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The risk of environmental pollution is a consequence of every kind of energy, including fossil fuels, nuclear power plants, and thermoelectric power plants. For the purpose of reducing the use ratio of such energy, research on eco-friendly energy is being actively carried out, and has shown that among all kinds of energy, solar energy has an advantage: it can supply us with inexhaustible clean energy. However, since solar energy depends on sunlight, the output may be unstable as it is influenced by weather or surrounding structures. In this paper, there is presented a control system which transmits power to a storage device, in a specific state, after the energy of the low-illumination section is charged in a supercapacitor using the accumulation-type controller by use of a supercapacitor. Feedback from the power output of photovoltaic panels (PVs) demonstrates that the power of the low-illumination section can be charged without being discarded. The charging rate was compared with other solar controllers being sold on the market, and the comparison was made through state of charge (SOC) measurements after the battery had been charged by photovoltaic panels for a whole day. It was confirmed that the solar controller, by use of supercapacitor integrator proposed in this paper, stored higher levels of energy than the existing solar controllers over the same hours and under the same conditions.
So-Hyeon Jo; Joo Woo; Gi-Sig Byun; Jae-Hoon Jeong; Heon Jeong. Study on the Integral Compensator Using Supercapacitor for Energy Harvesting in Low-Power Sections of Solar Energy. Energies 2021, 14, 2262 .
AMA StyleSo-Hyeon Jo, Joo Woo, Gi-Sig Byun, Jae-Hoon Jeong, Heon Jeong. Study on the Integral Compensator Using Supercapacitor for Energy Harvesting in Low-Power Sections of Solar Energy. Energies. 2021; 14 (8):2262.
Chicago/Turabian StyleSo-Hyeon Jo; Joo Woo; Gi-Sig Byun; Jae-Hoon Jeong; Heon Jeong. 2021. "Study on the Integral Compensator Using Supercapacitor for Energy Harvesting in Low-Power Sections of Solar Energy." Energies 14, no. 8: 2262.
Recently, there have been many types of research applying drones with a thermal camera to detect deteriorations in photovoltaic (PV) modules. A thermal camera can measure temperatures on the surface of PV modules and find the deteriorated area. However, a thermal camera generally has a lower resolution than a visible camera because of the limitations of cost. Due to different resolutions between the visible and thermal cameras, there are often invalid frames from a thermal camera. In this paper, we describe a gimbal controller with a real-time image processing algorithm to control the angle of the camera to position the region of interest (ROI) in the center of target PV modules to solve this problem. We derived the horizontal angle and vertical position of ROI in visible images using image processing algorithms such as the Hough transform. These values are converted into a PID control signal for controlling the gimbal. This process makes the thermal camera capture the effective area of target PV modules. Finally, experimental results showed that the photovoltaic module’s control area was properly located at the center of the thermal image.
Hyun-Cheol Park; Sang-Woong Lee; Heon Jeong. Image-Based Gimbal Control in a Drone for Centering Photovoltaic Modules in a Thermal Image. Applied Sciences 2020, 10, 4646 .
AMA StyleHyun-Cheol Park, Sang-Woong Lee, Heon Jeong. Image-Based Gimbal Control in a Drone for Centering Photovoltaic Modules in a Thermal Image. Applied Sciences. 2020; 10 (13):4646.
Chicago/Turabian StyleHyun-Cheol Park; Sang-Woong Lee; Heon Jeong. 2020. "Image-Based Gimbal Control in a Drone for Centering Photovoltaic Modules in a Thermal Image." Applied Sciences 10, no. 13: 4646.
Several factors cause the output degradation of the photovoltaic (PV) module. The main affecting elements are the higher PV module temperature, the shaded cell, the shortened or conducting bypass diodes, and the soiled and degraded PV array. In this paper, we introduce an image processing technique that automatically identifies the module generating the hot spots in the solar module. In order to extract feature points, we used the maximally stable extremal regions (MSER) method, which derives the area of interest by using the inrange function, using the blue color of the PV module. We propose an effective matching method for feature points and a homography translation technique. The temperature data derivation method and the normal/ abnormal decision method are described in order to enhance the performance. The effectiveness of the proposed system was evaluated through experiments. Finally, a thermal image analysis of approximately 240 modules was confirmed to be 97% consistent with the visual evaluation in the experimental results.
Heon Jeong; Goo-Rak Kwon; Sang-Woong Lee. Deterioration Diagnosis of Solar Module Using Thermal and Visible Image Processing. Energies 2020, 13, 2856 .
AMA StyleHeon Jeong, Goo-Rak Kwon, Sang-Woong Lee. Deterioration Diagnosis of Solar Module Using Thermal and Visible Image Processing. Energies. 2020; 13 (11):2856.
Chicago/Turabian StyleHeon Jeong; Goo-Rak Kwon; Sang-Woong Lee. 2020. "Deterioration Diagnosis of Solar Module Using Thermal and Visible Image Processing." Energies 13, no. 11: 2856.
In the last few decades, photovoltaic (PV) power station installations have surged across the globe. The output efficiency of these stations deteriorates with the passage of time due to multiple factors such as hotspots, shaded cell or module, short-circuited bypass diodes, etc. Traditionally, technicians inspect each solar panel in a PV power station using infrared thermography to ensure consistent output efficiency. With the advancement of drone technology, researchers have proposed to use drones equipped with thermal cameras for PV power station monitoring. However, most of these drone-based approaches require technicians to manually control the drone which in itself is a cumbersome task in the case of large PV power stations. To tackle this issue, this study presents an autonomous drone-based solution. The drone is mounted with both RGB (Red, Green, Blue) and thermal cameras. The proposed system can automatically detect and estimate the exact location of faulty PV modules among hundreds or thousands of PV modules in the power station. In addition, we propose an automatic drone flight path planning algorithm which eliminates the requirement of manual drone control. The system also utilizes an image processing algorithm to process RGB and thermal images for fault detection. The system was evaluated on a 1-MW solar power plant located in Suncheon, South Korea. The experimental results demonstrate the effectiveness of our solution.
Chris Henry; Sahadev Poudel; Sang-Woong Lee; Heon Jeong. Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera. Applied Sciences 2020, 10, 3802 .
AMA StyleChris Henry, Sahadev Poudel, Sang-Woong Lee, Heon Jeong. Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera. Applied Sciences. 2020; 10 (11):3802.
Chicago/Turabian StyleChris Henry; Sahadev Poudel; Sang-Woong Lee; Heon Jeong. 2020. "Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera." Applied Sciences 10, no. 11: 3802.
Customer requirements for unmanned aerial vehicles (UAVs) with long flight times are increasing exponentially in the personal, commercial, and military use areas. Due to their limited payload, large numbers of on-board battery packs cannot be used and this is the main reason behind the need for battery management software (BMS) packages with state of charge (SOC) estimation functions to increase the flight time. At the same time, as the UAV application range has extended widely, the size of UAVs has increased and heavy-duty UAVs are slowly appearing. As a result, the system operating power of the UAVs has been increased tremendously and their safe system power operation has become an issue. This is the main reason for the need of BMS having state of power (SOP) estimation functions. In this work a 6 S Li-Po battery pack is simulated with two ladder equivalent circuit models (ECMs) considering an impedance effect whose parameters are found using hybrid pulse power characterization (HPPC) current patterns with parameter determination using the table-based linear interpolation (TBLI) method. Two state estimation methods, including the current integration method and the extended Kalman filter (EKF) method are developed and the estimation accuracies of SOC and SOP are compared. Results show that the most accurate SOC estimation turns out to be 0.1477% (indoor test with HPPC), 0.1324% (outdoor test with 0 kg payload), and 0.2021% (outdoor test with 10 kg payload). Also, the most accurate SOP estimation error turns out to be 1.2% (indoor test with HPPC), 3.6% (outdoor test with 0 kg payload), and 4.2% (outdoor test with 10 kg payload).
Sunghun Jung; Heon Jeong. Extended Kalman Filter-Based State of Charge and State of Power Estimation Algorithm for Unmanned Aerial Vehicle Li-Po Battery Packs. Energies 2017, 10, 1237 .
AMA StyleSunghun Jung, Heon Jeong. Extended Kalman Filter-Based State of Charge and State of Power Estimation Algorithm for Unmanned Aerial Vehicle Li-Po Battery Packs. Energies. 2017; 10 (8):1237.
Chicago/Turabian StyleSunghun Jung; Heon Jeong. 2017. "Extended Kalman Filter-Based State of Charge and State of Power Estimation Algorithm for Unmanned Aerial Vehicle Li-Po Battery Packs." Energies 10, no. 8: 1237.