@article{HuettlGottschalkSchneideretal.2019, author = {H{\"u}ttl, Bernd and Gottschalk, L. and Schneider, S. and Pflaum, D. and Schulze, Achim}, title = {Accurate Performance Rating of Photovoltaic Modules under Outdoor Test Conditions}, series = {Solar Energy}, volume = {2019}, journal = {Solar Energy}, number = {177}, pages = {737 -- 745}, year = {2019}, abstract = {Outdoor performance analyses of photovoltaic modules can be advantageous compared to indoor investigations, as they take into account the influences of natural test conditions on the modules. However, such outdoor performance assessments usually suffer from poor accuracies due to undefined test conditions for the modules. This paper reports on a comprehensive concept for improved outdoor analysis which results in performance data with indoor laboratory precision. The approach delivers current-voltage characteristics for even more test conditions than required by the standard IEC 61853-1. Hence, curves of modules' electrical parameters above irradiance can be deduced for any temperatures. The concept allows precise determination of temperature coefficients for user-defined irradiances taking into account outdoor effects like light-soaking or light-induced degradation. The calibration and measurement uncertainty of the presented outdoor analysis method is evaluated quantitatively. For the measurements an advanced outdoor set-up was used.}, language = {en} } @inproceedings{SchneiderHuettlGerstenbergetal.2019, author = {Schneider, S. and H{\"u}ttl, B. and Gerstenberg, L. and Wesselak, V. and Voswinckel, S. and Schulze, Achim}, title = {PV-FeldLab -Feldlabor f{\"u}r Vorort-Leistungsbestimmungen und Alterungsidentifikation von Photovoltaikgeneratoren}, series = {2. Regenerative Energietechnik Konferenz in Nordhausen}, booktitle = {2. Regenerative Energietechnik Konferenz in Nordhausen}, publisher = {RET.com 2019}, address = {Nordhausen}, year = {2019}, abstract = {Ziel des Projektes ist es, ein Verfahren f{\"u}r elektrische Vorort-Leistungsanalysen von Photovoltaik (PV)-Anlagen zu entwickeln. Dazu werden die elektrischen Parameter von PV-Strings lokal und global mit Hilfe eines neuen Feldlaboransatzes mit Indoor-Laborgenauigkeit vermessen. Das neue Messverfahren ist normgerecht und wird helfen, die Standardisierung von Feldmessungen weiterzuentwickeln. Im Projekt werden bekannte Messmethoden der Photovoltaik, (IR-Thermografie, I-U-String-Messung, digitale Datenverarbeitung mittels Selbstreferenzierung) zu einem innovativen Gesamtkonzept synthetisch zusammengef{\"u}hrt. Die Entwicklung des Messverfahrens wird an speziell gealterten PV-Anlagen marktbestimmender Technologien (c-Si-, CdTe-, CIGS-Muster-PV-Anlagen) vorangetrieben. So wird das Verfahren auf Erkennung spezifischer Degradationsszenarien und marginaler quantitativer Parameterver{\"a}nderungen trainiert und gesch{\"a}rft}, language = {de} } @inproceedings{SchneiderPflaumGottschalketal.2018, author = {Schneider, S. and Pflaum, D. and Gottschalk, L. and Schulze, Achim and H{\"u}ttl, B.}, title = {Laborgenaue elektrische Charakterisierungen von PV-Modulen unter Feldbedingungen}, series = {1. Regenerative Energietechnik Konferenz in Nordhausen}, booktitle = {1. Regenerative Energietechnik Konferenz in Nordhausen}, publisher = {RET.con 2018}, address = {Nordhausen}, year = {2018}, abstract = {Outdoor-I-U-Messungen an Photovoltaik- (PV) - Modulen haben den Vorteil, dass Effekte wie „light soaking" oder „light-induced degradation" unter nat{\"u}rlichen Bedingungen untersucht werden k{\"o}nnen. Solche Outdoor-Analysen k{\"o}nnen unter Anwendung des Selbstreferenzierungsalgorithmus (SRA) die Zuverl{\"a}ssigkeit von Indoor-Flasher-Messungen erreichen. Dieses neue Konzept liefert I-UEigenschaften f{\"u}r wesentlich mehr Testbedingungen als vom Standard IEC 61853 gefordert und so k{\"o}nnen Schwachlichtkurven aller relevanten elektrischen I-U-Parameter f{\"u}r bestimmte Temperaturen erzeugt werden. Wir berichten {\"u}ber experimentelle Ergebnisse einer 6-t{\"a}gigen Outdoor-Messkampagne an D{\"u}nnschicht-PV-Modulen. Wir evaluieren dar{\"u}ber hinaus die M{\"o}glichkeiten, Temperaturkoeffizienten, STC- und NOCT-Daten zu bestimmen und I-U-Kennlinien als Basis f{\"u}r Fitting-Analysen abzuleiten. Abschließend wird die Genauigkeit der neuen Outdoor-Methode untersucht.}, language = {de} } @inproceedings{HuettlHeimfarthSchulzeetal.2012, author = {H{\"u}ttl, B. and Heimfarth, J.P. and Schulze, Achim and Schelhase, S. and Hafemeister, M. and Weiler, U. and Queck, M. and Patzlaff, T. and Bauer, M. and Becker, F.}, title = {Improved JV-Analysis on CdTe Dot Cells by Controlled Series Resistance}, series = {Proc. 27th European Photovoltaic Solar Energy Conference and Exhibition}, booktitle = {Proc. 27th European Photovoltaic Solar Energy Conference and Exhibition}, publisher = {27th European Photovoltaic Solar Energy Conference and Exhibition}, year = {2012}, abstract = {CdS/CdTe thin film solar technology is one of the most promising concepts to accomplish high efficiencies and low costs in mass production. Further knowledge about electric parameters, such as serial resistances and recombination losses, is needed in order to improve in a systematic approach the module efficiency. The basic tool for device evaluation is the JV-measurement (current density - voltage) under standard test conditions. JVmeasurements result in rather direct determination of electric parameters, and detailed JV-analysis in indirect determination of some more relevant electric parameters. For thin film solar cells the usual 2-diode model and the responsible equation can be reduced to a 1-diode model due to significant recombination processes in the space charge region. However, the fit results suffer on imprecision of derived recombination currents (Jrec), and series resistance (Rs), due to their ambiguousness in fit routine. Direct and precise access of Rs by measurements fails for CdTe, due to its significant dependency on illumination levels. In this work we present a new method for precise Rs and Jrec determination of CdTe cells.}, language = {en} } @inproceedings{HuettlWachterGottschalketal.2016, author = {H{\"u}ttl, B. and Wachter, M. and Gottschalk, L. and Schulze, Achim and Becker, F.}, title = {Self Reference Algorithm for Precise Outdoor Characterization of PV Modules}, series = {PV-Days Halle 2016}, booktitle = {PV-Days Halle 2016}, publisher = {PV-Days Halle 2016}, address = {Halle}, year = {2016}, abstract = {Outdoor or just on site analyzes of photovoltaic modules can offer some advantages in comparison to indoor Lab stud-ies. In this way the electrical performance is monitored and logged under natural operating conditions in full range of irradiation and temperature. The entirety of monitored IV data allows deriving low-light characteristics of electric parameters, temperature coefficients and root cause analysis for possible degradation. We apply so-called self-reference algorithm in order to increase the accuracy of analysis. The improved precision is due to application of effective acting irradiation and effective temperature instead of externally measured data. Effective irradiation is determined in self-referencing scheme: the short circuit current of the module is assigned to the irradiation.}, language = {en} } @inproceedings{WachterGottschalkSimmleretal.2016, author = {Wachter, M. and Gottschalk, L. and Simmler, M. and Schulze, Achim and Becker, F. and Sayala, M. and H{\"u}ttl, B.}, title = {Short Circuit Current Measurements at Clear-Sky Conditions on Photovoltaic Modules: Basic for a Reliable Self-Reference Algorithm}, series = {Proc. 32nd European Photovoltaic Solar Energy Conference and Exhibition (PVSEC)}, booktitle = {Proc. 32nd European Photovoltaic Solar Energy Conference and Exhibition (PVSEC)}, publisher = {32nd European Photovoltaic Solar Energy Conference and Exhibition (PVSEC)}, year = {2016}, abstract = {Outdoor or just on site analyzes of photovoltaic modules can offer some advantages in comparison to indoor Lab studies. In this way the electrical performance is monitored and logged under natural operating conditions in full range of irradiation and temperature. The entirety of monitored IV data allows deriving low-light characteristics of electric parameters, temperature coefficients and root cause analysis for possible degradation. We apply so-called self-reference algorithm in order to increase the accuracy of analysis. The improved precision is due to application of effective acting irradiation and effective temperature instead of externally measured data. Effective irradiation is determined in self-referencing scheme: the short circuit current of the module is assigned to the irradiation. This assignment has to be calibrated, preferably at standard test conditions for irradiation. In this contribution we demonstrate a process of precise measurement of Isc under clear-sky outdoor conditions and the determination of the short circuit current for STC. The measurement method avoids errors by spectral deviations with respect to AM 1.5G spectrum, by optical reflection losses for high angles of incidence and errors by high albedo impact. In particular we investigate comparatively a CdTe module under outdoor conditions and by means a calibrated indoor Lab flasher.}, language = {en} } @inproceedings{SchulzePanuysenDaumeetal.2024, author = {Schulze, Achim and Panuysen, Markus and Daume, Darwin and Sch{\"o}nau, Maximilian}, title = {Quantitative Shade Detection for PV-Systems based on Clearsky Data}, series = {Proceedings of the 41st EU PVSEC}, booktitle = {Proceedings of the 41st EU PVSEC}, pages = {3}, year = {2024}, abstract = {In modern monitoring applications, losses from shading should be quantitatively separated from other loss mechanisms such as soiling, degradation or total failures. Based on clearsky reference data, we introduce a shading matrix which enables accurate hourly description of shading losses of PV strings over the year. This matrix can be used to improve failure detections and reference yield data in PV systems. Furthermore, a shading matrix with respect to sun position is introduced to improve the spatial understanding of shading caused by surrounding objects.}, language = {en} } @inproceedings{SchoenauDaumePanhuysenetal.2024, author = {Sch{\"o}nau, Maximilian and Daume, Darwin and Panhuysen, Markus and Kreller, T. and Jachmann, J. and Schulze, Achim and H{\"u}ttl, Bernd and Landes, Dieter}, title = {Hindcasting Solar Irradiance by Machine Learning using Photovoltaic Data}, series = {Proceedings of the 41st EU PVSEC}, booktitle = {Proceedings of the 41st EU PVSEC}, pages = {5}, year = {2024}, abstract = {This work introduces an innovative approach to calculate high-accuracy solar irradiance data for effective asset management of photovoltaic plants using Machine Learning. Ground-based pyranometers are expensive and seldom maintained, while weather service providers face limitations in spatial and temporal accuracy. A novel irradiance data model is introduced, that combines satellite weather information with data from PV plants to reconstruct historical irradiance levels with high accuracy. Our method uses existing PV arrays as "virtual sensors" to capture the local operating conditions, specifically the local irradiance incident on the array. The model was developed and validated using data from 43 medium to large-scale PV plants and two high-precision irradiance sensors. Results show superior performance compared to satellite weather data. With a root mean square deviation of 71 W/m² for global horizontal irradiation and 133 W/m² for direct normal irradiation with 5-minute resolution data, the model is about three times as accurate as the satellite weather prediction. This approach offers significant advantages in spatial resolution, reliability, and cost-effectiveness over conventional irradiance data by satellites or sensors. Utilizing SMARTBLUE AG'S dense network of thousands of monitored PV plants, the proposed methodology will enable the accurate prediction of irradiance in Germany, significantly enhancing asset management capabilities for PV plants.}, language = {de} } @inproceedings{SchoenauDaumePanhuysenetal.2024, author = {Sch{\"o}nau, Maximilian and Daume, Darwin and Panhuysen, Markus and Schulze, Achim and H{\"u}ttl, Bernd and Landes, Dieter}, title = {Verbesserte Clear-Sky-Erkennung durch hybrides Maschinelles Lernen}, series = {Proceedings of the 7th Ret.Con, Nordhausen}, booktitle = {Proceedings of the 7th Ret.Con, Nordhausen}, pages = {8}, year = {2024}, abstract = {Die pr{\"a}zise Erkennung von Clear-Sky-Momenten ist f{\"u}r die {\"U}berwachung und Effizienzana-lyse von Photovoltaikanlagen von zentraler Bedeutung, da zu diesen Zeitpunkten definierte und model-lierbare Einstrahlungsverh{\"a}ltnisse herrschen. Es wird ein hybrides Modell zur verbesserten Erkennung von Clear-Sky-Momenten auf Basis von Einstrahlungsdaten vorgestellt. Hierf{\"u}r wurden zun{\"a}chst ma-nuell, dann mithilfe eines CNNs Merkmale aus den Einstrahlungsdaten gebildet. Eine Falls tudie mit Referenzdaten belegt, dass durch die Kombination dieser wissens-und datengetriebenen Methoden Clear-Sky-Momente zuverl{\"a}ssiger identifiziert werden k{\"o}nnen. Dadurch k{\"o}nnen Analysemethoden schneller und zuverl{\"a}ssiger Aussagen {\"u}ber die untersuchten PV-Anlagen treffen.}, language = {de} } @article{SchoenauPanhuysenSonntagetal.2025, author = {Sch{\"o}nau, and Panhuysen, and Sonntag, and Banse, and Seel, and Jachmann, and Schulze, Achim and H{\"u}ttl, and Landes,}, title = {String outages in photovoltaic plants}, series = {Renewable Energies}, volume = {3}, journal = {Renewable Energies}, number = {1}, publisher = {Sage}, doi = {10.1177/27533735251347879}, pages = {12}, year = {2025}, abstract = {In this work, the factors leading to string outages are examined, and an enhanced method for detecting faults at the sub-string level is presented. Utilizing GPT4-o to analyze O\&M reports of 5089 photovoltaic plants, we classified outages according to the affected components and the underlying origin, identifying the most frequent string fault causes. An approach employing CUSUM Charts is introduced to identify substring outages within PV systems effectively. The methodology utilizes fundamental field data that is commonly available in practice. A filtering approach, combined with the use of CUSUM control charts, minimizes false positives, ensuring that only consistent underperformance is flagged as an out-age. The methodology returns far fewer false positives and more stable error intervals for substring outages than a former monitoring approach. Overall, the study demonstrates a significant improvement in detecting substring outages. The advanced methodology enables more effective O\&M for PV plants, where substring outages are reliably identified after a short detection time.}, language = {en} } @misc{SchoenauSchoenauDaumeetal.2024, author = {Sch{\"o}nau, Maximilian and Sch{\"o}nau, Elisabeth and Daume, Darwin and Panhuysen, Markus and Schulze, Achim and H{\"u}ttl, Bernd and Landes, Dieter}, title = {Improved Sampling of IV Measurements}, series = {Proceedings of the 41st EU PVSEC}, journal = {Proceedings of the 41st EU PVSEC}, publisher = {Proceedings of the 41st EU PVSEC}, doi = {10.4229/EUPVSEC2024/3AV.3.50}, pages = {3}, year = {2024}, abstract = {The measurement of IV curves is the most important characterization technique of photovoltaic devices. This work seeks to determine a fair sampling rate for IV curves. Traditional sampling methods, such as using equidistant voltage steps, result in biased data by over- and undersampling the measurement at different voltages, which affect critical evaluations such as maximum power point (MPP) determination as well as series and shunt resistance estimation. Therefore, an adaptive sampling approach based on calculating the arc length of the IV curve is proposed, aiming to distribute data points equidistantly along the curve. This method is intended to provide a more balanced representation of the measurement data across all segments of the curve. The results indicate that using this adaptive approach, the measurement points can be significantly reduced at lower voltages while maintaining essential data density at key characteristics such as the MPP, facilitating faster and more accurate assessments.}, language = {en} }