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
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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