• Journal of Power Electronics
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• v.19 no.5
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• pp.1270-1277
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• 2019

A problem with virtual synchronous generator (VSG) systems is that they are difficult to operate stably with photovoltaic (PV) power as the DC side. With this problem in mind, a PV-VSG control strategy considering the dynamic characteristics of the DC side is proposed after an in-depth analysis of the dynamic characteristics of photovoltaic power with a parallel energy-storage capacitor. The proposed PV-VSG automatically introduces DC side voltage control for the VSG when the PV enters into an unstable working interval, which avoids the phenomenon where an inverter fails to work due to a DC voltage sag. The stability of the original VSG and the proposed PV-VSG were compared by a root locus analysis. It is found that the stability of the PV-VSG is more sensitive to the inertia coefficient J than the VSG, and that a serious power oscillation may occur. According to this, a new rotor model is designed to make the inertial coefficient automatically change to adapt to the operating state. Experimental results show that the PV-VSG control strategy can achieve stable operation and maximum power output when the PV output power is insufficient.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.6
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• pp.535-540
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• 2022

DC-link capacitors are reliability-critical components in a photovoltaic (PV) inverter. Typically, the lifetime of a DC-link capacitor is evaluated by considering the voltage and hot-spot temperature of the capacitor under the specific operating condition of the PV inverter. However, the output of the PV inverter is determined by solar irradiation and ambient temperature, which vary with the seasons; accordingly, the hot-spot temperature of the capacitor also changes. Therefore, the mission profile of the PV system should be considered to effectively evaluate the reliability of the DC-link capacitor. In this study, the reliability of the DC-link capacitor of a three-level NPC inverter is comparatively analyzed with and without considering the mission profiles of the PV system, where two mission profiles recorded in Arizona and Iza are considered. The accumulated damage of the DC-link capacitor is calculated based on the lifetime model by analyzing its thermal loading. Afterward, a reliability evaluation of the DC-link capacitor is performed at the component level and then at the system level by considering all capacitors by means of Monte Carlo analysis. Results reveal the importance of performing a mission-profile-based reliability evaluation during the design of high-reliability PV inverters to achieve the target reliability performance.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.3
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• pp.50-64
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• 2011

In this paper, a DC-coupled photovoltaic (PV), fuel cell (FC) and ultracapacitor hybrid power system is studied for building microgrid. In this proposed system, the PV system provides electric energy to the electrolyzer to produce hydrogen for future use and transfer to the load side, if possible. Whenever the PV system cannot completely meet load demands, the FC system provides power to meet the remaining load. The main weak point of the FC system is slow dynamics, because the power slope is limited to prevent fuel starvation problems, improve performance and increase lifetime. A power management and control algorithm is proposed for the hybrid power system by taking into account the characteristics of each power source. The main works of this paper are hybridization of alternate energy sources with FC systems using long and short storage strategies to build an autonomous system with pragmatic design, and a dynamic model proposed for a PV/FC/UC bank hybrid power generation system. A simulation model for the hybrid power system has been developed using Matlab/Simulink, SimPowerSystems and Matlab/Stateflow. The system performance under the different scenarios has been verified by carrying out simulation studies using a practical load demand profile, hybrid power management and control, and real weather data.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.8
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• pp.70-81
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• 2011

In this paper, a multi-agent control system for DC-coupled photovoltaic (PV), fuel cell (FC), ultracapacitor(UC) and battery hybrid power system is studied for commercial buildings & apartment buildings microgrid. In this proposed system, the PV system provides electric energy to the electrolyzer to produce hydrogen for future use and transfer to the load side, if possible. Whenever the PV system cannot completely meet load demands, the FC system provides power to meet the remaining load. A multi-agent system based-power management and control algorithm is proposed for the hybrid power system by taking into account the characteristics of each power source. The main works of this paper are hybridization of alternate energy sources with FC systems using long and short storage strategies to build the multi-agent control system with pragmatic design, and a dynamic model proposed for a PV/FC/UC/battery bank hybrid power generation system. A dynamic simulation model for the hybrid power system has been developed using Matlab/Simulink, SimPowerSystems and Stateflow. Simulation results are also presented to demonstrate the effectiveness of the proposed multi-agent control and management system for building microgrid.

• Current Photovoltaic Research
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• v.10 no.1
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• pp.6-15
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• 2022

Climate change is among the most important issues facing mankind in modern society. However, global PV energy expansion has been driven mainly by OECD countries. We investigate the determinants of PV energy growth by panel data of selected OECD countries from 1991 to 2018. We investigate four categories of driving factors: socioeconomic, technological, country specific, and policy factors. The test results support that PV capacity growth is significantly driven by technology development and multidimensional environment policy factors. Socioeconomic factors such as CO₂, GDP, and electricity price are statistically significant on the growth of PV energy, too. Whereas, country-specific solar potential factor is the least related. As most of the socioeconomic factors are exogenous, we need to focus more on PV technology development and policy measures.

• Transactions on Electrical and Electronic Materials
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• v.18 no.2
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• pp.103-110
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• 2017

The number of applications of solar photovoltaic (PV) systems in power generation grids has increased in the last decade because of their ability to generate efficient and reliable power in a variety of low installation in domestic applications. Various PV converter topologies have therefore emerged, among which the modular multilevel converter (MMC) is very attractive due to its modularity and transformerless features. The modeling and control of the MMC has become an interesting issue due to the extremely large expansion of PV power plants at the residential scale and due to the power quality requirement of this application. This paper proposes a novel control method of MMC which is used to directly integrate the photovoltaic arrays with the power grid. Traditionally, a closed loop control has been used, although circulating current control and capacitors voltage balancing in each individual leg have remained unsolved problem. In this paper, the integration of model predictive control (MPC) and traditional closed loop control is proposed to control the MMC structure in a PV grid tied mode. Simulation results demonstrate the efficiency and effectiveness of the proposed control model.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.64-71
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• 2017

Considering the volatility, intermittent and random of photovoltaic (PV) generation systems, accurate forecasting of PV power output is important for the grid scheduling and energy management. In order to improve the accuracy of short-term power forecasting of PV systems, this paper proposes a prediction model based on environmental factors and support vector machine optimized by genetic algorithm (GA-SVM). In order to improve the prediction accuracy of this model, weather conditions are divided into three types, and the gray correlation coefficient algorithm is used to find out a similar day of the predicted day. To avoid parameters optimization into local optima, this paper uses genetic algorithm to optimize SVM parameters. Example verification shows that the prediction accuracy in three types of weather will remain at between 10% -15% and the short-term PV power forecasting model proposed is effective and promising.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.6
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• pp.696-703
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• 2003

A photovoltaic power generation system is an infinite and clean energy system. Recently. because of the realization of high efficiency and low cost PV modules, the studies on the PV system have extensively increased. In this paper. we present an online scheme for parameter estimation of solar cell, based on the model adjustment technique and a real-coded genetic algorithm(RCGA). The ideal diode model and the diode model with series and shunt resistors are used to estimate their parameters, Simulation works using field data in the form of a V-I characteristic curve are carried out to demonstrate the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.204-207
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• 2004

The paper proposes a simulation model of grid-connected photovoltaic generation system (PV system) using on PSCAD/EMTDC, a reliable power system and apparatus transient analysis program. A equivalent circuit model of a solar cell is used for modeling solar array. A series of parameters required for array modeling are deduced from general specification data of a solar module. A PWM voltage source inverter (VSI) model is presented and current control scheme is implemented for VSI control. A maximum power point tracking (MPPT) technique is applied for controlling the PV system. Simulation case study provides V-I and V-P characteristics of solar array and PV system control performance for irradiation changes.

• Journal of Power Electronics
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• v.17 no.4
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• pp.1014-1026
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• 2017

The traditional fault diagnosis method for photovoltaic (PV) inverters has a difficult time meeting the requirements of the current complex systems. Its main weakness lies in the study of nonlinear systems. In addition, its diagnosis time is long and its accuracy is low. To solve these problems, a hidden Markov model (HMM) is used that has unique advantages in terms of its training model and its recognition for diagnosing faults. However, the initial value of the HMM has a great influence on the model, and it is possible to achieve a local minimum in the training process. Therefore, a genetic algorithm is used to optimize the initial value and to achieve global optimization. In this paper, the HMM is combined with a genetic algorithm (GHMM) for PV inverter fault diagnosis. First Matlab is used to implement the genetic algorithm and to determine the optimal HMM initial value. Then a Baum-Welch algorithm is used for iterative training. Finally, a Viterbi algorithm is used for fault identification. Experimental results show that the correct PV inverter fault recognition rate by the HMM is about 10% higher than that of traditional methods. Using the GHMM, the correct recognition rate is further increased by approximately 13%, and the diagnosis time is greatly reduced. Therefore, the GHMM is faster and more accurate in diagnosing PV inverter faults.