This paper presents an energy efficient transmitter for multi-standard applications (IEEE802.15.4, BLE, 5Mbps) in ISM2.4GHz band. It incorporates a fully digital PLL with two point modulation to achieve upto 5Mbps data rate at 9.5mW power consumption (including all power management blocks) at 0dBm output power, leading to 1.9nJ/b efficiency. The proposed digital PLL uses a counter based area and power efficient re-circulating TDC, current reuse low area DCO using resistive tail, process compensated high speed divider, class-AB PA stages, and fully integrated on-chip LDOs. The entire transmitter occupies 0.35mm² Silicon area in a 65nm digital CMOS process.

This study examines the susceptibility of bifacial glass/glass passivated emitter and rear cell (PERC) modules to potential-induced degradation-polarization (PID-p) in the field. While there are several studies showing PID-p occurring on both front and back faces of bifacial PERC in accelerated tests, we address the yet unclarified behavior in fielded modules. We examine the effects of mounting configuration; specifically, comparing modules mounted near ground and in elevated ground rack configurations. Modules with the cell circuit in −1500 V system voltage configuration, whether mounted on racks about 30 cm above the ground or elevated 2 m high showed mean degradation of 4.5% to 6% in power under standard test conditions over about 2.5 weeks as measured from the front side of the module. This extent of degradation remained sustained for a duration of about 6 months analyzed. Average daytime temperatures of modules in the various mounting configurations were similar and therefore judged to be insufficient to be a primary influence for the modest PID-p rate differences that we observed among mounting configurations. Increased leakage current in the morning suggests morning dew was sustained longer on modules near the ground measured over six months which would be expected to increase the PID-p rate over the long term. However, the main difference seen between the modules on the various mountings during the initial period with up to 6% mean degradation by PID-p was the approximately two times the irradiance from albedo on the rear of modules mounted in elevated ground rack compared to those on the near ground rack. This difference in incident albedo led to a modestly reduced rate of the development of PID-p of the modules on the elevated ground rack. The difference is attributed to the dissipation of PID-p-causing electrical charge by the albedo incident on the module rear. The behavior could be modeled by a sigmoidal equation with consideration of the differences in the insolation on the module rear.

The deliberate removal of photovoltaic modules from a string can occur for various reasons encompassing maintenance, measurements, theft, or failure, reducing that string length relative to others when replacement modules are not available and there are not any viable alternative makes and models that could be inserted. This phenomenon, delineated in our prior experimentally validated research, manifests two significant effects: (1) a shift in the ideal maximum power point and (2) the induction of potentially substantial reverse currents in the shortened strings at open-circuit voltage, V_OC. However, the scalability and asymptotic limits of these observed behaviors concerning array size remained undetermined. In this study, we elucidate the operational dynamics of such arrays by manipulating two mismatch-contributing variables in simulated arrays of up to 900 strings: the number of removed modules per string (indicative of the level of mismatch, ranging up to 5) and the quantity of shortened strings (1 to 60). Simulation outcomes underscore that mismatch severity impacts array operation more than the proportion of shortened strings. This research delves into the practical ramifications of operating with shortened strings, including implications for low-irradiance operation and the manifestation of deleterious reverse currents (>35 A in specific cases), emphasizing the need for careful array configuration for optimal performance and safety in these implementations.

In this paper, we use deep learning models to detect and classify various data objects within photovoltaics (PV) module datasheets stored in pdf or other image file formats. We seek to differentiate between regular text, table, header, footer, module drawing, document header, IV curve, PV curve, warranty graph, module image, and icon in order to automate the extraction of useful metadata from datasheets. Our work investigates whether existing computer vision models such as Detectron2 and YOLOv8x can be leveraged to accurately localize and classify the various data types with and without transfer learning. The models were trained on 57 datasheets with multiple pages or 169 pages in total. Detectron2 achieved an overall mAP50-95 of 0.78 without transfer learning (600 epochs and 16 hours) and an mAP50-95 of 0.54 (60 epochs and 2 hours) with transfer learning. YOLOv8x achieved an mAP50-95 of 0.89 without transfer learning (1000 epochs and 2 hours) and an mAP50-95 of 0.9 (20 epochs and several minutes).