Electroluminescence (EL) imaging is a photovoltaic (PV) module characterization technique, which provides high accuracy in detecting defects and faults, such as cracks, broken cells interconnections, shunts, among many others; furthermore, the EL technique is used extensively due to a high level of detail and direct relationship to injected carrier density. However, this technique is commonly practiced only indoors—or outdoors from dusk to dawn—because the crystalline silicon luminescence signal is several orders of magnitude lower than sunlight. This limits the potential of such a powerful technique to be used in utility scale inspections, and therefore, the interest in the development of electrical biasing tools to make outdoor EL imaging truly fast and efficient. With the focus of quickly acquiring EL images in daylight, we present in this article a drone-based system capable of acquiring EL images at a frame rate of 120 frames per second. In a single second during high irradiance conditions, this system can capture enough EL and background image pairs to create an EL PV module image that has sufficient diagnostic information to identify faults associated with power loss. The final EL images shown in this work reached representative quality ${rm{SNR_{AVG}}$ of 4.6, obtained with algorithms developed in previous works. These drone-based EL images were acquired with global horizontal solar irradiance close to one sun in the plane of the array.

The size and number of utility-scale bifacial photovoltaic (PV) installations has proliferated in recent years but concerns over modeling accuracy remain. The aim of this work is to provide the PV community with a validation study of eight tools used to simulate bifacial PV performance. We simulate real 26 kilowatt-peak (kW_p) bifacial arrays within a 420-kW_p site located in northern Europe (55.6° N, 12.1° E). The substructures investigated include horizontal single-axis trackers (HSATs) and fixed tilt racks that have dimensions analogous to those found in utility-scale PV installations. Each bifacial system has a monofacial reference system with similar front side power. We use on-site solar radiation (global, diffuse, and beam) and albedo measurements from spectrally flat class A sensors as inputs to the simulation tools, and compare the modeled values to field measurements of string level power, rear and front plane of array irradiance, and module temperature. Our results show that state-of-the-art bifacial performance models add ~0.5% uncertainty to the PV modeling chain. For the site investigated, 2-D view factor fixed tilt simulations are within ±1% of the measured monthly bifacial gain. However, simulations of single-axis tracker systems are less accurate, wherein 2-D view factor and 3-D ray tracing are within approximately 2% and 1% of the measured bifacial gain, respectively.

The Photovoltaic (PV) Performance Modeling Collaborative (PVPMC) organized a blind PV performance modeling intercomparison to allow PV modelers to blindly test their models and modeling ability against real system data. Measured weather and irradiance data were provided along with detailed descriptions of PV systems from two locations (Albuquerque, New Mexico, USA, and Roskilde, Denmark). Participants were asked to simulate the plane‐of‐array irradiance, module temperature, and DC power output from six systems and submit their results to Sandia for processing. The results showed overall median mean bias (i.e., the average error per participant) of 0.6% in annual irradiation and −3.3% in annual energy yield. While most PV performance modeling results seem to exhibit higher precision and accuracy as compared to an earlier blind PV modeling study in 2010, human errors, modeling skills, and derates were found to still cause significant errors in the estimates.

In this work we investigate and present preliminary results for two methods for luminescence imaging of photovoltaic (PV) modules in outdoor conditions, with the aim of choosing the most suitable method for implementation on a drone PV plant inspection system. We examined experimentally both electroluminescence (EL) and photoluminescence (PL) PV module imaging methods under natural light conditions, and determined that fast pulsed EL imaging with InGaAs detector cameras can yield reasonably accurate results under daylight conditions. Moreover, we formulated the necessary requirement for a PL light source, which would allow PL imaging of modules under daylight conditions.

Electroluminescence (EL) imaging inspections of PV power plants can bring a huge improvement in accuracy. The use of InGaAs camera will also make such inspections fast, but the restriction to acquire the images during dusk or evening is a limitation. Performing lock-in EL is a way to go for daylight EL. This paper proposes an extension of the SNR ₅₀ quality measure to estimate the quality of a stack of N images and evaluates the impact of some factors over the measured and visual quality of images acquired with InGaAs sensors. The factors analyzed are the characteristics of the noise in the acquired images, the influence of the sun variations and the averaging over multiple acquired images.