The efficiency of photovoltaic (PV) systems is significantly influenced by operating temperature. Previous research, primarily conducted on smaller, more controlled PV arrays, has demonstrated that increasing wind speed can enhance heat transfer from the panels to the surrounding air, leading to lower operating temperatures and consequently improved energy conversion efficiency. However, the impact of wind on larger-scale PV generators operating under real-world conditions remains less understood. This research addresses the gap by investigating the influence of natural wind patterns on the energy output of a large-scale PV generator currently connected to the grid. The primary research question revolves around determining whether the observed positive effects of wind on smaller-scale PV arrays are consistent in larger systems and what the nature and magnitude of potential energy losses are. Understanding this is crucial for accurate energy yield prediction and the overall economic viability of PV power plants. The importance of accurate estimations for project bankability, energy production forecasts, and system lifespan due to thermal stresses, all necessitate a detailed investigation into the effects of wind patterns. This research explores this complex relationship by examining mismatch losses, a direct measure of the impact of uneven temperature distribution and voltage variations within a PV generator. It seeks to provide a more comprehensive understanding of the impact of wind on large-scale PV systems, incorporating insights from fluid mechanics to explain the observed phenomenon and thus improving the accuracy of energy yield estimations.

Existing literature highlights the temperature dependence of PV module performance, with overheating reducing efficiency and accelerating aging. Studies on small-scale PV/thermal (PV/T) systems show that enhanced convection due to panel tilting increases heat transfer and reduces temperatures, seemingly improving efficiency. However, these studies are limited by the scale and controlled environment. While some research analyzed wind impact on real large-scale PV generator energy output, the effects of temperature differences within the system remained largely masked. Computational simulations and reduced-scale wind tunnel studies have been used to explore these effects but lack the complexity and variability of real-world conditions. The current state-of-the-art in large PV systems thermal analysis relies heavily on computational simulations and reduced scale experiments, thus failing to capture the intricate interplay between the wind and the heat distribution in a full-scale PV plant. This work aims to bridge this gap by analyzing data from a real-world operational PV generator, providing valuable insights into the complex relationship between wind and energy production.

The study was conducted at the Solar Energy Institute of the South Campus of the Technical University of Madrid, using a PV generator consisting of 21 Siliken SLK60P6L modules (245 Wp each). The generator is south-oriented (azimuth = 0°), tilted at 30°, and connected to the grid since March 2013. Temperature and voltage data were collected using PT1000 sensors and T-shaped connectors, with a 5-minute interval over a period of more than three years (February 25th, 2017, to July 2nd, 2020). The data were synchronized with a nearby meteorological station that provided wind speed, direction, effective irradiance, and cell temperature measurements. Four modules were instrumented with additional PT1000 sensors for detailed temperature profiling. One module was calibrated in a solar box to determine its characteristics under standard test conditions (STC) and its thermal coefficients. The mismatch losses (MML) were calculated from the operating voltage dispersion, using an equation derived from previous work. This methodology involved using the short-circuit current (*I_SC*) and open-circuit voltage (*V_OC*) to deduce irradiance (G) and cell temperature (*T_C*) using equations 2 and 3. The open-circuit voltage method was used to determine *T_C*. The calibrated data and the measured data were compared under different wind incidences using the temperature of the cell (*T_VOC*) and the temperature measured using PT1000 sensors (*T_REF*). The thermal coefficients were determined using a calibration process in a solar box. This allowed determination of the irradiance (G) and the cell temperature (*T_C*) based on measured *I_SC* and *V_OC*. The MML values were calculated from the operating voltage dispersion (CV_VOP) using equation 4, considering the intrinsic and extrinsic components. The data analysis focuses on the correlation between wind speed, direction, and MML, alongside temperature gradients within the PV generator. Transient temperature changes due to wind variations were also observed using a thermographic camera. The data analysis focused on correlating wind speed, direction, and MML, along with the temperature gradients within the PV generator. Transient temperature measurements due to small wind changes were taken with a thermal camera, helping to visualize the cooling effects of the wind.

The study revealed a counter-intuitive relationship between wind speed and energy production in the large-scale PV generator. Increased wind speeds led to increased mismatch losses (MML), reaching a maximum of 0.28% under high frontal wind incidence. This is attributed to uneven temperature distribution across the PV modules caused by variations in heat transfer due to the airflow patterns generated by the wind. The wind interacts with the PV generator inducing variations in the air flux that modify the heat transfer from the modules to the air. Frontal wind incidence resulted in higher MML (up to 0.28%) compared to rear incidence (0.21%). Lower wind speeds or even the absence of wind led to significantly lower MML (as low as 0.13%). The temperature differences (ΔT) within the PV generator were positively correlated with effective irradiance (G) and negatively correlated with the operating voltage difference (ΔV_OP). The temperature distribution within the PV generator followed patterns consistent with fluid mechanics theory for flat plates, with the warmest temperatures observed in turbulent zones where heat transfer was minimal. Analysis of the thermal drop between cell temperature (*T*_VOC) and back sheet temperature (*T*_REAR) showed a clear impact of wind on the internal heat flux discrepancy in the PV generator, emphasizing the interdependence of wind effects and internal heat flow. Long-term analysis (MML_MONTH) showed that monthly losses varied according to the prevailing wind patterns, which showed a consistent pattern year-on-year, indicating a strong link between local wind patterns and energy losses. Higher MML_MONTH values were observed during periods with high wind speeds from the southwest quadrant, while lower MML_MONTH was observed during periods with low or no wind. These observations consistently occurred for three consecutive years.

The findings challenge the assumption that increased wind speed always leads to improved PV performance. The observed energy losses in the large-scale PV generator demonstrate that the interaction between wind and the PV array is complex and not simply a matter of enhanced cooling. The observed temperature variations are highly dynamic and driven by the varying airflow patterns around and within the PV panels. The results highlight the importance of considering local wind patterns for accurate energy yield estimations and life-cycle assessments. The interplay between internal heat flux and wind-induced temperature gradients is critical in determining overall efficiency. The fluid mechanics-based explanation for the observed temperature distributions provides a solid theoretical foundation for the observed energy losses. The significant impact of wind patterns on long-term energy production necessitates incorporating wind data into PV power plant design and financial modelling to improve forecasting accuracy and minimize risk.

This study demonstrates that wind patterns significantly impact the energy production of large-scale PV generators, leading to unexpected energy losses due to uneven temperature distributions and subsequent mismatch losses. Frontal wind incidences result in greater losses compared to rear incidences, and low or absent wind leads to minimal losses. The observed thermal behavior is directly linked to airflow properties. The research highlights the need to integrate wind patterns into long-term energy yield estimations and PV plant design considerations, particularly given the increasing frequency of extreme weather events associated with climate change. Future research could investigate strategies to mitigate these losses, perhaps through optimized PV panel design or layout changes that take into account the wind conditions and their effects on the heat distribution.

The study focuses on a single PV generator in a specific geographical location. Generalizing these findings to other locations and PV system configurations requires further research. While the methodology used for MML calculation is accurate, the underlying model relies on certain assumptions about the PV module behavior. The study also does not account for other factors that might affect PV performance, such as soiling or shading. These effects could potentially interact with the wind-induced temperature variations, although these were minimal due to the location chosen and the operational period.