Compact Fluorescent Lamps (CFLs) and Light-emitting Diode (LED) lamps have received wide acceptance in lighting applications during the last few years. However, without a power factor correction (PFC), the lamps reach a lagging power factor below 0.64 while the total harmonic distortion (THD) in the input current can be over 136%. Therefore, this paper presents an efficient, small size, low cost, and analog technology based on PFC for CFLs and tubular LED lamps. The topology to couple the line with the ballast of the lamp consists of a boost electronic converter under a hysteretic controller that is designed based on hysteretic current mode control. Besides, an experimental prototype is implemented with the PFC applied to a 15 W CFL and 12 W tubular LED lamp. The results show that the prototype corrects the lagging power factor to a value close to 0.98 and that harmonic levels are obtained below the limits set by the IEC 61000-3-2 Class C standard. Furthermore, the test showed that lamps with the PFC can be switched on and off more times than lamps without the PFC due to the low THD produced in the CFL, thus avoiding abrupt changes in the line current. These results are promising, as the controller does not require a compensation ramp as in other PWM control strategies, and it can be adapted to any type of lamp that draws a pulsating input current. In addition, the proposed controller could be applied to correct the power factor or regulate voltage for other applications, as it is an autonomous control technique with compact implementation.

Automatic crop identification and monitoring is a key element in enhancing food production processes as well as diminishing the related environmental impact. Although several efficient deep learning techniques have emerged in the field of multispectral imagery analysis, the crop classification problem still needs more accurate solutions. This work introduces a competitive methodology for crop classification from multispectral satellite imagery mainly using an enhanced 2D convolutional neural network (2D-CNN) designed at a smaller-scale architecture, as well as a novel post-processing step. The proposed methodology contains four steps: image stacking, patch extraction, classification model design (based on a 2D-CNN architecture), and post-processing. First, the images are stacked to increase the number of features. Second, the input images are split into patches and fed into the 2D-CNN model. Then, the 2D-CNN model is constructed within a small-scale framework, and properly trained to recognize 10 different types of crops. Finally, a post-processing step is performed in order to reduce the classification error caused by lower-spatial-resolution images. Experiments were carried over the so-named Campo Verde database, which consists of a set of satellite images captured by Landsat and Sentinel satellites from the municipality of Campo Verde, Brazil. In contrast to the maximum accuracy values reached by remarkable works reported in the literature (amounting to an overall accuracy of about 81%, a f1 score of 75.89%, and average accuracy of 73.35%), the proposed methodology achieves a competitive overall accuracy of 81.20%, a f1 score of 75.89%, and an average accuracy of 88.72% when classifying 10 different crops, while ensuring an adequate trade-off between the number of multiply-accumulate operations (MACs) and accuracy. Furthermore, given its ability to effectively classify patches from two image sequences, this methodology may result appealing for other real-world applications, such as the classification of urban materials.

No abstract

No abstract

This paper presents the design and implementation of an on-grid microinverter control technique for managing active and reactive power based on a dq transformation. The system was implemented in a solar microinverter development kit (Texas Instruments—TMDSSOLARUINVKIT). This microinverter has two stages: DC-DC and DC-AC. The DC-DC stage contains an active clamp flyback converter, where the maximum power point tracking (MPPT) of the solar panel is obtained with a current-based incremental conductance algorithm. The DC-AC stage comprises a dual-buck inverter in which voltage-, current-, and phase-tracking control loops are implemented to control the active and reactive power. These techniques were simulated in MATLAB using the proposed mathematical model and experimentally validated in the solar development kit. The results show that the simulated model behaved similarly to the real system, and the control techniques presented good performance. The maximum power point (MPP) of the solar panel was monitored in the DC-DC stage using a current reference provided by the incremental conductance MPPT algorithm and was regulated by a 2P2Z control. The algorithm is robust against continuous changes in irradiance, as it quickly follows the ideal power and continually operates at a point close to the MPP. In addition, the active and reactive power control in the DC-AC stage enables the microinverter to supply the maximum active power. Moreover, the microinverter supplies reactive power according to a defined reference and within the established limits. The proposed mathematical model of the microinverter can be used to design new control techniques and other microinverter topologies. In addition, this active and reactive power-control technique can be implemented in low-power and low-cost microinverters to successfully maintain power quality in small microgrids.