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Table of contents
Acknowledgement
Table of contents
List of figures
List of tables
List of abbreviations
List of publications
General introduction
Background and motivation
Problem statement
Thesis outline
Contribution of thesis
Chapter I Photovoltaic array faults: State of the art
I.2.1. Defect due to human error
I.2.2. Environmental factors
I.2.3. Material interaction
I.2.4. Cause-effect circle
I.3.1. Fault classification
I.3.2. PV array faults
I.3.2.1. Cell-level faults
I.3.2.2. Module-level faults
I.3.2.3. Array-level faults
I.4.1. Safety hazard categorization
I.4.2. Power loss categorization
I.4.3. Summary of fault impact
Faults cases studied
Conclusion
Chapter II Fault detection and diagnosis of photovoltaic array: State of the art 24 Introduction Visual inspection for fault diagnosis
Automatic information analysis for fault diagnosis
II.3.1. Modelling
II.3.1.1. Physics-based modelling
II.3.1.2. Data-based modelling
II.3.2. Pre-processing
II.3.2.1. Format unification
II.3.2.2. Data cleaning
II.3.2.3. Data augmentation
II.3.2.4. Format transformation
II.3.3. Feature extraction
II.3.3.1. Statistical parameters
II.3.3.2. Signal transformation methods
II.3.3.3. Image processing methods
II.3.3.4. Multivariate transformation techniques
II.3.3.5. Estimation and control techniques
II.3.4. Feature analysis for FDD
II.3.4.1. Threshold analysis
II.3.4.2. Statistical analysis
II.3.4.3. Machine learning techniques
II.3.5. Illustration of the four-step automatic PV FDD scheme
II.4.1. Summary of fault diagnosis methods
II.4.2. Description of the proposed FDD strategy
Chapter III Correction of PV I-V curve measured under faulty condition
Preparation of I-V curves for correction
III.2.1. PV array modeling
III.2.1.1. Cell-level modeling
III.2.1.2. Module-level modeling
III.2.1.3. Array-level modeling
III.2.2. Environmental settings
III.2.3. Configuration of faults
III.2.4. Impact of faults on I-V curves
III.3.1. Usual correction procedures
III.3.1.1. Procedure 1 (P1)
III.3.1.2. Procedure 2 (P2)
III.3.1.3. Procedure 3 (P3)
III.3.2. New correction procedure
Metrics for the evaluation of correction performance
III.4.1.1. Metric for the evaluation of correction of the entire curve
III.4.1.2. Metric for the evaluation of correction of single parameters
III.5.1. Performance of correction procedures using single I-V curve
III.5.1.1. Selection of G and Tm based on field-measurements
III.5.1.2. Correction performance with constant fault severity
III.5.1.3. Correction performance with varying fault severity
III.5.2. Performance of correction methods using multiple I-V curves
III.5.2.1. Selection of G and Tm for reference curves
III.5.2.2. Correction performance with constant fault severity
III.5.2.3. Correction performance with variable fault severity
Conclusion
Chapter IV PV fault diagnosis using I-V curves and machine learning classifiers
introduction
Configuration of the simulated dataset
IV.2.1. PV array model configuration
IV.2.2. Generation of dataset
IV.3.1. Correction of I-V curve
IV.3.2. Resampling of I-V curve
IV.4.1. Feature transformation
IV.4.1.1. Recurrence Plot (RP)
IV.4.1.2. Gramian Angular Difference Field (GADF)
IV.4.2. Dimensionality reduction of features
IV.5.1. Analysis techniques-machine learning classifiers
IV.5.2. Diagnosis results using simulated data
IV.5.2.1. Performance of fault classification
IV.5.2.2. Robustness to additional disturbance
IV.5.2.3. Influence of PCA
IV.5.2.4. Influence of transformation
IV.5.3. Diagnosis results using experimental data
IV.5.3.1. Description of experimental platforms
IV.5.3.2. Experimental test result
IV.6.1. Methods for comparison
IV.6.1.1. Methods based on partial usage of I-V curves
IV.6.1.2. Methods based on complete usage of I-V curves
IV.6.2. Comparison results
Conclusion and perspectives
Résumé en francais
Bibliography
