Energy losses in photovoltaic generators due to wind patterns

The study investigates whether and how wind patterns (speed and direction) impact energy losses in large-scale photovoltaic (PV) generators through thermally induced mismatch losses (MML) between modules. While small-scale experiments often show wind-enhanced convective cooling that lowers PV temperatures and improves efficiency, the author hypothesizes that in large, real-world PV generators, wind can induce spatially nonuniform heat transfer and temperature gradients across modules, increasing operating-voltage dispersion and thus mismatch losses. The work contextualizes MML into intrinsic (due to manufacturing/electrical parameter dispersion) and extrinsic (due to environmental thermal variations driven by wind) components, noting an intrinsic MML of approximately 0.09% for the analyzed generator. The extrinsic component is expected to vary with wind patterns, potentially tripling the intrinsic value. The thermal behavior is framed by flat-plate fluid mechanics, where boundary-layer development and turbulence over large arrays produce position-dependent temperature distributions, potentially influenced by mounting structures and tracker configurations. The objective is to quantify the wind-induced thermal gradients, their effect on operating voltages, and the resulting MML at daily and monthly timescales, and to assess implications for energy estimates and plant lifespan.

Prior work on large PV systems’ thermal behavior has largely relied on computational fluid dynamics and wind-tunnel studies with reduced-scale panels due to difficulty controlling natural wind and environmental variables. These studies report that increasing convection (e.g., via panel tilt) enhances heat transfer and lowers panel temperatures. It is well established that higher cell temperature reduces conversion efficiency and accelerates PV ageing. Numerous PV/PVT studies have demonstrated cooling strategies (air/water cooling, spray cooling) that can improve module performance, but these results come from small-scale or building-integrated contexts where the module–air interface is too small to capture full airflow development under natural wind. In large generators, module temperature depends on its position and incident airflow, and increased wind does not necessarily yield better performance. Previous field studies focusing on energy output can mask intra-array temperature gradients that alter module electrical parameters. Earlier research identified temperature-gradient-induced circuit losses and the role of shunt resistance dispersion at low irradiance. Additionally, observations at large plants reported intra-array temperature spreads up to ~14 °C, with array geometry and tilt influencing turbulence, boundary-layer development, and temperature distribution. Overall, the literature suggests complex wind–thermal interactions in large PV arrays, but direct field evidence linking natural wind patterns to mismatch losses has been limited.

Site and array: Measurements were conducted on a PV generator at the Solar Energy Institute, Technical University of Madrid (40.39°N, 3.63°W). The array comprises 21 Siliken SLK60P6L monofacial multicrystalline BSF modules (245 Wp, 60 cells, 1.60 × 0.99 m), arranged as 3 parallel strings of 7 series-connected modules, forming a 10.89 × 3.20 m panel. An additional disconnected module closed the surface to prevent local turbulence. The array is south-oriented (azimuth 0°), tilted 30°, and has been grid-connected since March 2013.

Instrumentation and data collection: Module rear-surface temperatures and operating voltages were recorded with PT1000 sensors (class A, ±0.3 °C) and T-shaped connectors, using a 20-bit datalogger at 5-minute intervals. Data span February 25, 2017 to July 2, 2020. Four edge modules (M1–M4) were instrumented with three PT1000 sensors each on the rear side per IEC 62853 recommendations to capture intra-module ΔT. Measurements were synchronized with a nearby Geonica meteorological station (installed March 2013) recording wind speed/direction (1-minute resolution), and a reference module measuring effective irradiance and cell temperature. Data were limited to sunny, shading-free windows (roughly 08:30–13:45 UTC in winter, 07:00–14:45 UTC in summer). Periods when the array was in open circuit for maintenance or other experiments were excluded for the main MML analyses.

Additional thermal characterization: Transient cell temperature maps under slight wind variations were captured using a FLIR E60 thermographic camera (six images over 40 s; temperature scale 35–37 °C) on February 1, 2022, 11:30 UTC. One instrumented module (M3) was previously calibrated outdoors in a “solar box” to obtain STC parameters and thermal coefficients. During specific thermal-drop analyses, the instrumented module and the array were placed in open circuit to magnify thermal effects; module open-circuit temperature (T_VOC) and a rear PT1000 temperature (T_REF) were compared under different wind incidences alongside ambient air temperature (T_AIR).

Derived quantities: Irradiance G and cell temperature T_C were inferred from recorded I_SC and V_OC using IEC 60904-5 guidance: G = G_REF × I_SC / [I_SC,REF × (1 + α (T_C − T_C,REF))]; with I_SC,REF=8.44 A, α (I_SC thermal coeff.)=0.057%/°C, T_C,REF=25 °C; and T_VOC = T_C + β (V_OC − V_OC,REF) / (1 − α_ln × ln(G/G_REF)); with β (V_OC thermal coeff.)=−0.34%/°C, V_OC,REF=36.99 V, α_ln (ratio of thermal to open-circuit voltage)=0.045 for c-Si, and G_REF=1000 W m⁻².

Mismatch loss estimation: Since string current is common, inter-module differences manifest in operating voltage V_OP. The mean V_OP of the 21 modules was taken as an approximation of V at the MPP. For each module, ΔV_OP maps to a power deviation ΔP. Mismatch losses (MML) were derived from the relative dispersion CV_VOP (standard deviation of V_OP divided by mean V_OP) via an empirical quadratic fit independent of season: MML = a × CV_VOP + b × CV_VOP², with coefficients a≈0.002 and b≈0.17 determined experimentally for these modules. The total MML comprises an intrinsic component (manufacturing/electrical dispersion, taken constant short-term; ~0.09% for this generator) and an extrinsic component varying with wind-induced thermal heterogeneity.

Analysis framework: Daily mismatch losses (MML_DAY) were evaluated on sunny, shading-free days and cross-referenced with concurrent wind roses and wind-speed distributions to assess the influence of wind speed and incidence (frontal vs rear). Monthly mismatch losses (MML_MONTH) were examined over multi-year periods to assess seasonal and interannual patterns and their alignment with local wind regimes. Thermal-drop behavior (differences between T_VOC, T_REAR, and T_AIR) was analyzed under representative wind conditions (frontal, rear, low/null wind) to interpret mechanisms linking airflow regimes, boundary-layer development, and intra-array temperature gradients.

• Wind increases mismatch losses: Wind speed increases intra-array temperature differences (ΔT), which increase operating-voltage dispersion (ΔV_OP) and elevate mismatch losses beyond intrinsic levels. Extrinsic MML can roughly triple the intrinsic MML (~0.09%) in windy conditions.
• Daily MML by wind incidence: Frontal wind incidence (predominantly SW) produced MML_DAY ≈ 0.28%, whereas rear wind incidence (predominantly N) yielded MML_DAY ≈ 0.21%. On days with similar wind incidence but lower speeds, MML_DAY decreased (frontal ≈ 0.25%; rear ≈ 0.17%). Very low or null wind yielded MML_DAY ≈ 0.13%.
• Operating voltage dispersion: ΔV_OP decreased as irradiance increased, stabilizing for G ≥ 700 W m⁻² with typical dispersion ~0.5–2.0 V. Elevated ΔV_OP at low irradiance is consistent with shunt-resistance dispersion in multicrystalline BSF cells.
• Temperature patterns and fluid mechanics: Temperature distributions across the array match flat-plate boundary-layer theory. Turbulent zones exhibit viscous sublayer formation that limits convective heat transfer, keeping module temperatures higher. Low ΔT occurs where local heat-transfer variation is minimal; ΔT is higher near the beginning of flow where heat transfer changes rapidly. Observations are similar for parallel or diagonal wind incidence; perpendicular incidence yields more uniform temperatures across the generator.
• Thermal drops (T_VOC vs T_REAR vs T_AIR): In frontal high-wind cases, module temperatures were typically ~20 °C above T_AIR; in rear high-wind, both T_VOC and T_REAR approached T_AIR, sometimes <10 °C above; in calm conditions, PV temperatures were ~30 °C above T_AIR, and T_VOC ≈ T_REAR, coinciding with lower MML.
• Monthly and long-term patterns: MML_MONTH increased during summer (June–August) when higher SW winds prevail, and was lower in periods (Jan–Mar, Sep–Dec) with more frequent low or null winds. Interannual similarity of these patterns suggests a robust linkage to local wind regimes. Natural ageing contributed an additional MML_MONTH increase of about 0.04% per year for this generator.

The results support the hypothesis that natural wind patterns can increase mismatch losses in large PV generators by inducing nonuniform convective cooling and temperature gradients across modules. Boundary-layer development over the array, including laminar-to-turbulent transition and viscous sublayer effects, produces spatially varying heat-transfer coefficients that translate into module temperature differences, operating-voltage dispersion, and MML. Frontal wind incidence generally leads to higher MML than rear incidence, consistent with differing airflow development and heat-transfer variability across the array. Calm conditions minimize thermal gradients (T_VOC ≈ T_REAR across modules) and reduce MML toward the intrinsic baseline (~0.09%). These findings reconcile the apparent contradiction between small-scale PVT cooling benefits and large-array behavior: while cooling can lower absolute temperatures, heterogeneous airflow over utility-scale arrays introduces temperature nonuniformities that degrade electrical matching. The alignment of daily and monthly MML variations with wind roses and wind-speed distributions, and their reproducibility across years, underscores the importance of local wind climatology in energy yield and reliability assessments. The observed annual increase in MML_MONTH also indicates that ageing compounds wind-induced losses over plant lifetimes. Practically, windier sites, particularly with prevalent frontal incidences, may experience higher mismatch losses than calmer locations with similar irradiance, affecting bankability and long-term performance predictions.

The study demonstrates in real operating conditions that increasing wind speed can raise mismatch and energy losses in a large PV generator by inducing spatially variable heat transfer and temperature gradients across modules. Frontal wind incidence yields higher daily MML than rear incidence, and calm or low-wind conditions minimize losses. Monthly losses track local wind regimes, increasing during windier seasons, with an additional ~0.04% per year increase attributable to ageing. Implications include the need to explicitly incorporate local wind patterns into energy yield models and financial assessments over plant lifetimes. For sites with comparable irradiance, less windy locations may be preferable to reduce MML. Future research should resolve airflow–thermal fields at higher spatial and temporal resolution across full arrays, quantify the role of mounting/racking geometry and bifacial/tracker configurations, refine models linking wind regimes to MML in diverse climates, and explore array design/operational strategies to mitigate wind-induced mismatch.

• Single-site, single-array study in Madrid, Spain; generalization to other climates, array geometries, and technologies (e.g., bifacial, trackers) requires caution.
• Natural wind is inherently turbulent; the study cannot fully control or resolve fine-scale airflow features. The author notes that higher-resolution temperature measurements to capture subtle variations were out of scope.
• Data analysis restricted to sunny, shading-free time windows; periods with array open circuit for other activities were excluded from MML computation.
• MML derivation relies on empirical mapping from operating-voltage dispersion with coefficients calibrated for these specific modules; applicability to other module types may require recalibration.
• Temperature sensors measure discrete rear locations (and derived T_VOC); spatial temperature fields across cells/modules are inferred rather than fully mapped; PT1000 uncertainty ±0.3 °C.
• Conclusions about mechanisms (e.g., boundary-layer behavior) are supported by theory and observational patterns but not by direct flow-field measurements.