• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.233-243
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• 2021

Photovoltaic panels are hazardous electronic waste that has heavy metal as one of the hazardous components. Each year, hazardous electronic waste is increasing worldwide and every heavy rainfall exposes the photovoltaic panel to become the source of heavy metal soil contamination. the development needs a monitoring technology for this hazardous exposure. this research use relationships between SAR temporal baseline and coherence of Sentinel-1 satellite to detected photovoltaic panel. Also, the photovoltaic plant detection tested using the difference between that photovoltaic panel and the other difference surface of coherence. The author tested the photovoltaic panel and its environment to calculate differences in coherence relationships. As a result of the experiment, the coherence of the photovoltaic panel, which is assumed to be a permanent scatterer, shows a bias that is biased toward a median value of 0.53 with a distribution of 0.50 to 0.65. Therefore, further research is needed to improve errors that may occur during processing. Additionally, the author found that the change detection using a temporal baseline is possible as the rate of reduction of coherence of photovoltaic panels differs from those of artificial objects such as buildings. This result could be an efficient way to continuously monitor regardless of weather conditions, which was a limitation of the existing optical satellite image-based photovoltaic panel detection research and to understand the spatial distribution in situations such as photovoltaic panel loss.

• Journal of Advanced Navigation Technology
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• v.22 no.3
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• pp.233-239
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• 2018

Since solar power generation is intermittent depending on weather conditions, it is necessary to predict the accurate generation amount of solar power to improve the efficiency and economical efficiency of solar power generation. This study proposes a short - term deep learning prediction model of solar power generation using meteorological data from Mokpo meteorological agency and generation data of Yeongam solar power plant. The meteorological agency forecasts weather factors such as temperature, precipitation, wind direction, wind speed, humidity, and cloudiness for three days. However, sunshine and solar radiation, the most important meteorological factors for forecasting solar power generation, are not predicted. The proposed model predicts solar radiation and solar radiation using forecast meteorological factors. The power generation was also forecasted by adding the forecasted solar and solar factors to the meteorological factors. The forecasted power generation of the proposed model is that the average RMSE and MAE of DNN are 0.177 and 0.095, and RNN is 0.116 and 0.067. Also, LSTM is the best result of 0.100 and 0.054. It is expected that this study will lead to better prediction results by combining various input.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.641-642
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• 2015

에너지저장장치(Energy Storage System, ESS)는 생산된 전력을 전력계통에 저장했다가 전력이 가장 필요한 시기에 꺼내어 사용함으로써 에너지효율을 높이는 능동적인 기술이다. ESS는 풍력발전, 태양광발전 등 신재생에너지와 같은 분산전원 도입, 피크부하에 대응하기 위한 부하평준화, 전력품질개선 등 그 역할과 기능이 확대되고 있다. 본 논문에서는 후쿠시마 사고 이후 강조된 비상전원과 관련하여 원자력발전소 부지 내 비상전원으로써의 ESS 활용과 그 가능성에 대해서 기술하였다.

• Proceedings of the KAIS Fall Conference
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• 2012.05b
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• pp.545-548
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• 2012

1차 에너지 소비량은 계속 증가 되고 있는데 화석연료의 고갈과 기후문제 등으로 소비량을 줄이기 위해 원자력발전과 신재생에너지인 풍력 및 태양광 등이 현재 증가하고 있다. 하지만 증가하는 부하에 대한 대응방안으로 원자력 발전소는 가변제어가 아닌 고정방식이기 때문에 기저부하에 대한 공급을 담당하고 신에너지는 발전량이 일정하지 않기 때문에 또한 피크부하 대응에 불가능하다 그러므로 양수발전소 또는 다른 발전소를 통해 피크부하를 관리하고 있는 상황이다. 하지만 단 몇 시간의 피크부하를 위해 지속적인 발전소를 지을 수 있는 부지도 없고 건설시간도 매우 많이 걸리는 단점이 있다. 본 논문에서는 양수발전 또는 다른 발전소를 대체할 수 있는 전력저장시스템(BESS)의 부하평준화효과에 대한 경제성평가 기법에 대하여 제시한다.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1070-1071
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• 2015

전국에 분포하고 있는 분산형 전원설비의 구성요소와 발전소에 대한 정보를 축적하여 개인 사업자 및 공급자가 해당 정보를 쉽게 접근할 수 있도록 웹기반의 통합관리시스템을 구축하였다. 대표적인 분산형 전원설비인 태양광 발전소와 풍력 발전소의 구성요소 사양들과 전국 발전소의 설치 및 발전용량들에 대한 데이터를 축적하여 데이터베이스화하였다. 축적된 데이터베이스는 웹기반의 통합관리시스템으로 구축되어 사용자가 쉽게 정보를 검색하여 문제를 해결할 수 있도록 하였다. 또한 각 발전소의 실시간 모니터링을 통한 데이터는 통계기법을 통하여 수익성을 평가할 수 있는 기초 자료로 활용 가능하도록 하였다. 향후 비즈니스 모델을 활용하여 통합관리시스템의 지속적인 운영이 가능하도록 한다.

• Journal of Bio-Environment Control
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• v.28 no.3
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• pp.243-254
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• 2019

With the recent accelerated policy-making and interests in new renewable energy, plans to develop and supply the new renewable energy have been devised across multiple regions in Korea. Solar energy, in particular, is being applied to small-scale power supply in provincial areas, as solar cells are used to convert solar energy into electric energy to produce electric power. Nonetheless, in the case of solar power plants, the need for a large stretch of land and considerable sum of financial support implies that the planning step should take into consideration the most suitable meteorological and geographical factors. In this study, the proxy variables of meteorological and geographical factors associated with solar energy were considered in analyzing the vulnerable areas regarding the photovoltaic power generation facility across the nation. GIS was used in the spatial analysis to develop a map for assessing the optimal location for photovoltaic power generation facility. The final vulnerability map developed in this study did not reveal any areas that exhibit vulnerability level 5 (very high) or 1 (very low). Jeollanam-do showed the largest value of vulnerability level 4 (high), while a large value of vulnerability level 3 (moderate) was shown by several administrative districts including Gwangju metropolitan city, Jeollabuk-do, Chungcheongbuk-do, and Gangwon-do. A value of vulnerability level 2 (low) was shown by the metropolitan cities including Daegu, Ulsan, and Incheon. When the 30 currently operating solar power plants were compared and reviewed, most were found to be in an area of vulnerability level 2 or 3, indicating that the locations were relatively suitable for solar energy. However, the limited data quantity for solar power plants, which is the limitation of this study, prevents the accuracy of the findings to be clearly established. Nevertheless, the significance of this study lies in that an attempt has been made to assess the vulnerability map for photovoltaic power generation facility targeting various regions across the nation, through the use of the GIS-based spatial analysis technique that takes into account the diverse meteorological and geographical factors. Furthermore, by presenting the data obtained for all regions across the nation, the findings of this study are likely to prove useful as the basic data in fields related to the photovoltaic power generation.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.1
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• pp.35-44
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• 2018

Korea government aims to generate 20 percent of its electricity with clean, renewable energy by 2030, while reducing its reliance on fossil fuel and nuclear power plants. Technically, solar energy has resource potential that far exceeds the entire global energy demand. Solar energy industry has experienced phenomenal growth in recent years due to both technological improvements resulting in cost reductions and government policies for renewable energy development and utilization. Even though solar power generation has several advantages over other forms of electricity generation, the major problem is the requirement of land which is scarcely available in the local site and its cost. This study analyzes the available capacity of landing and floating solar plants for the case of chonnam province in korea. The results of design capacity show about 7.5GW for landing and 1.5GW for floating solar power plant. Also, with a purpose to comprehend intention-behaviour gap about acceptance of solar community, the solutions are suggested.

• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.620-626
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• 2020

This research propose and simulate a solar power generation system monitoring system based on Modbus TCP communication using RaspberryPi, an IOT equipment, as a master and an inverter as a slave. In this model, various sensors are added to the RaspberryPi to add necessary information for monitoring solar power plants, and power generation prediction and monitoring information are transmitted to the smart phone through real-time power generation prediction. In addition, information that is continuously generated by the solar power plant is built on the server as big data, and a deep learning model for predicting power generation is trained and updated. As a result of the study, stable communication was possible based on Modbus TCP with the Raspberry Pi in the inverter, and real-time prediction was possible with the deep learning model learned in the Raspberry Pi. The server was able to train various deep learning models with big data, and it was confirmed that LSTM showed the best error with a learning error of 0.0069, a test error of 0.0075, and an RMSE of 0.0866. This model suggested that it is possible to implement a real-time monitoring system that is simpler, more convenient, and can predict the amount of power generation for inverters of various manufacturers.

• Journal of Advanced Navigation Technology
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• v.22 no.5
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• pp.478-484
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• 2018

This study predicts solar radiation, solar radiation, and solar power generation using hourly weather data such as temperature, precipitation, wind direction, wind speed, humidity, cloudiness, sunshine and solar radiation. I/O pattern in supervised learning is the most important factor in prediction, but it must be determined by repeated experiments because humans have to decide. This study proposed four input and output patterns for solar and sunrise prediction. In addition, we predicted solar power generation using the predicted solar and solar radiation data and power generation data of Youngam solar power plant in Jeollanamdo. As a experiment result, the model 4 showed the best prediction results in the sunshine and solar radiation prediction, and the RMSE of sunshine was 1.5 times and the sunshine RMSE was 3 times less than that of model 1. As a experiment result of solar power generation prediction, the best prediction result was obtained for model 4 as well as sunshine and solar radiation, and the RMSE was reduced by 2.7 times less than that of model 1.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.113-118
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• 2019

A solar power plant is a facility that produces electricity. As the risk of fire and electric shock accidents is diversified, the risk of workers, surrounding people, and facilities is increased, preventing safety accidents and promptly responding to safety accidents Is emerging. In light of the necessity of such development, it is necessary to develop a solar power generation management system that can diagnose and maintain the problems of the power generation system in real time by developing technologies for collecting and analyzing the data produced by the solar power generation system As a result, the utilization rate and the maintenance cost can be reduced. In order to do this, it is necessary to accurately predict the solar power generation amount in the present state, to diagnose the abnormality of the current power generation state and to grasp the abnormal position, and to use the model considering economical efficiency when the abnormal position is grasped, And the time and other information should be provided.