- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Experimental Assessment of a Smart Sun Tracking System Consumption for the Improvement of a Crystalline Silicon Photovoltaic Module Performance under Variable Weather Conditions
摘要： Sun tracking systems are often used to improve the performance of crystalline silicon photovoltaic plants. However, their power consumption still remains a challenge till date. In this paper, a low power consumption sun tracking system has been implemented for driving a crystalline silicon photovoltaic module under variable weather conditions. The experimental results showed that this sun follower consumes less than 1% of the increased energy. Taking into account the tracker consumption, the energy gain can attain up to 25% under the same weather conditions compared to a fixed photovoltaic system. In addition to that, results gotten from data-based simulations are coherent with experimental results.
关键词： smart sun tracking system,supervision algorithm,data-based simulations,performance of photovoltaic module,microcontroller unit
更新于2025-09-23 15:19:57
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Supervision of a PV system with storage connected to the power line and design of a battery protection system
摘要： This work presents the supervision strategy in an Arduino card PV generator with storage connected to the low voltage grid. The studied system is composed of a photovoltaic generator, a boost converter, a buck-boost converter and a single-phase inverter. The power of the PV module depends on atmospheric conditions. Batteries are often confronted with overload problems and underload. The objective of this article is to manage the charging and discharging of the batteries, taking into account their protection against overload and under load and supervise the system. For this, supervision algorithms implemented in Arduino developed. Simulation results under Matlab show that the Arduino board has ensured the protection of the batteries and system supervision.
关键词： Arduino-card,Supervision,Statics converters,Photovoltaic
更新于2025-09-10 09:29:36
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Calibrated optical time transfer of UTC(k) for supervision of telecom networks
摘要： We report on the evaluation of the performance of optical time transfer links connecting a facility of Deutsche Telekom in Bremen with the Physikalisch-Technische Bundesanstalt in Braunschweig. In the current configuration three links have been established, two via a hub in Hannover and one using an independent alternate route. They are equipped with electronically stabilized fiber optic time and frequency transfer systems and parallel operation is maintained since December 2016. A novel method of link calibration, composed of two steps (one performed in the laboratory and the second one in the field), to accurately determine the influence of fiber chromatic dispersion is discussed in detail, and a thorough analysis of the uncertainty budget is given. We show that the time transfer performance achieved is difficult to characterize based on measurements with time interval counters that are the standard equipment in timing laboratories and in the telecommunications sector. In our experiments, values of TDEV at the low ps-level at averaging times between 104 to 106 seconds have been achieved. The uncertainty of time transfer (including all kinds of delays) is of the order of 50 ps in a cascade of links. The results obtained show that such a kind of link is capable to deliver signals to a remote end with an instability being at least two orders of magnitude below the current requirements included in relevant Recommendations of the International Telecommunication Union – Telecommunication Sector (ITU-T). Moreover, the current implementation would allow primary Cs fountain clocks to be compared at the level of their performance, that is characterized by an uncertainty at the low 10-16 level and a frequency instability of the same order of magnitude at one day averaging.
关键词： propagation delay calibration,frequency transfer,telecom network supervision,time transfer,uncertainty budget,fiber optic
更新于2025-09-10 09:29:36
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Self-Supervised Feature Learning With CRF Embedding for Hyperspectral Image Classification
摘要： The challenges in hyperspectral image (HSI) classification lie in the existence of noisy spectral information and lack of contextual information among pixels. Considering the three different levels in HSIs, i.e., subpixel, pixel, and superpixel, offer complementary information, we develop a novel HSI feature learning network (HSINet) to learn consistent features by self-supervision for HSI classification. HSINet contains a three-layer deep neural network and a multifeature convolutional neural network. It automatically extracts the features such as spatial, spectral, color, and boundary as well as context information. To boost the performance of self-supervised feature learning with the likelihood maximization, the conditional random field (CRF) framework is embedded into HSINet. The potential terms of unary, pairwise, and higher order in CRF are constructed by the corresponding subpixel, pixel, and superpixel. Furthermore, the feedback information derived from these terms are also fused into the different-level feature learning process, which makes the HSINet-CRF be a trainable end-to-end deep learning model with the back-propagation algorithm. Comprehensive evaluations are performed on three widely used HSI data sets and our method outperforms the state-of-the-art methods.
关键词： self-supervision,feature learning,convolutional neural network (CNN),Conditional random field (CRF),hyperspectral image (HSI) classification
更新于2025-09-09 09:28:46