/s23031280