Data Analysis and Modeling for Fault Detection in Solar Photovoltaic (PV) System : 광전지(PV) 시스템의 고장 탐지를 위한 테이터 분석 및 모델링

다국어 초록 (Multilingual Abstract)

This thesis presents the solar (PV) Power output data analysis and modelling using least mean square (LMS), linear regression and multiple linear regression algorithms and comparison between them to find the best model for applying in the PV system Fault detection. This method has been developed and validated using climatic and electrical output obtained from a SANYO 200 Wp photovoltaic modules installed at the Hae-Nam, Korea. This modelling includes the correlation of solar PV power output and solar irradiation. In modelling algorithms, PV power is modelled adaptively as a function of solar irradiation and each model is compared in terms of estimated error performance based on statistical and graphical methods. From the result, it was found that the multiple linear regression modelling is the best for solar PV modelling with MSE 0.00818 with modelling error 1.58% which is less than that compared to the model using the least Mean square (LMS) having 1.97% and linear regression 5.98%.
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After successfully modeled, the solar Photo-voltaic (PV) power output as a function of solar irradiance, resulting best model is used for the development of practical fault detection.
Our modelling results had fairly low complexity with high fault detection rates. The fault detection is based on the analysis of the power losses using the linear regression modelling. The model estimated by step-wise linearity of the PV power output as a function of irradiance. The results obtained from this modelling indicate that the under normal condition the solar radiation and PV power output have a very strong positive correlation and very useful for solar PV data prediction. In addition, the observations below the proposed linear functions are considered as the faulty PV data. From Overall results, we can conclude that this PV system data modelling and fault detection approach is reasonable for PV power estimation and faulty data analysis

목차 (Table of Contents)

• Abstract i
• Acknowledgements v
• List of Figures x
• List of Tables xii
• 1 Introduction 1
• 1.1 Motivation and Background 1
• 1.2 Literature Review 4
• 1.3 Problem Statement 6
• 1.4 Contribution 8
• 1.5 Outline of the Thesis 9
• 2 Background 11
• 2.1 Rapid growth of Photovoltaic Industry 11
• 2.2 Fundamentals of Photovoltaic Systems 14
• 2.2.1 Solar PV cell Technologies 16
• 2.2.2 How to scale up to solar cells to a PV array 18
• 2.2.3 PV System Components 22
• 2.2.4 Maximum Power Point Tracker (MPPT) 24
• 2.2.5 Electrical Parameters of the Solar PV System 26
• 2.2.6 Solar PV Module dependency on the Climate Factor 30
• 2.2.7 Solar Power in practice 33
• 2.3 Basic Data Analysis 35
• 2.3.1 Statistical Analysis 35
• 2.3.1.1 Numeric Analysis 35
• 2.3.1.2 Histogram Analysis 37
• 2.3.1.3 Q-Q Plot 37
• 2.3.1.4 Correlation 38
• 2.3.1.5 Min-Max Scaling 39
• 2.4 Types of Modelling Algorithms 40
• 2.4.1 Linear Regression 40
• 2.4.2 Correlation Analysis 41
• 2.4.3 Scatter Plot 42
• 2.4.4 LMS Algorithms 43
• 3 Experimental Set-Up 47
• 3.1 Experimental Set-up 47
• 3.2 Experimental Set-Up for Data Collection 51
• 4 Data Analysis for Simulation 53
• 4.1 Data Analysis Introduction 53
• 4.1.1 Data Collection 53
• 4.2 Block Diagram for Data Analysis and Proposed Method 54
• 4.3 Input Data Analysis 56
• 4.3 Power and Irradiance values analysis for the 1-min measurements 57
• 4.2.1 Histogram Analysis 58
• 4.2.2 Scatter Plot Analysis 59
• 4.2.3 Q-Q Plot Analysis 61
• 4.2.4 Statistical Analysis 63
• 4.3 Data Averaging 64
• 4.3.1 Data Analysis for the 10-min Measurements Data 64
• 4.4 Data Analysis Conclusion 68
• 5 Model Development And Comparison 71
• 5.1 LMS Based PV Output Modelling 71
• 5.2 Linear Regression based PV Output Modelling 77
• 5.3 Linear Regression based PV Output Modelling 80
• 5.2 MSE Comparison of Modelling 82
• 6 Multiple Linear Model Development for Solar PV Fault Detection 85
• 7 Conclusion and future works 90
• 7.1 Conclusion 90
• 7.2 Future work: Data analysis and Development of solar PV fault detection 92
• References 94