• Journal of Information Processing Systems
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• v.19 no.6
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• pp.791-802
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• 2023

The manual inspection of photovoltaic (PV) panels to meet the requirements of inspection work for large-scale PV power plants is challenging. We present a hot spot detection and positioning method to detect hot spots in batches and locate their latitudes and longitudes. First, a network based on the YOLOv3 architecture was utilized to identify hot spots. The innovation is to modify the RU_1 unit in the YOLOv3 model for hot spot detection in the far field of view and add a neural network residual unit for fusion. In addition, because of the misidentification problem in the infrared images of the solar PV panels, the DeepLab v3+ model was adopted to segment the PV panels to filter out the misidentification caused by bright spots on the ground. Finally, the latitude and longitude of the hot spot are calculated according to the geometric positioning method utilizing known information such as the drone's yaw angle, shooting height, and lens field-of-view. The experimental results indicate that the hot spot recognition rate accuracy is above 98%. When keeping the drone 25 m off the ground, the hot spot positioning error is at the decimeter level.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.2
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• pp.152-158
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• 2015

Unlike photovoltaic systems installed on land, photovoltaic systems applied to the offshore plant have the characteristic that is installed in a limited space. For single point mooring plant, it is advantageous in terms of a reliable power supply to be installed in different directions of photovoltaic panels, because it is not possible to identify the position of the sun by rotation of the plant itself. Differences of installation angle between photovoltaic panels make a difference of the intensity of radiation irradiated on each photovoltaic panel, and it brings loss of generation quantity due to the partial shading. In order to provide a photovoltaic system suitable for offshore plant, the modeling which contains multiple photovoltaic panels controlled by single controller is performed. Then, it was examined how the output characteristics of the photovoltaic system change about the difference of the intensity of radiation that varies depending on the altitude of the sun. Finally, through the simulation, a development model of the photovoltaic system which is suitable for offshore plant is suggested.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1046-1052
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• 2010

In residential house, photovoltaic (PV) system among various alternatives in renewable energy system is the most efficient and feasible solution for reducing energy consumption and electricity cost. However, relatively high initial cost make people reluctant to install PV system in their houses. Therefore, in the initial state for PV system installation in the house, it is very important to decide proper capacity of the PV system considering the expected energy usage and solar energy supplying condition with the house. This paper proposes a novel optimization model for deciding appropriate capacity of the PV system for residential house. The objective function of the model is to minimize the annual cost including electricity bill, operation and maintenance cost, and annual fixed cost calculated from the initial installation cost based on capital recovery factor (CRF). The model also shows the optimal inclining angle of PV panels of the system. In this paper, we estimate the PV output using PVWATTS (PV simulator of Office of Energy Efficiency and Renewable Energy) and find optimal solutions by Sequential Quadratic Programming (SQP) method using MATLAB software. The proposed approach is finally applied to a residential model house in Gangneung, Gangwon-Do and verified its feasibility for adopting to PV system design for residential houses.

• Journal of the Korean Solar Energy Society
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• v.38 no.1
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• pp.37-44
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• 2018

Solar panels are modules made up of many cells, like the N-type monosilicon, P-type monosilicon, P-type multisilicon, amorphous thin-film silicon, and CIGS solar cells. An efficient photovoltaic (PV) power is important to use to determine what kind of cell types are used because residential solar systems receive attention. In this study, we used 3-type solar panels - such as N-type monosilicon, P-type monosilicon, and CIGS solar cells - to investigate what kind of solar panel on a house or building performs the best. PV systems were composed of 3-type solar panels on the roof with each ~1.8 kW nominal power. N-type monosilicon solar panel resulted in the best power generation when monitored. Capacity Utilization Factor (CUF) and Performance Ratio (PR) of the N-type Si solar panel were 14.6% and 75% respectively. In comparison, N-type monosilicon and CIGS solar panels showed higher performance in power generation than P-type monosilicon solar power with increasing solar irradiance.

• Proceedings of the KIPE Conference
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• 2016.07a
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• pp.105-106
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• 2016

In low-power grid-connected photovoltaic(PV) system, Single-phase transformerless full-bridge inverter is commonly used. However in transformerless photovoltaic application, the ground parasitic capacitance created by grounding between PV panels and ground. This ground parasitic capacitance inject additional current into the inverter, these currents cause electromagnetic interference problem, safety problem and harmonics problem in PV applications. In order to eliminate the ground current, This paper propose various inverter topologies in PV applications. These proposed inverter topologies are verified through simulation using PSIM.

• ETRI Journal
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• v.40 no.5
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• pp.664-676
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• 2018

Given the rapidly increasing market penetration of photovoltaic (PV) systems in many fields, including construction and housing, the effective maintenance of PV systems through remote monitoring at the panel level has attracted attention to quickly detect faults that cause reductions in yearly PV energy production, and which can reduce the whole-life cost. A key point of PV monitoring at the panel level is cost-effectiveness, as the installation of the massive PV panels that comprise PV systems is showing rapid growth in the market. This paper proposes an implementation method that involves the use of a panel-level wireless PV monitoring module (WPMM), and which assesses the cost-effectiveness of this approach. To maximize the cost-effectiveness, the designed WPMM uses a voltage-divider scheme for voltage metering and a shunt-resistor scheme for current metering. In addition, the proposed method offsets the effect of element errors by extracting calibration parameters. Furthermore, a design method is presented for portable and user-friendly PV monitoring, and demonstration results using a commercial 30-kW PV system are described.

• Journal of Bio-Environment Control
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• v.26 no.4
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• pp.258-267
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• 2017

The price competitiveness of photovoltaic system (PV system) has risen recently due to the growth of industries, however, it is rarely applied to the greenhouse compared to other renewable energy. In order to evaluate the application of PV system in the greenhouse, power generation and optimal installation area of PV panels should be analyzed. For this purpose, the prediction of the heating and cooling loads of the greenhouse is necessary at first. Therefore, periodic and maximum energy loads of a multi-span greenhouse were estimated using Building Energy Simulation(BES) and optimal installation area of PV panels was derived in this study. 5 parameter equivalent circuit model was applied to analyzed power generation of PV system under different installation angle and the optimal installation condition of the PV system was derived. As a result of the energy simulation, the average cooling load and heating load of the greenhouse were 627,516MJ and 1,652,050MJ respectively when the ventilation rate was $60AE{\cdot}hr^{-1}$. The highest electric power production of the PV system was generated when the installation angle was set to $30^{\circ}$. Also, adjustable PV system produced about 6% more electric power than the fixed PV system. Optimal installation area of the PV panels was derived with consideration of the estimated energy loads. As a result, optimal installation area of PV panels for fixed PV system and adjustable PV system were $521m^2$ and $494m^2$ respectively.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.16 no.4
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• pp.9-15
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• 2020

In this study, effect of reinforced insulation on heating and cooling loads were studied due to installation of PV panels on factory building roof with a floor area of 12,960 m². For PV panel installation, combination of aluminum, polyurethane, air, polystyrene and steel materials were added to the original roof, which increased thermal insulation performance. Half of the roof were covered with PV panel and the other half without. Temperature and relative humidity were measured for 8 days during summer season for both indoor spaces. PV panel showed the effect of lowering the indoor space temperature by 0.6℃. TRNSYS dynamic simulation showed that with PV panel, cooling load per area is reduced by 1.7 W/m² and heating by 10.0 W/m². PV panels installed on building roof not only generate electricity but also can save energy by reducing cooling and heating loads.

• Journal of Power Electronics
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• v.14 no.6
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• pp.1281-1292
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• 2014

Photovoltaic (PV) solar systems with series-connected module integrated converters (MICs) are receiving increased attention because of their ability to create high output voltage while performing local maximum power point tracking (MPPT) control for individual solar panels, which is a solution for partial shading effects in PV systems at panel level. To eliminate the partial shading effects in PV system more effectively, sub-MICs are utilized at the cell level or grouped cell level within a PV solar panel. This study presents the results of a series-output-connection MPPT (SOC-MPPT) controller for sub-MIC architecture using a single sensor at the output and a single digital MPPT controller (sub-MIC SOC-MPPT controller and architecture). The sub-MIC SOC-MPPT controller and architecture are investigated based on boost type sub-MICs. Experimental results under steady-state and transient conditions are presented to verify the performance of the controller and the effectiveness of the architecture.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.6
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• pp.619-627
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• 2016

Cells of a PV (photovoltaic) module can suffer defects due to various causes resulting in a loss of power output. As a malfunctioning cell has a higher temperature than adjacent normal cells, it can be easily detected with a thermal infrared sensor. A conventional method of PV cell inspection is to use a hand-held infrared sensor for visual inspection. The main disadvantages of this method, when applied to a large-scale PV power plant, are that it is time-consuming and costly. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule was applied to automatically detect defective panels using the mean intensity and standard deviation range of each panel by array. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97% or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule. In this study, we used a panel area extraction method that we previously developed; fault detection accuracy would be improved if panel area extraction from images was more precise. Furthermore, the proposed algorithm contributes to the development of a maintenance and repair system for large-scale PV power plants, in combination with a geo-referencing algorithm for accurate determination of panel locations using sensor-based orientation parameters and photogrammetry from ground control points.