• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.859-866
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• 2021

Agricultural Photovoltaic power generation is a new model that installs solar power generation facilities on top of farmland. Through this, it is possible to increase farm household income by producing crops and electricity at the same time. Recently, various attempts have been made to utilize agricultural solar power generation. Agricultural photovoltaic power generation has a disadvantage in that maintenance is relatively difficult because it is installed on a relatively high structure unlike conventional photovoltaic power generation. To solve these problems, intelligent and efficient operation and diagnostic functions are required. In this paper, we discuss the design and implementation of a prediction and diagnosis system to collect and store the power output of agricultural solar power generation facilities and implement an intelligent prediction model. The proposed system predicts the amount of power generation based on the amount of solar power generation and environmental sensor data, determines whether there is an abnormality in the facility, calculates the aging degree of the facility and provides it to the user.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.825-832
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• 2022

In this paper, we discuss the design and implementation of predictive and diagnostic models for realizing intelligent predictive models by collecting and storing the power output of agricultural photovoltaic power generation systems. Our model predicts the amount of photovoltaic power generation using RNN, LSTM, and GRU models, which are recurrent neural network techniques specialized for time series data, and compares and analyzes each model with different hyperparameters, and evaluates the performance. As a result, the MSE and RMSE indicators of all three models were very close to 0, and the R2 indicator showed performance close to 1. Through this, it can be seen that the proposed prediction model is a suitable model for predicting the amount of photovoltaic power generation, and using this prediction, it was shown that it can be utilized as an intelligent and efficient O&M function in an agricultural photovoltaic system.

• Proceedings of the KIPE Conference
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• 2015.07a
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• pp.277-278
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• 2015

태양광발전소를 설치하기 위한 경제적 타당성을 분석하는 경우 기상청에서 제공하는 해당지역의 날씨정보를 기반으로 하는 PV Cell의 연간 발전량 예측 및 분석이 중요한 변수가 된다. 또한 날씨 조건에 대한 PV 발전의 예측은 기 설치되어 운전중에 있는 태양광발전소의 고장진단 및 성능평가에도 사용될 수 있다. 본 논문은 다양한 날씨 조건 중 주변온도, 풍속, 일사량에 따른 PV Cell의 특성을 분석하고, 실시간으로 변화하는 날씨환경에 대하여 순시적으로 PV Cell의 출력특성을 정확히 예측할 수 있는 모델을 수립한다.

• Nuclear industry
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• v.5 no.2 s.24
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• pp.45-49
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• 1985

원자력발전소는 다중방호의 개념에서 복수의 안전설비를 갖는 등 계통이 팽대하고 복잡하며 또한 엄격한 품질관리를 실시하므로 다른 발전형식에 비해 건설비가 높은 편이다. 따라서 설비의 유효이용을 도모하는 것이 중요하다. 이와 같은 관점에서 앞으로 기설발전소의 장수명화를 도모하고, 전수명기간의 발전전력량을 증대시킴으로서 원자력발전 코스트를 다른 발전코스트보다 유리하게 하는 것도 가능하다. 장수명화를 실현시키기 위해서는 수명평가상 중요한 구조기기의 노화진단기술, 수명예측방법 및 수명을 좌우하고 교환이 곤란한 기기$\cdot$시스템의 보수기술$\cdot$공법 등에 관한 기술개발이 중요한 과제이다.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.10a
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• pp.423-427
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• 2009

In the circumstances which a highly reliable operation in electric power facilities of extra high voltage and large capacity is needed, the importance of a preventive diagnostic technology is growing large day and day. The settlement of a preventive diagnostic technology for optimization and efficient management on the electric power facilities like GIS enable the reduce of repair fee, the improvement of safety and the systematic management of electric power facilities. The remaining life prediction of facilities will play a decisive role as a core technology of a preventive diagnostics in the future. And so it is necessary a continuous research and concern for the development of a preventive diagnostic technology hereafter.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.412-417
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• 2013

Analytical modeling of photovoltaic power systems has been receiving significant attentions in recent years in that it is easy to apply for prediction of its dynamics and fault detection and diagnosis in advanced engineering technologies. This paper presents a novel probabilistic modeling approach for such power systems with a big data sequence. Firstly, we express input/output function of photovoltaic power systems in which solar irradiation and ambient temperature are regarded as input variable and electric power is output variable respectively. Based on this functional relationship, conditional probability for these three random variables(such as irradiation, temperature, and electric power) is mathematically defined and its estimation is accomplished from ratio of numbers of all sample data to numbers of cases related to two input variables, which is efficient in particular for a big data sequence of photovoltaic powers systems. Lastly, we predict the output values from a probabilistic model of photovoltaic power systems by using the expectation theory. Two case studies are carried out for testing reliability of the proposed modeling methodology in this paper.

• 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.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.667-668
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• 2008

현대 산업사회의 특징은 대용량화와 고속화에 있다. 전력부분에 있어서도 예외는 아니어서 초고층빌딩, 대단지 아파트, 공장, 위락시설, 병원 등 대용량 전력부하가 급격한 속도로 증가하고 있다. 특히 전력부분에 있어서는 대용량화와 동시에 경량 및 부피감소의 요구가 증대되면서도 신뢰성 높은 전력 품질 또한 요구되고 있다. 전기안전의 측면에 있어서 발전소의 안전불량은 전력수용가의 수급 차질로 인한 데이터 전산망, 생산설비의 막대한 재산 피해 뿐 아니라 병원 등에서의 인명피해도 발생시킬 수 있으므로 이에 대한 대책이 꼭 강구되어야 한다. 본 논문은 소용량 2극 동기발전기의 공극자속 파형 특성연구를 통하여 발전기 회전자 권선의 단락고장진단을 위한 예측진단기술을 개발하는 과정의 일환으로 진행되었으며 우선 정상상태의 공극자속을 컴퓨터 시뮬레이션 방법으로 계산하고 이를 시험치와 비교하여 해석 결과의 신뢰성을 평가하였다. 본 연구의 결과를 바탕으로 발전기의 모의 고장으로 발생하는 공극 자속의 특성을 연구하여 발전설비의 안전 확대에 기여할 것이다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.672-680
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• 2019

This paper proposes "A Smart Farm Environment Optimization and Yield Prediction Platform based on IoT and Deep Learning" which gathers bio-sensor data from farms, diagnoses the diseases of growing crops, and predicts the year's harvest. The platform collects all the information currently available such as weather and soil microbes, optimizes the farm environment so that the crops can grow well, diagnoses the crop's diseases by using the leaves of the crops being grown on the farm, and predicts this year's harvest by using all the information on the farm. The result shows that the average accuracy of the AEOM is about 15% higher than that of the RF and about 8% higher than the GBD. Although data increases, the accuracy is reduced less than that of the RF or GBD. The linear regression shows that the slope of accuracy is -3.641E-4 for the ReLU, -4.0710E-4 for the Sigmoid, and -7.4534E-4 for the step function. Therefore, as the amount of test data increases, the ReLU is more accurate than the other two activation functions. This paper is a platform for managing the entire farm and, if introduced to actual farms, will greatly contribute to the development of smart farms in Korea.

• Journal of IKEEE
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• v.24 no.2
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• pp.403-408
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• 2020

Photovoltaic (PV) has both intermittent and uncertainty in nature, so it is difficult to accurately predict. Thus anomaly detection technology is important to diagnose real time PV generation. This paper identifies a correlation between various parameters and classifies the PV data applying k-nearest neighbor and dynamic time warpping. Results for the two classifications showed that an outlier detection by a fault of some facilities, and a temporary power loss by partial shading and overall shading occurring during the short period. Based on 100kW plant data, machine learning analysis and test results verified actual outliers and candidates of outlier.