기상청 개방형-API 기상정보 기반 중·소규모 태양광발전 성능평가 및 고장감지 원격모니터링시스템
KMA Open-API Weather Information based Performance Evaluation and Fault Detection Remote Monitoring System for Small and Medium-Scale PV Systems
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초록
Small and medium-sized unmanaged grid-connected photovoltaic (PV) systems are difficult to manage owing to the installation costs of monitoring systems. Consequently, they can be left unattended for a long time following break downs. Thus, in this study, we developed a low-cost remote monitoring system for small-sized PV systems. The proposed monitoring system is equipped with a failure detection algorithm based on the simulation of an active system that accurately predicts the power output under normal operating conditions. Additionally, the monitoring system can recognize and notify a failure when the actual power generation is abnormally low. To lower the cost, we utilized SBC as a data server by edge computing and designed a monitoring system using the open-source software openHAB. Moreover, we used an open-API (ground weather observation data) provided by the Korea meteorological administration (KMA) to obtain hourly weather data (horizontal radiation, outdoor temperature, wind speed, etc.) for free to use in the simulation analysis. The Shewhart control chart was used as a benchmark for fault detection, where the ratio of the measured power generation to simulated generation was set as the observed value. From the verification test for actual grid-connected PV systems, it was confirmed that the monitoring system can effectively identify the occurrence of abnormal operating conditions, such as open-circuit, partial shading, etc.
Abstract
Small and medium-sized unmanaged grid-connected photovoltaic (PV) systems are difficult to manage owing to the installation costs of monitoring systems. Consequently, they can be left unattended for a long time following break downs. Thus, in this study, we developed a low-cost remote monitoring system for small-sized PV systems. The proposed monitoring system is equipped with a failure detection algorithm based on the simulation of an active system that accurately predicts the power output under normal operating conditions. Additionally, the monitoring system can recognize and notify a failure when the actual power generation is abnormally low. To lower the cost, we utilized SBC as a data server by edge computing and designed a monitoring system using the open-source software openHAB. Moreover, we used an open-API (ground weather observation data) provided by the Korea meteorological administration (KMA) to obtain hourly weather data (horizontal radiation, outdoor temperature, wind speed, etc.) for free to use in the simulation analysis. The Shewhart control chart was used as a benchmark for fault detection, where the ratio of the measured power generation to simulated generation was set as the observed value. From the verification test for actual grid-connected PV systems, it was confirmed that the monitoring system can effectively identify the occurrence of abnormal operating conditions, such as open-circuit, partial shading, etc.
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