Refine
Document Type
- Conference Paper (3)
- Article (1)
Is part of the Bibliography
- no (4)
Keywords
- Field measurement (2)
- Solar modules (2)
- Degradation (1)
- Elektrolumineszenz (1)
- Field Measurement (1)
- LID (1)
- Langzeitstudie (1)
- Modulalterung (1)
- PID (1)
- Performance Ratio (1)
Institute
Photovoltaikmodule unterliegen im Betrieb verschiedenen Degradationsmechanismen, die zu Leistungsverlusten führen. Diese Studie[1] untersucht die tatsächliche Degradation ausgewählter Solarmodule der Photovoltaikanlage an der Westfälischen Hochschule Gelsenkirchen über einen Zeitraum von bis zu 23 Jahren[2]. Ziel ist es, die Langzeitstabilität verschiedener Modultechnologien unter realen Betriebsbedingungen zu evaluieren und Aussagen über die Qualität der Degradation zu treffen.
Die Untersuchung umfasst PV-Installationen mit kristallinen Modulen (mono- und polykristallin), Dünnschichtmodulen (amorphes und mikrokristallines Silizium) sowie innovativen Heterojunction-Modulen (Sanyo HIT). Nach Auswertung der Ertragsdaten und Performance Ratio wurden zehn Module aus den einzelnen Anlagen selektiert und im Solarlabor mittels Sonnensimulator unter Standard-Testbedingungen (STC) vermessen. Ergänzende Elektrolumineszenz- und Thermographie-Analysen ermöglichten die Identifikation von Mikrodefekten, Zellrissen und Hot-Spots.
Die Ergebnisse bezogen auf die Laborleistungsmessung zeigen Degradationsraten zwischen 0,16% und 0,65% pro Jahr, abhängig von der Modultechnologie. Polykristalline PeakonP-Module weisen durchschnittliche Verluste von 0,53% jährlich auf. Monokristalline Solon Black 230 Module zeigen geringere Degradation (0,37% p.a.), während rückseitenkontaktierte Solon Black 280 Module geringe Werte erreichen (0,16% p.a.). Heterojunction-Module (Sanyo HIT) zeigen eine Degradation von 0,64% p.a. Die Hauptdegradationsursachen sind lichtinduzierte Degradation (LID), potentialinduzierte Degradation (PID), Zellrisse und Delaminationen. Die meisten Module erfüllen oder übertreffen die Herstellergarantien von 90% nach 10 Jahren.
[1] Die in diesem Beitrag präsentierten Ergebnisse basieren auf der Bachelorarbeit von Daniel Westers, die im FB 2 (Lehreinheit Elektrotechnik) der Westfälischen Hochschule im Jahr 2021 im Solarlabor unter Betreuung von Prof. Dr. Andreas Schneider durchgeführt wurde.
[2] Alle zeitlichen Angaben in dieser Arbeit beziehen sich auf das Jahr der Bearbeitung der Bachelorarbeit.
The precision of yield calculation of modern design and simulation software for photovoltaic systems strongly rely, beside the accuracy of the specified module and inverter data, on the quality of the weather data. Since data from weather stations is not available for most locations world-wide this data is calculated by using modern interpolation methods. Beside this, simulation software typically uses historical weather data. In this work the mismatch of yield simulation results based on proprietary data, meaning interpolated or also called synthetical data, and data coming from a weather station in proximity to the installation is evaluated. The simulated data sets are compared to measurement data as obtained by the inverter output and hence give a profound understanding how interpolated data may influence the simulation results. The outcome shows that the quality of the yield simulation, if compared to the measurement data, is increased by a factor of up to four if on-site weather data is used as input for the simulation. The largest source of deviation is irradiation, which varies up to 10% if synthetical and measured irradiation on-site is compared. The second largest sources for simulation mismatches are power calculation and module temperature correction.
In this work a mathematical approach to calculate solar panel temperature based on measured irradiance, temperature and wind speed is applied. With the calculated module temperature, the electrical solar module characteristics is determined. A program developed in MatLab App Designer allows to import measurement data from a weather station and calculates the module temperature based on the mathematical NOCT and stationary approach with a time step between the measurements of 5 minutes. Three commercially available solar panels with different cell and interconnection technologies are used for the verification of the established models. The results show a strong correlation between the measured and by the stationary model predicted module temperature with a coefficient of determination R2 close to 1 and a root mean square deviation (RMSE) of ≤ 2.5 K for a time period of three months. Based on the predicted temperature, measured irradiance in module plane and specific module information the program models the electrical data as time series in 5-minute steps. Predicted to measured power for a time period of three months shows a linear correlation with an R2 of 0.99 and a mean absolute error (MAE) of 3.5, 2.7 and 4.8 for module ID 1, 2 and 3. The calculated energy (exemplarily for module ID 2) based on the measured, calculated by the NOCT and stationary model for this time period is 118.4 kWh, resp. 116.7 kWh and 117.8 kWh. This is equivalent to an uncertainty of 1.4% for the NOCT and 0.5% for the stationary model.
Advanced Determination of Temperature Coefficients of Photovoltaic Modules by Field Measurements
(2023)
Abstract
In this work data from outdoor measurements, acquired over the course of up to three years on commercially available solar panels, is used to determine the temperature coefficients and compare these to the information as stated by the producer in the data sheets. A program developed in MatLab App Designer allows to import the electrical and ambient measurement data. Filter algorithms for solar irradiance narrow the irradiance level down to ~1000 W/m2 before linear regression methods are applied to obtain the temperature coefficients. A repeatability investigation proves the accuracy of the determined temperature coefficients which are in good agreement to the supplier specification if the specified values for power are not larger than -0.3%/K. Further optimization is achieved by applying wind filter techniques and days with clear sky condition. With the big (measurement) data on hand it was possible to determine the change of the temperature coefficients for varying irradiance. As stated in literature we see an increase of the temperature coefficient of voltage and a decline for the temperature coefficient of power with increasing irradiance.
