• Journal of Power Electronics
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• v.14 no.4
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• pp.733-741
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• 2014

The integration of photovoltaic (PV) energy sources into DC grid has gained considerable attention because of its enhanced conversion efficiency with reduced number of power conversion stages. During the integration process, a local control unit is normally included with every power conversion stage of the PV source to accomplish the process of maximum power point tracking. A centralized monitoring and supervisory control unit is required for monitoring, power management, and protection of the entire system. Therefore, we propose a field-programmable gate array (FPGA) based centralized control unit that integrates all local controllers with the centralized monitoring unit. The main focus of this study is on the process of integrating many local control units into a single central unit. In this paper, we present design and optimization procedures for the hardware implementation of FPGA architecture. Furthermore, we propose a transient analysis and control design methodology with consideration of the nonlinear characteristics of the PV source. Hardware experiment results verify the efficiency of the central control unit and controller design.

• 한국신재생에너지학회:학술대회논문집
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• 2007.11a
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• pp.242-242
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• 2007

• Advances in Energy Research
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• v.1 no.2
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• pp.97-106
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• 2013

This study designs and tests a photovoltaic system with distributed maximum power point tracking (DMPPT) methodology using a field programmable gate array (FPGA) controller. Each solar panel in the distributed PV system is equipped with a newly designed DC/DC converter and the panel's voltage output is regulated by a FPGA controller using PI control. Power from each solar panel on the system is optimized by another controller where the quadratic maximization MPPT algorithm is used to ensure the panel's output power is always maximized. Experiments are carried out at atmospheric insolation with partial shading conditions using 4 amorphous silicon thin film solar panels of 2 different grades fabricated by Chi-Mei Energy. It is found that distributed MPPT requires only 100ms to find the maximum power point of the system. Compared with the traditional centralized PV (CPV) system, the distributed PV (DPV) system harvests more than 4% of solar energy in atmospheric weather condition, and 22% in average under 19% partial shading of one solar panel in the system. Test results for a 1.84 kW rated system composed by 8 poly-Si PV panels using another DC/DC converter design also confirm that the proposed system can be easily implemented into a larger PV power system. Additionally, the use of NI sbRIO-9642 FPGA-based controller is capable of controlling over 16 sets of PV modules, and a number of controllers can cooperate via the network if needed.

• Journal of the Korean Solar Energy Society
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• v.38 no.3
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• pp.21-28
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• 2018

The objects of PV (Photovoltaic) monitoring system is to reduce the loss of system and operation and maintenance costs. In case of PV plants with configured of centralized inverter type, only 1 PV module might be caused a large loss in the PV plant. For this reason, the monitoring technology of PV module-level that find out the location of the fault module and reduce the system losses is interested. In this paper, a fault diagnosis algorithm are proposed using thermal and electrical characteristics of PV modules under failure. In addition, the monitoring system applied with proposed algorithm was constructed. The wireless sensor using LoRa chip was designed to be able to connect with IoT device in the future. The characteristics of PV module by shading is not failure but it is treated as a temporary failure. In the monitoring system, it is possible to diagnose whether or not failure of bypass diode inside the junction box. The fault diagnosis algorithm are developed on considering a situation such as communication error of wireless sensor and empirical performance evaluation are currently conducting.

• KIEAE Journal
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• v.15 no.1
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• pp.89-95
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• 2015

The centralized power supply system and rainwater treatment system, which are major infrastructure in modern cities, are showing their limitations in accommodating environment load due to climate changes that has aggravated recently. As a result, complex issues such as shortage of reserve power and urban flooding have emerged. As a single solution, decentralized systems such as a model integrating photovoltaic system and rooftop greening system are suggested. When these two systems are integrated and applied together, the synergy effect is expected as the rooftop greening has an effect of preventing urban flooding by controlling peak outflow and also reduces ambient temperature and thus the surface temperature of solar cells is lowered and power generation efficiency is improved. This study aims to compare and analyze the monitoring results of four algorithms that define correlations between micro-climate variables around rooftop greening and the surface temperature of solar cells and generate their significance. By doing so, this study seeks to present an effective algorithm that can estimate the surface temperature of solar cell that has direct impact on the efficiency of photovoltaic power generation by observing climate variables.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.326-328
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• 1996

The 10kW or 1MW model of a photovoltaic array written in PSPICE is presented in this paper. A problem with this large scale centralized photovoltaic system is the decrease of power due to the resistance of cable connecting individual subarray with inverter. In this paper, we analyzed the relationship between the resistance of cable and subarray output power of 1MW photovoltaic system by the PSPICE modeling. As a result of simulation, we can proved that photovoltaic array output power is limitted by the resistance of cable.

• Journal of IKEEE
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• v.18 no.1
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• pp.159-164
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• 2014

This paper targets the development of micro-converter such as a power converter for photovoltaic module. In corresponding to the poor performance of centralized PV system under partial shading, the power converter for single PV module to maximize the energy harvest from PV module. The power converter is constantly tracking the maximum power point of photovoltaic system and increases energy output power. To minimize the quantity of devices and switchs, 320W solar micro-converter is developed using synchronous rectifier. From the basis of these results, through simulations and experiments were verified efficiency.

• Plant Journal
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• v.17 no.3
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• pp.34-36
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• 2021

The interest in renewable energy-based distributed power generation systems is increasing due to the recognitions of the breakthrough of existing centralized power generation, energy conversion, and environmental problems. In this study, the optimal capacity was selected by simulating a distributed power generation system based on PV and WT using lead acid batteries as the energy storage system. CHP was adopted as the existing power source, and the optimal capacity of the system was derived through MOGA according to the operating modes(full load/part load) of the existing power source. In addition, it was confirmed that the battery life differs when the battery charging method is changed at the same battery capacity. Therefore, for economical and stable power supply and demand, the capacity selection of the distributed generation system considering the battery charging method should be performed.

• Journal of Power Electronics
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• v.17 no.4
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• pp.1037-1047
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• 2017

The Differential Power Processing (DPP) converter is a promising multi-module photovoltaic inverter architecture recently proposed for photovoltaic systems. In this paper, a DPP converter architecture, in which each PV-panel has its own DPP converter in shunt, performs distributed maximum power point tracking (DMPPT) control. It maintains a high energy conversion efficiency, even under partial shading conditions. The system architecture only deals with the power differences among the PV panels, which reduces the power capacity of the converters. Therefore, the DPP systems can easily overcome the conventional disadvantages of PCS such as centralized, string, and module integrated converter (MIC) topologies. Among the various types of the DPP systems, the feed-forward method has been selected for both its voltage balancing and power transfer to a modified H-bridge inverter that needs charge balancing of the input capacitors. The modified H-bridge multi-level inverter had some advantages such as a low part count and cost competitiveness when compared to conventional multi-level inverters. Therefore, it is frequently used in photovoltaic (PV) power conditioning system (PCS). However, its simplified switching network draws input current asymmetrically. Therefore, input capacitors in series suffer from a problem due to a charge imbalance. This paper validates the operating principle and feasibility of the proposed topology through the simulation and experimental results. They show that the input-capacitor voltages maintain the voltage balance with the PV MPPT control operating with a 140-W hardware prototype.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.87-106
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• 2024

Vehicle-to-Home (V2H) and Home Centralized Photovoltaic (HCPV) systems can address various energy storage issues and enhance demand response programs. Renewable energy, such as solar energy and wind turbines, address the energy gap. However, no energy management system is currently available to regulate the uncertainty of renewable energy sources, electric vehicles, and appliance consumption within a smart microgrid. Therefore, this study investigated the impact of solar photovoltaic (PV) panels, electric vehicles, and Micro-Grid (MG) storage on maximum solar radiation hours. Several Deep Learning (DL) algorithms were applied to account for the uncertainty. Moreover, a Reinforcement Learning HCPV (RL-HCPV) algorithm was created for efficient real-time energy scheduling decisions. The proposed algorithm managed the energy demand between PV solar energy generation and vehicle energy storage. RL-HCPV was modeled according to several constraints to meet household electricity demands in sunny and cloudy weather. Simulations demonstrated how the proposed RL-HCPV system could efficiently handle the demand response and how V2H can help to smooth the appliance load profile and reduce power consumption costs with sustainable power generation. The results demonstrated the advantages of utilizing RL and V2H as potential storage technology for smart buildings.