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Advanced Fault Diagnosis and Condition Monitoring Schemes for Solar PV Systems

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Planning of Hybrid Renewable Energy Systems, Electric Vehicles and Microgrid

Part of the book series: Energy Systems in Electrical Engineering ((ESIEE))

Abstract

In the present era of smart technologies, the power sector has highly benefited as monitoring, supervision, and control have moved toward the intelligent power delivery. High-quality power estimation, self-healing, and machine-to-machine communication-based approaches have been appreciated to achieve more reliable and secured smart grids (SG). Renewable energy sources (RES), mainly solar photovoltaic (PV) systems are intermittent in nature and wisely utilized in power generation either as stand-alone or grid connected. Energy sector is focused on RES to reduce carbon footprint and affordable energy to all. Prediction, decision-making, and fast healing for recovery after faults in system, are prime objectives for fault diagnosis and condition monitoring of RES. Classical PV fault diagnosis schemes are available, which basically follow the general process of detection, feature extraction, and classification of fault data. Enormous data has to be handled by the processors either offline or online. In this chapter, fault detection schemes for handling preprocessing of raw data from various sensors through wire or wireless-based time domain or frequency domain methods like Fourier transform, Wavelet transform along with novel approaches based on the internet of things (IoT) have been rigorously reviewed. Traditional as well as advanced artificial intelligence (AI), machine learning (ML), and emerging approaches for PV fault classification and mitigation have been discussed thoroughly. Along with comprehensive and critical literature review, a smart PV fault classification scheme is proposed for the enhancement of the performance of solar PV systems.

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Abbreviations

I:

PV Cell current, (A)

k:

Boltzmann constant

Rsh:

Shunt resistance of PV cell

Rse:

Series resistance of PV cell

V:

PV cell voltage

Iph:

Light generated current

Io:

Reverse saturation current for D diode

Io2:

Reverse saturation current for D2 diode

VT:

Voltage due to temperature = VT(Ns*N*k*T)/q

Tc:

PV Cell temperature (operating)

N1:

Quality factor of D diode

N2:

Quality factor of the D2 diode

Voc:

OC voltage of PV cell

Vm:

Maximum voltage of PV cell

Isc:

SC current of PV cell

Im:

Max. current of PV cell

Pm:

Max. power of PV cell

Po:

Ideal power of PV cell

Prad:

Solar radiant power

ɳ:

Power conversion Efficiency

References

  • Aghaei M, Gandelli A, Grimaccia F, Leva S, Zich RE (2015) IR real time analyses for PV system monitoring by digital image processing techniques. In: Proceedings of international conference on event-based control, communication and signal processing (EBCCSP), pp 1–6

    Google Scholar 

  • Akram MN, Lotfifard S (2015) Modeling and health monitoring of dc side of photovoltaic array. IEEE Trans Sustain Energy 6(4):1245–1253

    Google Scholar 

  • Akram MW, Li G, Jin Y, Chen X, Zhu C, Zhao X, Aleem M, Ahmad A (2019) Improved outdoor thermography and processing of infrared images for defect detection in PV modules. Sol Energy 190:549–560

    Google Scholar 

  • Alam MK, Khan F, Johnson J, Flicker J (2015) A comprehensive review of catastrophic faults in PV arrays: types, detection, and mitigation techniques. IEEE J Photovoltaics 5(3):982–997. https://doi.org/10.1109/JPHOTOV.2015.2397599

    Article  Google Scholar 

  • Ali MH, Rabhi A, El Hajjaji A, Tina GM (2017) Real time fault detection in photovoltaic systems. Energy Procedia 111(September):914–923

    Google Scholar 

  • Alsina EF, Chica M, Trawiáski K, Regattieri A (2018) On the use of machine learning methods to predict component reliability from data driven industrial case studies. Int J Adv Manuf Technol 94(5–8):2419–2433

    Article  Google Scholar 

  • Aziz F, Ul Haq A, Ahmad S, Mahmoud Y, Jalal M, Ali U (2020) A novel convolutional neural network-based approach for fault classification in photovoltaic arrays. IEEE Access 8:41889–41904

    Google Scholar 

  • Baccoli R, Kumar A, Frattolillo A, Mastino C, Ghiani E, Gatto G (2021) Enhancing energy production in a PV collector—reflector system supervised by an optimization model: experimental analysis and validation. Energy Convers Manag 229

    Google Scholar 

  • Bartler A, Mauch L, Yang B, Reuter M, Stoicescu L (2018) Automated detection of solar cell defects with deep learning. In: Proceedings of 26th European signal processing conference (EUSIPCO), Sept 2018, pp 2035––2039

    Google Scholar 

  • Boggarapu PK, Manickam C, Lehman B, Chilakapati N et al (2020) Identification of pre-existing/undetected line-to-line faults in PV array based on pre-turn on/off condition of the PV inverter. IEEE Trans Power Electron

    Google Scholar 

  • Boppana S (2015) Outdoor soiling loss characterization and statistical risk analysis of photovoltaic power plants. Doctoral dissertation, Arizona State University

    Google Scholar 

  • Bouraiou A et al (2018) Experimental investigation of observed defects in crystalline silicon PV modules under outdoor hot dry climatic conditions in Algeria. Sol Energy 159(December):475–487

    Google Scholar 

  • Boutelhig A, Arab AH, Hanini S (2016) New approach to exploit optimally the PV array output energy by maximizing the discharge rate of a directly-coupled photovoltaic water pumping system (DC/PVPS). Energy Convers Manage 111:375–390

    Article  Google Scholar 

  • Carletti V, Greco A, Saggese A, Vento M (2020) An intelligent flying system for automatic detection of faults in photovoltaic plants. J Ambient Intell Hum Comput 11(5):2027–2040

    Google Scholar 

  • Chen L, Li S, Wang X (2016) Quickest fault detection in photovoltaic systems. IEEE Trans Smart Grid 9(3):1835–1847

    Google Scholar 

  • Chenni R, Makhlouf M, Kerbache T, Bouzid A (2007) A detailed modeling method for photovoltaic cells. Energy 32:1724–1730

    Article  Google Scholar 

  • Chine W, Mellit A, Lughi V, Malek A, Sulligoi G, Massi Pavan A (2016) A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks. Renew Energy 90:501–512

    Google Scholar 

  • Chouder A, Silvestre S (2010) Automatic supervision and fault detection of PV systems based on power losses analysis. Energy Convers Manage 51(10):1929–1937

    Google Scholar 

  • Cristaldi L, Faifer M, Lazzaroni M, Khalil MMAF, Catelani M, Ciani L (2015) Diagnostic architecture: a procedure based on the analysis of the failure causes applied to photovoltaic plants. Measurement 67:99–107

    Article  Google Scholar 

  • Darwish ZA, Kazem HA, Sopian K, Al-Goul MA, Alawadhi H (2015) Effect of dust pollutant type on photovoltaic performance. Renew Sustain Energy Rev 41:735–744

    Article  Google Scholar 

  • Deitsch S, Christlein V, Berger S, Buerhop-Lutz C, Maier A, Gallwitz F, Riess C (2012) Automatic classification of defective photovoltaic module cells in electroluminescence images. Sol Energy 185:455–468

    Google Scholar 

  • Dierauf T, Growitz A, Kurtz S, Cruz JLB, Riley E, Hansen C (2013) Weather corrected performance ratio. Contract 303:275–3000

    Google Scholar 

  • Dobaria BV, Sharma V, Adeshara A (2018) Investigation of failure and degradation types of solar PV plants in a composite climate: abstract after 4–6, years of field operation. Lect Notes Electr Eng 435:227–235

    Article  Google Scholar 

  • Dumnic B, LiivikE, Milicevic D, Popadic B, Katic V, Blaabjerg F (2018) Fault analysis and field experiences of central inverter based 2 MW PV plant. In: Proceedings of 20th European conference on power electronics and applications, pp 1–5

    Google Scholar 

  • El Fadil H, Giri F (2011) Climatic sensor less maximum power point tracking in PV generation systems. Contr Eng Pract 19:513–521

    Article  Google Scholar 

  • Fathabadi H (2015) Two novel techniques for increasing energy efficiency of photovoltaic-battery systems. Energy Convers Manage 105:149–166

    Article  Google Scholar 

  • Feaster S, Wamsted D (2020) IEEFA U.S.: utility-scale renewables top coal for the first quarter of 2020 [Online]. https://ieefa.org/ieefa-u-s-utility-scale-renewables-top-coal-for-thefirst-quarter-of-2020/

  • Fernández-Solas Á, Micheli L, Almonacid F, Fernández EF (2021) Optical degradation impact on the spectral performance of photovoltaic technology. Renew Sustain Energy Rev 141

    Google Scholar 

  • Freeman JM, Klise GT, Walker A, Lavrova O (2018) Evaluating energy impacts and costs from PV component failures. In: Proceedings of IEEE 7th world conference on photovoltaic energy conversion, pp 1761–1765

    Google Scholar 

  • Garoudja E, Harrou F, Sun Y, Kara K, Chouder A, Silvestre S (2017) Statistical fault detection in photovoltaic systems. Sol Energy 150:485–499

    Article  Google Scholar 

  • Gokmen N, Karatepe E, Celik B, Silvestre S (2012) Simple diagnostic approach for determining of faulted PV modules in string based PV arrays. Sol Energy 86:3364–3377

    Article  Google Scholar 

  • Gokmen N, Karatepe E, Silvestre S, Celik B, Ortega P (2013) An efficient fault diagnosis method for PV systems based on operating voltage-window. Energy Convers Manag 73:350–360

    Google Scholar 

  • Golnas A (2012) PV system reliability: an operator’s perspective. In: Proceedings of IEEE 38th photovoltaic specialists conference (PVSC), pp 1–6

    Google Scholar 

  • Aureliano Gomes de Brito M, Galotto L, Poltronieri Sampaio L, Dazevedo Melo G, Canesin CA (2013) Evaluation of the main MPPT techniques for photovoltaic applications. IEEE Trans Industr Electron 60:1156–1167

    Google Scholar 

  • Gong X, Wang N, Zhang Y, Yin S, Wang M, Wu G (2020) Fault diagnosis of micro grid inverter based on wavelet transform and probabilistic neural network. In: Proceedings of 39th Chinese control conference (CCC), pp 4078–4082

    Google Scholar 

  • González-Longatt FM (2005) Model of photovoltaic module in Matlab. In: The proceedings of the second Ibero-American conference of electrical, electronics and computation students, pp 1–5

    Google Scholar 

  • Grimaccia F, Leva S, Niccolai A (2017) PV plant digital mapping for modules’ defects detection by unmanned aerial vehicles. IET Renew Power Gener 11(10):1221–1228

    Google Scholar 

  • Gunda T, Jones CB (2019) Data-driven analysis of PV failures from O&M records. In: Proceedings of renewables O&M innovation workshop, Charlotte, NC, USA, Sandia National Lab. (SNL-NM)

    Google Scholar 

  • Hacke P, Lokanath S, Williams P, Vasan A, Sochor P, TamizhMani G, Shinohara H, Kurtz S (2018) A status review of photovoltaic power conversion equipment reliability, safety, and quality assurance protocols. Renew Sustain Energy Rev 82:1097–1112

    Article  Google Scholar 

  • Hamied A, Mellit A, Zoulid M, Birouk R (2018) IoT-based experimental prototype for monitoring of photovoltaic arrays. In: 2018 international conference on applied smart systems (ICASS), Medea, Algeria, pp 1–5. https://doi.org/10.1109/ICASS.2018.8652014

  • Hans MR, Tamhane MA (2020) IoT based hybrid green energy driven street lighting system. In: 2020 fourth international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, pp 35–41. https://doi.org/10.1109/I-SMAC49090.2020.9243365

  • Hariharan R, Chakkarapani M, Ilango GS, Nagamani C (2016) A method to detect photovoltaic array faults and partial shading in PV systems. IEEE J Photovoltaics 6(5):1278–1285. https://doi.org/10.1109/JPHOTOV.2016.2581478

    Article  Google Scholar 

  • Harrou F, Taghezouit B, Sun Y (2019) Improved k NN-based monitoring schemes for detecting faults in PV systems. IEEE J Photovoltaics 9(3):811–821

    Article  Google Scholar 

  • Honrubia-Escribano A, García-Sánchez T, Gómez-Lázaro E, Muljadi E, MolinaGarcía A (2015) Power quality surveys of photovoltaic power plants: characterisation and analysis of grid-code requirements. Renew Power Gener IET 9:466–473

    Article  Google Scholar 

  • Hooshyar A, El-Saadany EF, Sanaye-Pasand M (2016) Fault type classification in microgrids including photovoltaic DGs. IEEE Trans Smart Grid 7(5):2218–2229

    Article  Google Scholar 

  • Hopwood MW, Gunda T, Seigneur H, Walters J (2020) Neural network based classification of string-level IV curves from physically-induced failures of photovoltaic modules. IEEE Access 8:161480–161487

    Article  Google Scholar 

  • Hu Y, Cao W, Wu J, Ji B, Holliday D (2014) Thermography-based virtual MPPT scheme for improving PV energy efficiency under partial shading conditions. IEEE Trans Power Electron 29(11):5667–5672

    Google Scholar 

  • Hu Y, Zhang J, Cao W, Wu J, Tian GY, Finney SJ, Kirtley JL (2015) Online two-section PV array fault diagnosis with optimized voltage sensor locations. IEEE Trans Ind Electron 62(11):7237–7246

    Google Scholar 

  • IEA (2020) Renewables 2020. IEA, Paris. https://www.iea.org/reports/renewables-2020

  • Jain P, Poon J, Singh JP, Spanos C, Sanders SR, Panda SK (2019) A digital twin approach for fault diagnosis in distributed photovoltaic systems. IEEE Trans Power Electron 35(1):940–956

    Google Scholar 

  • Jaraniya D, Nema RK, Gawre SK (2020) Design and simulation of power electronics interface for modified P & O maximum power point tracking under suddenly varying irradiance. In: 2020 IEEE international students’ conference on electrical, electronics and computer science (SCEECS), pp 1–6

    Google Scholar 

  • Jordan DC, Marion B, Deline C, Barnes T, Bolinger M (2020) PV field reliability status—analysis of 100 000 solar systems. Prog Photovoltaics Res Appl 28(8):739–754

    Google Scholar 

  • Kajari-Schröder S, Kunze I, Köntges M (2012) Criticality of cracks in PV modules. Energy Procedia 27:658–663

    Article  Google Scholar 

  • Karmacharya IM, Gokaraju R (2017) Fault location in ungrounded photovoltaic system using wavelets and ann. IEEE Trans Power Del 33(2):549–559

    Google Scholar 

  • Karmacharya IM, Gokaraju R (2018) Fault location in ungrounded photovoltaic system using wavelets and ANN. IEEE Trans Power Del 33(2):549–559

    Article  Google Scholar 

  • Katoch S et al (2018) Shading prediction, fault detection, and consensus estimation for solar array control. In: 2018 IEEE industrial cyber-physical systems (ICPS), St. Petersburg, Russia, 2018, pp 217–222. https://doi.org/10.1109/ICPHYS.2018.8387662

  • Kekre A, Gawre SK (2017) Solar photovoltaic remote monitoring system using IOT. In: 2017 international conference on recent innovations in signal processing and embedded systems (RISE), Bhopal, India, pp 619–623. https://doi.org/10.1109/RISE.2017.8378227

  • Khoshnami A, Sadeghkhani I (2018) Sample entropy-based fault detection for photovoltaic arrays. IET Renew Power Gener 12(16):1966–1976

    Article  Google Scholar 

  • Klein K, Langner R, Kalz D, Herkel S, Henning HM (2016) Grid support coefficients for electricity-based heating and cooling and field data analysis of present-day installations in Germany. Appl Energy 162:853–867

    Article  Google Scholar 

  • Klise KA (2016) Performance monitoring using Pecos. Sandia National Lab. (SNL-NM), Albuquerque, NM, USA, Tech. Rep. SAND2016–4303C

    Google Scholar 

  • Koutroulis E, Blaabjerg F (2012) A new technique for tracking the global maximum power point of PV arrays operating under partial-shading conditions. IEEE J Photovolt 2:184–190

    Article  Google Scholar 

  • Kumar A, Gawre SK, Sarkar M, Gosula S (2018) A real-time comparative data analysis of different types of solar panels during partial shading with distinct tilt angles. In: 2018 15th IEEE India council international conference (INDICON), pp 1–6. https://doi.org/10.1109/INDICON45594.2018.8987115

  • Kurukuru VSB, Haque A, Khan MA, Tripathy AK (2019) Fault classification for photovoltaic modules using thermography and machine learning techniques. In: 2019 international conference on computer and information sciences (ICCIS), Sakaka, Saudi Arabia, pp 1–6. https://doi.org/10.1109/ICCISci.2019.8716442

  • Li Z, Yu Y, Wu C, Yang Z, Meng J (2019) Detection of high-impedance line-line fault in photovoltaic arrays based on voltage divider. In: 2019 IEEE sustainable power and energy conference (iSPEC), pp 786–791. IEEE

    Google Scholar 

  • Li X, Yang Q, Lou Z, Yan W (2019) Deep learning based module defect analysis for large-scale photovoltaic farms. IEEE Trans Energy Convers 34(1):520–529

    Google Scholar 

  • Lin H, Chen Z, Wu L, Lin P, Cheng S (2015) On-line monitoring and fault diagnosis of PV array based on BP neural network optimized by genetic algorithm. Multi-disciplinary trends in artificial intelligence. Springer International Publishing, pp 102–112

    Google Scholar 

  • Livera A, Theristis M, Makrides G, Georghiou GE (2019) Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems. Renew Energy 133:126–143

    Article  Google Scholar 

  • Livera A, Theristis M, Koumpli E, Theocharides S, Makrides G, Sutterlueti J, Stein JS, Georghiou GE (2020) Data processing and quality verification for improved photovoltaic performance and reliability analytics. Prog Photovoltaics Res Appl

    Google Scholar 

  • Lu H, Lu L, Wang Y (2016) Numerical investigation of dust pollution on a solar photovoltaic (PV) system mounted on an isolated building. Appl Energy 180:27–36

    Article  Google Scholar 

  • Maki A, Valkealahti S (2012) Power losses in long string and parallel-connected short strings of series-connected silicon-based photovoltaic modules due to partial shading conditions. IEEE Trans Energy Convers 27:173–183

    Article  Google Scholar 

  • Malhotra R (2015) A systematic review of machine learning techniques for software fault prediction. Appl Soft Comput J 27:504–518

    Article  Google Scholar 

  • Mansouri MM, Hadjeri S, Brahami M (2021) New method of detection, identification, and elimination of photovoltaic system faults in real time based on the adaptive Neuro-fuzzy system. IEEE J Photovoltaics 11(3):797–805. https://doi.org/10.1109/JPHOTOV.2021.3051145

    Article  Google Scholar 

  • Mellit A, Hamied A, Lughi V, Pavan AM (2020) A low-cost monitoring and fault detection system for stand-alone photovoltaic systems using IoT technique. In: Zamboni W, Petrone G (eds) ELECTRIMACS 2019. Lecture notes in electrical engineering, vol 615. Springer, Cham. https://doi.org/10.1007/978-3-030-37161-6_26

  • Meyer S et al (2013) Snail trails: root cause analysis and test procedures. Energy Procedia 38:498–505

    Article  Google Scholar 

  • Murtaza AF, Bilal M, Ahmad R, Sher HA (2019) A circuit analysis based fault finding algorithm for photovoltaic array under LL/LG faults. IEEE J Emerg Sel Top Power Electron 1–1

    Google Scholar 

  • Naveen Venkatesh S, Sugumaran V (2021) Fault diagnosis of visual faults in photovoltaic modules: a review. Int J Green Energy 18(1):37–50. https://doi.org/10.1080/15435075.2020.1825443

    Article  Google Scholar 

  • Nie J, Luo T, Li H (2020) Automatic hotspots detection based on UAV infrared images for large-scale PV plant. Electron Lett 56(19):993–995

    Google Scholar 

  • Oprea S-V, Bâra A, Preoţescu D, Elefterescu L (2019) Photovoltaic power plants (PV-PP) reliability indicators for improving operation and maintenance activities. A case study of PV-PP Agigea located in Romania. IEEE Access 7:39142–39157

    Article  Google Scholar 

  • Pereira RIS, Jucá SCS, Carvalho PCM, Souza CP (2019) IoT network and sensor signal conditioning for meteorological data and photovoltaic module temperature monitoring. IEEE Lat Am Trans 17(06):937–944. https://doi.org/10.1109/TLA.2019.8896816

    Article  Google Scholar 

  • Peters L, Madlener R (2017) Economic evaluation of maintenance strategies for ground-mounted solar photovoltaic plants. Appl Energy 199:264–280

    Article  Google Scholar 

  • Phoolwani UK, Sharma T, Singh A, Gawre SK (2020) IoT based solar panel analysis using thermal imaging. In: 2020 IEEE international students’ conference on electrical, electronics and computer science (SCEECS), Bhopal, India, pp 1–5. https://doi.org/10.1109/SCEECS48394.2020.114

  • Pierdicca R, Malinverni ES, Piccinini F, Paolanti M, Felicetti A, Zingaretti P (2018) Deep convolutional neural network for automatic detection of damaged photovoltaic cells. Int Arch Photogramm Remote Sens Spatial Inf Sci 42:893–900

    Google Scholar 

  • Pillai DS, Rajasekar N (2018) An MPPT-based sensorless line–line and line–ground fault detection technique for PV systems. IEEE Trans Power Electron 34(9):8646–8659

    Google Scholar 

  • Pillai DS, Rajasekar N (2018) A comprehensive review on protection challenges and fault diagnosis in PV systems. Renew Sustain Energy Rev 91. https://doi.org/10.1016/j.rser.2018.03.082

  • Pillai DS, Natarajan R (2019) A compatibility analysis on NEC, IEC, and UL standards for protection against line-line and line-ground faults in PV arrays. IEEE J Photovoltaics 9(3):864–871

    Article  Google Scholar 

  • Planas E, Andreu J, Gárate JI, de Alegría IM, Ibarra E (2015) AC and DC technology in microgrids: a review. Renew Sustain Energy Rev 43:726–749

    Article  Google Scholar 

  • Platon R, Martel J, Woodruff N, Chau TY (2015) Online fault detection in PV systems. IEEE Trans Sustain Energy 2015:1200–1207

    Google Scholar 

  • Pradeep Kumar VVS, Fernandes BG (2017) A fault-tolerant single-phase grid-connected inverter topology with enhanced reliability for solar PV applications. IEEE J Emerg Sel Topics Power Electron 5(3):1254–1262

    Google Scholar 

  • Pradeep Kumar B, Saravana Ilango G, Jaya Bharata Reddy M, Chilakapati N (2017) Online fault detection and diagnosis in photovoltaic systems using wavelet packets. IEEE J Photovoltaics 8(1):257–265

    Google Scholar 

  • Rahman MRU, Chen H (2020) Defects inspection in polycrystalline solar cells electroluminescence images using deep learning. IEEE Access 8:40547–40558

    Google Scholar 

  • Rahmann C, Vittal V, Ascui J, Haas J (2016) Mitigation control against partial shading effects in large-scale PV power plants. IEEE Trans Sustain Energy 7:173–180

    Article  Google Scholar 

  • Ramkiran B, Sundarabalan CK, Sudhakar K (2020) Performance evaluation of solar PV module with filters in an outdoor environment, Case Stud Therm Eng 21

    Google Scholar 

  • Ristow A, Begovic M, Pregelj A, Rohatgi A (2008) Development of a methodology for improving photovoltaic inverter reliability. IEEE Trans Ind Electron 55(7):2581–2592

    Article  Google Scholar 

  • Roy S, Alam MK, Khan F, Johnson J, Flicker J (2017) An irradiance-independent, robust ground-fault detection scheme for PV arrays based on spread spectrum time-domain reflectometry (SSTDR). IEEE Trans Power Electron 33(8):7046–7057

    Google Scholar 

  • Saleh KA, Hooshyar A, El-Saadany EF, Zeineldin HH (2017) Voltage-based protection scheme for faults within utility-scale photovoltaic arrays. IEEE Trans Smart Grid 9(5):4367–4382

    Google Scholar 

  • Sangwongwanich A, Yang Y, Sera D, Blaabjerg F, Zhou D (2018) On the impacts of PV array sizing on the inverter reliability and lifetime. IEEE Trans Ind Appl 54(4):3656–3667

    Article  Google Scholar 

  • Seapan M, Hishikawa Y, Yoshita M, Okajima K (2020) Detection of shading effect by using the current and voltage at maximum power point of crystalline silicon PV modules. Sol Energy 211:1365–1372

    Article  Google Scholar 

  • SETO in 2020: a decade of progress, a promising future, DOE, New York, NY, USA (2010)

    Google Scholar 

  • SeyyedHosseini M, Yazdian-Varjani A, Mohamadian M (2020) IOT based multi agent micro inverter for condition monitoring and controlling of PV systems. In: 2020 11th power electronics, drive systems, and technologies conference (PEDSTC), Tehran, Iran, pp 1–6. https://doi.org/10.1109/PEDSTC49159.2020.9088449

  • Silvestre S, Aires da Silva M, Chouder A, Guasch D, Karatepe E (2014) New procedure for fault detection in grid connected PV systems based on the evaluation of current and voltage indicators. Energy Convers Manage 86:241–249

    Google Scholar 

  • Spataru S, Sera D, Kerekes T, Teodorescu R (2015) Diagnostic method for photovoltaic systems based on light I-V measurements. Sol Energy 119:29–44

    Article  Google Scholar 

  • Spertino F, Chiodo E, Ciocia A, Malgaroli G, Ratclif A (2019) Maintenance activity, reliability analysis and related energy losses in five operating photovoltaic plants. In: Proceedings of IEEE international conference on environment and electrical engineering and IEEE industrial and commercial power systems Europe, pp 1–6

    Google Scholar 

  • Sreelakshmy J, Pradeep Kumar B, Saravana Ilango G, Nagamani C (2018) Identification of faults in PV array using maximal overlap discrete wavelet transform. In: 2018 20th national power systems conference (NPSC), pp 1–6. IEEE

    Google Scholar 

  • Stember LH, Huss WR, Bridgman MS (1982) A methodology for photovoltaic system reliability & economic analysis. IEEE Trans Rel R–31(3):296–303

    Google Scholar 

  • Talayero AP, Llombart A, Melero JJ (2020) Diagnosis of failures in solar plants based on performance monitoring. Renew Energy Power Qual J 18:33–128

    Google Scholar 

  • Teo JC, Tan RHG, Mok VH, Ramachandaramurthy VK, Tan CK (2020) Impact of bypass diode forward voltage on maximum power of a photovoltaic system under partial shading conditions. Energy 191

    Google Scholar 

  • Tsanakas JA, Chrysostomou D, Botsaris PN, Gasteratos A (2015) Fault diagnosis of photovoltaic modules through image processing and canny edge detection on field thermographic measurements. Int J Sustain Energy 34(6):351–372

    Google Scholar 

  • Tyagi VV, Rahim NAA, Rahim NA, Selvaraj JAL (2013) Progress in solar PV technology: research and achievement. Renew Sustain Energy Rev 20:443–461. https://doi.org/10.1016/j.rser.2012.09.028

  • Wang W, Liu AC-F, Chung HS-H, Lau RW-H, Zhang J, Lo AW-L (2015) Fault diagnosis of photovoltaic panels using dynamic current–voltage characteristics. IEEE Trans Power Electron 31(2):1588–1599

    Google Scholar 

  • Wu J, Yan Z, Sun Q (2019) Multiple faults detection of three-level NPC inverter based on improved deep learning network. In: Proceedings of international conference on applications and techniques in cyber intelligence. Springer, pp 1575–1583 [Online]. http://link-springer-com-443.webvpn.fjmu.edu.cn/chapter/10.1007%2F978-3-030-25128-4_195#citeas

  • Yahyaoui I (2016) Specifications of photovoltaic pumping systems in agriculture: sizing, fuzzy energy management and economic sensitivity analysis. Book. ISBN: 9780128120392. Elsevier

    Google Scholar 

  • Yahyaoui I, Segatto MEV (2017) A practical technique for on-line monitoring of a photovoltaic plant connected to a single-phase grid. Energy Convers Manag 132. https://doi.org/10.1016/j.enconman.2016.11.031

  • Yahyaoui I, Tadeo F, Segatto MEV (2016) Control strategy for small-scale photovoltaic systems connected to single-phase grids. In: The proceeding of the international renewable energy congress. IREC, IEEE, pp 1–6

    Google Scholar 

  • Yi Z, Etemadi AH (2016) Fault detection for photovoltaic systems based on multi-resolution signal decomposition and fuzzy inference systems. IEEE Trans Smart Grid 8(3):1274–1283

    Google Scholar 

  • Zaki SA, Zhu H, Fakih MA, Sayed AR, Yao J (2021) Deep learning–based method for faults classification of PV system. IET Renew Power Gener 15:193–205

    Article  Google Scholar 

  • Zhang X, Sun H, Zhou Y, Xi J, Li M (2013) A novel method for surface defect detection of photovoltaic module based on independent component analysis. Math Problems Eng 2013

    Google Scholar 

  • Zhao Y, De Palma J-F, Mosesian J, Lyons R, Lehman B (2012) Line–line fault analysis and protection challenges in solar photovoltaic arrays. IEEE Trans Ind Electron 60(9):3784–3795

    Google Scholar 

  • Zhao Y, Lehman B, Ball R, Mosesian J, Palma J-F (2013) Outlier detection rules for fault detection in solar photovoltaic arrays. In: 2013 twenty-eighth annual IEEE applied power electronics conference and exposition (APEC), pp 2913–2920. IEEE

    Google Scholar 

  • Zhao Y, Balboni F, Arnaud T, Mosesian J, Ball R, Lehman B (2014) Fault experiments in a commercial-scale PV laboratory and fault detection using local outlier factor. In: 2014 IEEE 40th photovoltaic specialist conference (PVSC), pp 3398–3403. IEEE

    Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, MANIT, Bhopal, India

    Suresh Kumar Gawre

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  1. Suresh Kumar Gawre
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Correspondence to Suresh Kumar Gawre .

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  1. Department of Electrical Engineering, National Institute of Technology, Durgapur, West Bengal, India

    Aashish Kumar Bohre

  2. Department of Electrical Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India

    Pradyumn Chaturvedi

  3. Faculty of Engineering and Science, University of Agder, Kristiansand, Norway

    Mohan Lal Kolhe

  4. Department of Electrical Engineering, Indian Institute of Technology, Kanpur, Uttar Pradesh, India

    Sri Niwas Singh

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Gawre, S.K. (2022). Advanced Fault Diagnosis and Condition Monitoring Schemes for Solar PV Systems. In: Bohre, A.K., Chaturvedi, P., Kolhe, M.L., Singh, S.N. (eds) Planning of Hybrid Renewable Energy Systems, Electric Vehicles and Microgrid. Energy Systems in Electrical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-19-0979-5_3

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  • DOI: https://doi.org/10.1007/978-981-19-0979-5_3

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