| Description |
1 online resource |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Note |
Print version record. |
| Contents |
Front Cover -- Degradation, Mitigation, and Forecasting Approaches in Thin Film Photovoltaics -- Copyright -- Contents -- List of figures -- List of tables -- Biography -- Preface -- Acknowledgements -- Acronyms -- 1 Introduction to photovoltaics -- 1.1 Introduction -- 1.2 Scenario of global energy requirements -- 1.3 Global warming and renewable energy -- 1.4 Brief overview of PV manufacturing -- 1.5 Characteristics of PV cell -- 1.5.1 Quantum efficiency versus wavelength characteristics -- 1.5.2 Current versus voltage characteristics -- 1.6 Current status of PV technology -- 1.7 Does PV pay back? -- Bibliography -- 2 Thin-film photovoltaics -- 2.1 Introduction -- 2.2 Photovoltaic effect -- 2.3 Some popular TFPV absorber materials -- 2.3.1 Amorphous silicon (a-Si) -- 2.3.2 Cadmium telluride (CdTe) -- 2.3.3 Copper indium gallium selenide (CIGS) -- 2.3.4 Copper zinc tin sulphide (CZTS) -- 2.3.5 Organic semiconductors -- 2.3.6 Organic-inorganic hybrid perovskite semiconductors -- 2.4 Conventional and emerging PV materials -- 2.5 Photovoltaic energy conversion -- 2.6 Thin-film photovoltaic manufacturing and recycling -- Bibliography -- 3 Performance-limiting issues in TFPVs -- 3.1 Introduction -- 3.2 Crystal defects -- 3.2.1 Point defects -- 3.2.2 Line defects -- 3.2.3 Planar defects -- 3.2.4 Volume defects -- 3.3 Carrier generation and recombination -- 3.4 Mobility degradation by scattering and grain boundary -- 3.5 Environmental and other factors -- 3.5.1 Effect of dust accumulation -- 3.5.2 Effect of humidity -- 3.5.3 Effect of wind -- 3.5.4 Effect of temperature -- 3.5.5 Effect of module orientation -- 3.5.6 Effect of shading -- 3.6 Leakage current and potential induced degradation -- Bibliography -- 4 Yield increase through soiling prevention -- 4.1 Introduction -- 4.2 Measuring output of PV cells, modules -- and systems. |
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4.3 Methods of cleaning solutions -- 4.3.1 Forced flow from air-conditioning systems -- 4.3.2 Rainfall, water-based and manual cleaning -- 4.3.3 Mechanized cleaning and electrodynamic screens (EDS) -- 4.3.4 Super hydrophobic and hydrophilic planes (SHOP and SHIP) -- 4.4 Mechanisms behind degradation and techniques to detect degradation -- Bibliography -- 5 Water-free automated solar-panel cleaning -- 5.1 Semiautomatic water-based cleaning solutions -- 5.2 Automated panel cleaning unit -- 5.3 Specifications of service unit -- 5.4 Process command flow of operations in a MicroController -- 5.5 Quantitative measurement of the effect of cleaning -- Bibliography -- 6 Numerical simulations of potential-induced degradation -- 6.1 Basics of potential-induced degradation -- 6.2 Simulation methodology -- 6.3 Simulation-based analysis of PID effects in TFPV cells -- 6.3.1 Effect of PID in nongraded CIGS cells -- 6.3.2 Effect of PID in graded CIGS cells -- 6.3.3 Effect of PID in CZTS, CZTSe and CZTSSe cells -- Bibliography -- 7 PID for multicrystalline soiled panels: a forecasting-based approach -- 7.1 Introduction -- 7.2 Experimental setup and methodology -- 7.3 Forecasting methods -- 7.3.1 Persistence learning algorithm (PLA) -- 7.3.2 Auto-regressive integrated moving average (ARIMA) -- 7.3.3 Single exponential smoothing (SES) -- 7.3.4 Artificial neural networks (ANN) -- 7.3.5 Support vector regression (SVR) -- 7.3.6 Random forests -- 7.3.7 Gradient boosting method -- 7.4 Results and discussions -- 7.4.1 Correlation study of parameters -- 7.4.2 Forecasting results -- 7.5 Conclusions -- Bibliography -- 8 Optimization of on-site PID detection methods -- 8.1 Introduction -- 8.2 Adopted methodology -- 8.3 Existing PID detection techniques -- 8.3.1 Electroluminescence (EL) imaging -- 8.3.2 Thermal imaging -- 8.3.3 Open-circuit voltage measurement. |
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8.3.4 Operating voltage measurement -- 8.3.5 Current voltage (IV) curve tracing -- 8.3.6 Dark current-voltage (IV) curve tracing -- 8.4 Application of multiple-criteria decision-making (MCDM) techniques -- 8.4.1 Simple additive weighting (SAW) -- 8.4.2 Technique for order of preference by similarity to ideal solution (TOPSIS) -- 8.4.3 Elimination Et Choix Traduisant la Realité (ELECTRE) -- 8.5 Results and discussions -- Bibliography -- 9 Next generation photovoltaics -- 9.1 Recently developed photovoltaics -- 9.1.1 Kesterite photovoltaics -- 9.1.2 Dye-sensitized photovoltaics -- 9.1.3 Organic photovoltaics -- 9.1.4 Quantum-dot photovoltaics -- 9.1.5 Perovskite photovoltaics -- 9.2 Commercialization challenges for next-generation PVs -- 9.3 Current research status and forecast for next-generation PVs -- Bibliography -- Index -- Back Cover. |
| Subject |
Photovoltaic cells -- Materials.
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Thin films.
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Cellules photovoltaïques -- Matériaux.
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Couches minces.
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Photovoltaic cells -- Materials
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Thin films
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| Added Author |
Bhargava, Kshitij.
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| Other Form: |
Print version: 9780128236420 |
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Print version: 0128234830 9780128234839 (OCoLC)1225623835 |
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Print version: DEB, DIPANKAR. DEGRADATION, MITIGATION AND FORECASTING APPROACHES IN THIN FILM PHOTOVOLTAICS. [S.l.] : ELSEVIER ACADEMIC PRESS, 2021 0128234830 (OCoLC)1225623835 |
| ISBN |
9780128236420 (electronic bk.) |
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0128236426 (electronic bk.) |
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9780128234839 |
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0128234830 |
| Standard No. |
AU@ 000069926472 |
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AU@ 000069932683 |
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