KEYWORDS: Solar cells, Temperature metrology, Photovoltaics, Atmospheric modeling, Air temperature, Wind speed, Algorithm development, Clouds, Solar energy, Neural networks
The majority of air and ground vehicle systems are reliant on specialized diesel fuel. This reliance increases the likelihood that operations may be operating in an energy constrained or contested environment given the state of international relations between global energy providers and consumers. Such a vulnerability has the potential to reduce operational effectiveness or efficiency if logistical supply chains were interrupted or impeded. The most effective and efficient methodology to reduce reliance on specialized diesel fuel is to hybridize our energy and power (E&P) systems, and support more diverse E&P solutions including renewable energy generation (photovoltaic (PV) arrays, wind generation, wave energy converters), nuclear, or decaying isotopes. In this paper/presentation, we present our advances in developing a set of predictive artificial intelligence and machine learning (AI/ML) algorithms that forecast E&P capabilities of a photovoltaic array indirectly and directly. These milestones are a product of two separate types of AI/ML approaches: (1) developing AI/ML based algorithms that predict ambient and panel temperature from various atmospheric-based sensor data which can then be used in combination with an irradiance profile and a MATLAB Simulink model to predict the E&P capabilities of the PV array (indirect method), and (2) developing AI/ML which predicts the resulting E&P capabilities of the PV array, using various atmospheric-based sensor data (direct method).
Atmospheric optical turbulence (AOT) degrades seeing conditions over long horizontal paths. Embedded into the typical AOT diurnal cycle are two time periods in which the AOT is at a minimum; these periods are called neutral events (NE). Previously, we stated that the NE generally occurs 60 min after sunrise and 40 min before sunset. We refine this empirical model using a statistical analysis on a March-June 1994 AOT-NE data set sampled over the Tularosa Basin, New Mexico. Reviewing the months successively, a systematic change in the time differential between sunrise (sunset) and NE was observed. This and other March-to-June trends are discussed, as are several factors that cause variations in the NE forecast. We conclude with recommendations for refining the AOT-NE forecasting model.
A study of optical turbulence measurements at 1 m above ground level over an arid desert terrain was conducted at White Sands Missile Range, New Mexico, in the spring of 1992. The optical turbulence was characterized by the index of refraction structure function, Cn2, measured directly with scintillometers. Following a side-by-side comparison of the scintillometers along essentially identical 1-km paths, the calibrated sensors were installed along a 3750-m path for this study. The path was segmented into four nearly equal subdivisions, each equipped with a scintillometer transmitter-receiver pair. This paper describes the terrain variation by segment and the local and synoptic weather conditions during the study, and summarizes the observations and correlations drawn from intercomparing the four simultaneously sampled scintillometer measurements acquired along the 3750-m path.
It is pointed out that the performance of speckle imaging or optical interferometer systems increases with (r sub 0/D) exp n, where r sub 0 is the atmospheric coherence length, D is the aperture size, and n is between 2 and 4. It has been determined that, since r sub 0 is about 10 cm at visible wavelengths and D may be several meters, selecting a site with a large r sub 0 becomes critical for 30-100-m baseline systems. A unique problem for such optical systems is the need for a relatively large, flat, approximately 100-m site; however, this is inconsistent with the atmospheric dynamics that produce optical sites. Albuquerque and Chilao Flats results indicate that katabatic flows produce r sub 0 values of 30-50 mm; on the other hand, large mountain tops tend to have large 50-200 m inner layers, making r sub 0 extremely sensitive to the surface heat flux and wind speed. It is concluded that few locations can achieve this; those along the California Pacific Coast and Mauna Kea are two such regions.
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