Fault Diagnostic Methodology for Grid-Connected Photovoltaic Systems

This research focuses on the design of a fault diagnosis methodology to contribute to the improvement of efficiency, maintainability and availability indicators of Grid-Connected Photovoltaic Systems. To achieve this, we start from the study of the mathematical model of the photovoltaic generator, then, a procedure is performed to quantify the operational losses of the photovoltaic generator and adjust the mathematical model of this to the real conditions of the system, through a polynomial adjustment. A real system of nominal power 7.5 kWp installed in the Solar Energy Research Center of the province of Santiago de Cuba is used to evaluate the proposed methodology. Based on the results obtained, the proposed approach is validated to demonstrate that it successfully supervises the system. The methodology was able to detect and identify 100% of the simulated failures and the tests carried out had a maximum false alarm rate of 0.22%, evidencing its capacity

SunDown: Model-driven Per-Panel Solar Anomaly Detection for Residential Arrays

There has been significant growth in both utility-scale and residential-scale solar installa- tions in recent years, driven by rapid technology improvements and falling prices. Unlike utility-scale solar farms that are professionally managed and maintained, smaller residential- scale installations often lack sensing and instrumentation for performance monitoring and fault detection. As a result, faults may go undetected for long periods of time, resulting in generation and revenue losses for the homeowner. In this thesis, we present SunDown, a sensorless approach designed to detect per-panel faults in residential solar arrays. SunDown does not require any new sensors for its fault detection and instead uses a model-driven ap- proach that leverages correlations between the power produced by adjacent panels to de- tect deviations from expected behavior. SunDown can handle concurrent faults in multiple panels and perform anomaly classification to determine probable causes. Using two years of solar generation data from a real home and a manually generated dataset of multiple solar faults, we show that our approach has a MAPE of 2.98% when predicting per-panel output. Our results also show that SunDown is able to detect and classify faults, including from snow cover, leaves and debris, and electrical failures with 99.13% accuracy, and can detect multi- ple concurrent faults with 97.2% accuracy

Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks

This article presents a methodology for automatic fault detection in photovoltaic arrays. Due to the great importance in the construction of increasingly robust photovoltaic plants, automatic fault detection has become a necessary tool to extend the useful life of these plants, avoid system shutdowns and reduce serious safety problems. In the present study, nine possible faults are detected, caused by malfunction in the bypass and blocking diodes. The solution consists of training two models based on artificial neural networks, the first model is a binary classifier that detects whether or not a fault occurs, the second is a multiclass classifier that detects the fault type. The obtained models were trained from simulation data, in an architecture of 9 photovoltaic panels interconnected in three rows by three columns matrix (extendable to larger systems). The evaluation shows that the prediction system has a total accuracy of 92.95%. Finally, this methodology is intended to be implemented in Colombia, in zones with difficult access and not interconnected to the electricity grid, seeking to reduce corrective maintenance

Methodology of fault diagnosis for grid-connected photovoltaic systems of network connection

[ES] Esta investigación tiene como objetivo el diseño de una metodología de diagnóstico de fallos para contribuir al mejoramiento de los indicadores de eficiencia, mantenimiento y disponibilidad de los Sistemas Fotovoltaicos de Conexión a Red (SFVCR). Para lograr dicho objetivo, se realiza el estudio del inversor de conexión a red y del modelo matemático del generador fotovoltaico. Luego se cuantifican las pérdidas operacionales del generador fotovoltaico y se adapta el modelo matemático de éste a las condiciones reales del sistema a través de un ajuste polinomial. Un sistema real de conexión a red de potencia nominal 7.5 kWp, instalado en el Centro de Investigaciones de Energía Solar (CIES) en la provincia Santiago de Cuba, se utiliza para evaluar la metodología propuesta. Con los resultados obtenidos se valida el diseño propuesto para demostrar que éste supervisa con éxito el SFVCR. La metodología fue capaz de detectar e identificar el 100 % de los fallos simulados y los ensayos realizado[EN] The aim of the present research work is the design of a methodology of fault diagnosis as a contribution to the improvement of indicators about efficiency, maintenance and availability of Photovoltaic Systems of Network Connection (PVSNC). The network connection inverter and the mathematical model of the Photovoltaic Generator were firstly analyzed. Afterwards, the existing operational losses of the Photovoltaic Generator were quantified, and the mathematical model was adapted to the real conditions of the System through a polynomial adjustment. A real network connection system of nominal power 7.5 kWp installed at the Research Center of Solar Energy, in the province of Santiago de Cuba, was used to assess the proposed methodology. The results obtained were validated to show that the proposed design successfully supervises the PVSNC.100% of the simulated faults were detected and identified with the designed methodology, whose usefulness was additionally shown when having a maximum rateNúñez A., J.; Benítez P., I.; Proenza Y., R.; Vázquez S., L.; Díaz M., D. (2020). Metodología de diagnóstico de fallos para sistemas fotovoltaicos de conexión a red. Revista Iberoamericana de Automática e Informática industrial. 17(1):94-105. https://doi.org/10.4995/riai.2019.11449OJS94105171Alam, M., Khan, F., Johnson, J. & Flicker, J., 2015. A comprehensive review of catastrophic faults in PV arrays: types, detection, and mitigation techniques. IEEE Journal of Photovoltaics 5(3):1-16. https://doi.org/10.1109/JPHOTOV.2015.2397599Berbesi, T. Aplicacion de técnicas robustas para detección y diagnóstico de fallos. 2012. Tesis Doctoral. Universidad de Valladolid, España.Brooks, B. 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Detection and Isolation of Faults on the Rotor Side Converter of Doubly Fed Induction Generators. Revista Iberoamericana de Automática e Informática Industrial,15(3), 297-308. ISSN: 1697-7912, https://doi.org/10.4995/riai.2017.9042Grimaldo Guerrero, J. W., Mendoza Becerra, M. A., Reyes Calle, W. P., 2017. Modelo para pronosticar la demanda de energía eléctrica utilizando los producto interno brutos sectoriales: Caso de Colombia. Revista Espacios Vol. 38 (22), 38.Guerrero, J. W. G., Toscano, A. D. R., Pacheco, L. V., Tovar, J. O., 2018. Analysis of the Energetic and Productive Effects Derived by the Installation of a Conveyor Belt in the Metal-mechanic Industry. International Journal of Energy Economics and Policy, 8(6), 196-201. https://doi.org/10.32479/ijeep.7066Houssein, N. Héraud, I. Souleiman and G. Pellet, "Monitoring and fault diagnosis of photovoltaic panels," 2010 IEEE International Energy Conference, Manama, 2010, pp. 389-394. https://doi.org/10.1109/ENERGYCON.2010.5771711Lorenzo, E., Martínez F., Muñoz, J., Narvarte, L. Predicción y ensayo de la producción de la energía FV conectada a la red. Era solar: Energías renovables, ISSN 0212-4157, Nº. 139, 2007, págs. 22-31Mekki, H., Mellit, A., Salhi & H.H. Artificial neural network-based modelling and fault detection of partial shaded photovoltaic modules. 2016. Simulation Modelling Practice and Theory, vol 67, p. 1-13. https://doi.org/10.1016/j.simpat.2016.05.005Meyer. E. L., Van Dyk, E. E. Assessing the reliability and degradation of photovoltaic module performance parameters, in IEEE Transactions on Reliability, vol. 53, no. 1, pp. 83-92, March 2004. https://doi.org/10.1109/TR.2004.824831Mikati, M., Santos, M., Armenta, C., 2013. Electric grid dependence on the configuration of a small-scale wind and solar power hybrid system. 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Metodología de diagnóstico de fallos para sistemas fotovoltaicos de conexión a red

The aim of the present research work is the design of a methodology of fault diagnosis as a contribution to the improvement of indicators about efficiency, maintenance and availability of Photovoltaic Systems of Network Connection (PVSNC). The network connection inverter and the mathematical model of the Photovoltaic Generator were firstly analyzed. Afterwards, the existing operational losses of the Photovoltaic Generator were quantified, and the mathematical model was adapted to the real conditions of the System through a polynomial adjustment. A real network connection system of nominal power 7.5 kWp installed at the Research Center of Solar Energy, in the province of Santiago de Cuba, was used to assess the proposed methodology. The results obtained were validated to show that the proposed design successfully supervises the PVSNC.100% of the simulated faults were detected and identified with the designed methodology, whose usefulness was additionally shown when having a maximum rate of 0.22% of false alarm in all the tests done.Esta investigación tiene como objetivo el diseño de una metodología de diagnóstico de fallos para contribuir al mejoramiento de los indicadores de eficiencia, mantenimiento y disponibilidad de los Sistemas Fotovoltaicos de Conexión a Red (SFVCR). Para lograr dicho objetivo, se realiza el estudio del inversor de conexión a red y del modelo matemático del generador fotovoltaico. Luego se cuantifican las pérdidas operacionales del generador fotovoltaico y se adapta el modelo matemático de éste a las condiciones reales del sistema a través de un ajuste polinomial. Un sistema real de conexión a red de potencia nominal 7.5 kWp, instalado en el Centro de Investigaciones de Energía Solar (CIES) en la provincia Santiago de Cuba, se utiliza para evaluar la metodología propuesta. Con los resultados obtenidos se valida el diseño propuesto para demostrar que éste supervisa con éxito el SFVCR. La metodología fue capaz de detectar e identificar el 100 % de los fallos simulados y los ensayos realizados tuvieron como máximo una tasa de falsa alarma de 0.22 %, evidenciándose su utilidad

Imputation of missing data in photovoltaic panel monitoring system

In scientific research, data acquisition and processing play a fundamental role. In photovoltaic systems, given their nature, this process presents deficiencies due to various factors such as the dispersion of the installed modules, climatic conditions or the amount of information that must be obtained, so the processes of data acquisition, storage and processing are very important. The present research developed a data acquisition, storage and processing system for photovoltaic systems, following the European standards IEC 60904 and IEC 61724 for data acquisition, Fog Computing for information storage and finally Machine Learning was used for processing. The results showed that the KNN-based model obtained a SCORE of 99.08%, MAE of 25.3 and MSE of 93.16. Concluding that the KNN-based model is the most robust model for data imputation in PV system monitoring

A review of automated solar photovoltaic defect detection systems : approaches, challenges, and future orientations

The development of Photovoltaic (PV) technology has paved the path to the exponential growth of solar cell deployment worldwide. Nevertheless, the energy efficiency of solar cells is often limited by resulting defects that can reduce their performance and lifespan. Therefore, it is crucial to identify a set of defect detection approaches for predictive maintenance and condition monitoring of PV modules. This paper presents a comprehensive review of different data analysis methods for defect detection of PV systems with a high categorisation granularity in terms of types and approaches for each technique. Such approaches, introduced in the literature, were categorised into Imaging-Based Techniques (IBTs) and Electrical Testing Techniques (ETTs). Although several review papers have investigated recent solar cell defect detection techniques, they do not provide a comprehensive investigation including IBTs and ETTs with a greater granularity of the different types of each for PV defect detection systems. Types of IBTs were categorised into Infrared Thermography (IRT), Electroluminescence (EL) imaging, and Light Beam Induced Current (LBIC). On the other hand, ETTs were categorised into Current-Voltage (I-V) characteristics analysis, Earth Capacitance Measurements (ECM), Time Domain Reflectometry (TDR), Power Losses Analysis (PLA), and Voltage and Current Measurements (VCM). Approaches based on digital/signal processing and Machine Learning (ML) models for each method are included where relevant. Moreover, the paper critically analyses the advantages and disadvantages of each of the adopted techniques, which can be referred to by future studies to identify the most suitable method considering the use-case’s requirements and setting. The adoption of each of the reviewed techniques depends on several factors, including the deployment scale, the targeted defects for detection, and the required location of defect analysis in the PV system, which are expanded further in the presented analysis. From a high-level perspective, while IBTs provide a high-resolution visual representation of the module surface, allowing for the detection and diagnosis of small structural defects that may be missed by other techniques, ETTs can detect electrical faults beyond the PV module’s surface. On the IBT level, the most notable adopted techniques in the literature are IRT- and EL-based. While IRT techniques are more practical for large-scale applications than EL imaging, the latter is considered a non-intrusive technique that is highly efficient in localising defects of solar cells. The paper also discusses challenges observed in the state-of-the-art related to data availability, real-time monitoring, accurate measurements, computational efficiency, and dataset distribution, and reviews data pre-processing and augmentation approaches that can address some of these challenges. Furthermore, potential future orientations are identified, addressing the limitations of PV defect detection systems

Propuesta metodológica para el tratamiento de datos para la evaluación del desempeño de sistemas fotovoltaicos. Caso de estudio: Tres sistemas fotovoltaicos de 1,5 kWac con tecnologías distintas, en el Campus-PUCP, en San Miguel

El presente trabajo busca proponer una metodología que permita asegurar la calidad de los registros de medición utilizados para evaluar un sistema fotovoltaico, de acuerdo a la norma IEC-61724. La evaluación, de acuerdo a la norma, requiere de un largo periodo experimental (al menos un año de medición). Durante este período, diversas fuentes de error pueden presentarse que afecten los indicadores de desempeño de la instalación. Para el caso del presente trabajo, la metodología propuesta fue implementada para evaluar de tres sistemas fotovoltaicos durante un periodo de dos años. La propuesta metodológica comprende la implementación de criterios de filtrado para detectar registros que no deben considerarse al momento de calcular indicadores. Para llegar a la serie de criterios utilizados, se revisaron en la bibliografía cuatro series de criterios, y el efecto de cada uno en los indicadores de desempeño fueron comparados. Adicionalmente, se propone una metodología para detectar puntos durante el registro en los que la instalación fotovoltaica se vio afectada por la presencia de sombras, basándose en la medición de irradiancia incidente en el plano de la instalación. Estos puntos no son detectados por criterios de filtrado convencionales, pero afectan significativamente los indicadores. Una vez que los registros que no deben ser utilizados son detectados y se toma en consideración posibles fallas en los sensores que ocurren durante un largo periodo de medición, se corre el riesgo que la cantidad de puntos correctos disponibles no sea suficiente para que los indicadores calculados sean representativos. La metodología propuesta incluye la implementación y evaluación de cuatro modelos de estimación de temperatura del módulo fotovoltaico, cuatro modelos de estimación de irradiancia en el plano de la instalación y cuatro modelos de estimación de potencia generada por la instalación fotovoltaica. Estos modelos fueron comparados entre ellos para identificar los más adecuados para corregir registros y completar puntos que no pudieron ser registrados. Finalmente, se presenta una comparación entre los resultados de calcular los indiciadores de desempeño de las tres instalaciones fotovoltaicas antes y después de haber implementado la propuesta metodológica. Con esto, se obtuvo una metodología replicable para otros estudios relacionados a instalaciones fotovoltaicas