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수평설치조건의 건물부착형 태양광발전시스템에 대한 장기 운영 성능평가
이효문(Lee Hyo-Mun),강은호(Kang Eun-Ho),김동수(Kim Dong-Su),윤종호(Yoon Jong-Ho) 한국태양에너지학회 2021 한국태양에너지학회 논문집 Vol.41 No.4
Regulations and certification systems have been installed in many building-applied renewable energy systems (BARESs). Efforts to investigate and maintain the performance of renewable energy systems (RESs) have been lacking from the perspective of users or facility managers. This maintenance issue has obstructed the RES from operating in terms of its original purpose. Therefore, in this study, the operation status is evaluated through a power generation performance analysis on building attached photovoltaic (BAPV) systems, which are representative BARESs. The average performance ratio (PR) was 68.97%, with 78.42% in 2012 when monitoring started and 62.34% after 6 years. The PR continued to decline over time, with a difference of 16.07% between 2012 and 2018. In addition, the losses were 2.22%, 3.14%, 3.51%, and 22.16% over the entire period for the balance of the system (BOS), module temperature, incident angle modifier, and miscellaneous losses, respectively. Although other losses did not change over time, the miscellaneous loss was 10.43% in 2012, which increased to 29.49% in 2018. The BAPV system has experienced a continuous performance degradation since 2012. This indicates that managers have conducted no maintenance or performance surveys. Furthermore, more power can be generated by the BAPV system if maintenance is regularly conducted. The literature review has shown that a system installed for regulation or certification is not maintained, and that the facility managers are being challenged. Therefore, it is necessary to consider means to guide the maintenance points or methods applied.
건물적용 태양광발전시스템의 유지관리를 위한 인공신경망 기반 최대출력점 예측 연구
이효문(Lee Hyomun),김동수(Kim Dongsu),윤종호(Yoon Jongho) 한국태양에너지학회 2021 한국태양에너지학회 논문집 Vol.41 No.5
To achieve the greatest performance, photovoltaic (PV) systems need to be continuously monitored for fault detection and diagnosis, which could affect their performance under operational conditions. Various fault detection and diagnosis methods have been developed to improve these technologies. For example, the “Voltage and Current Measurement Method” based on the One-Diode model has been widely adopted in many case studies to predict a maximum power point (i.e., power, current, and voltage) and then detect any issues that occur under harsh outdoor conditions. Although the One-Diode model has demonstrated accurate performance for fault detection and diagnosis, the accuracy has typically been limited to crystalline PV modules. To overcome this limitation, many fault diagnosis methods have been proposed, and machine-learning fault detection and diagnosis methods are effective alternatives because of the nonlinear output features and varying operating conditions of PV arrays. Therefore, this study presents the prediction model of maximum power point (MPP) using artificial neural network (ANN) algorithms. The prediction model of MPP was generated through an optimization analysis. The MPP was validated by using measured data obtained from a building attached to a PV (BAPV) system in Korea. The results of the optimization study showed the highest predictive accuracy when the structure of the ANN model was trained and learned with one hidden layer, 20 neurons per hidden layer, a sigmoid function, and an 0.01 learning rate. The coefficient of variation of RMS error (CvRMSE) between the predicted value and the labelled value was 3.14%. The prediction model for MPP was verified by using the measured data. The analysis of the measured and predicted values resulted in CvRMSE values of 11.66%, 11.70%, and 18.01% for power, current, and voltage, respectively.
이효문(Lee Hyomun),배상순(Bae SangSoon),최민주(Choi Minjoo),김동수(Kim Dongsu),윤종호(Yoon Jongho) 한국태양에너지학회 2022 한국태양에너지학회 논문집 Vol.42 No.5
Photovoltaic (PV) systems are commonly used as on-site electric power generators for ZEBs in the Republic of Korea. To enhance the performance of PV systems, considering efficient installation conditions, such as the optimal azimuth and tilt angles, is critical. Under domestic PV application guidelines, the azimuth of installation for building-applied PV systems is stipulated within a maximum of ±90° based on the south-facing direction. In general, the north-facing direction is known as a weak position for PV systems. However, several studies have shown that the north-facing direction can be a good option for building PV installations, typically when the installation areas are limited within a building site. Even if the PV panel faces north, the system can operate effectively with proper performance when installed at a suitable inclination angle. In existing studies, system behavior has been verified based on simulation-based analysis, but actual operational data analysis remains insufficient. This study aims to evaluate the feasibility of north-facing PV systems based on a performance evaluation of the measured data of a roof PV system. The roof PV system analyzed in this study was composed of modules, module-level power electronics (MLPE), and one inverter. The PV modules and MLPEs were installed on both the southern (SIR) and northern (NIR) inclined roofs. Based on the annual cumulative DC power, the energy yields of the MLPE connected to modules of the SIR and NIR are 1,445.0 and 1,068.7 ㎾h/㎾p, respectively. Our study found that the ratio of the performance of MLPE on the NIR to MLPE on the SIR was 74.0%. This ratio was similar to the energy yield of the PV system on the south vertical plane as compared to that on the south slope plane. The analyzed results revealed that an acceptable performance of the PV system installed on a northern slope at a suitable inclination angle could be expected as compared with other PV installations such as the southern vertical.
국내 건축물 에너지 성능 평가도구(ECO2)의 태양광 시스템 분석 방법 고찰
최민주(Min Joo Choi),이효문(Hyo Mun Lee),강은호(Eun Ho Kang),김동수(Dong Su Kim),윤종호(Jong Ho Yoon) 대한설비공학회 2022 대한설비공학회 학술발표대회논문집 Vol.2022 No.6
제로에너지건축물 의무화 가속화로 인해 에너지자립률 확보를 위한 태양광발전 시스템의 중요성이 강조되고 있다. 본 연구에서는 ECO2 프로그램의 태양광발전 시스템 고도화를 위해 태양광발전 시스템 해석 변수의 적정성을 확인하고, 이에 따른 개선안을 제안하였다. 해석 모델에 따른 연간 Energy Yield는 해석 모델 간의 특성 및 입력변수에 따라 차이를 보이며 One-diode 모델이 ECO2 해석 모델과 가장 적은 차이율을 나타냈다. 설치조건(방위, 경사)에 따른 33개 지역 평균 발전성능 분석결과 ECO2 프로그램에서 태양광발전 시스템에 대한 설치 방위각으로 모델링이 가능한 동(90°)부터 서(270°)까지의 방위를 기준하였을 때 정동향(90°)의 방위, 90°의 경사각 조건에서 649 kWh/kWp·yr로 가장 낮은 발전성능을 나타냈다. 이를 바탕으로 ECO2의 태양광발전 시스템 평가결과의 정밀도를 높이기 위한 개선 방안을 제안하였다. 1. ECO2 프로그램의 설치각도의 세분화가 필요하다. 2. 북향의 경우 발전성능에 따른 설치각도를 제한한 설치 방위 확대가 필요하다.