Abstract
Background/Objectives: Safety matters are being discussed and countermeasures to prevent various breakdowns/accidents are urgently required. Therefore, we propose a safety management solution for ESS fire monitoring.
 Methods/Statistical analysis: BLE 5.0-based ultra-compact wireless sensor-based edge that can detect off-gas early, as well as measure the temperature, humidity, vibration, and smoke in the golden time of approximately 10 minutes from the occurrence of thermal runaway inside the ESS to a fire. This paper proposes a safety system and an ESS-integrated safety management solution. Findings: It is known that the ESS currently in operation is installed without an integrated control method and a systematic protection system between facilities in order to connect new and renewable energy and to quickly supply the site, so there are many problems. For this reason, various accidents such as reactor failure and fire are occurring in the ESS installed at the actual site. Recently, 22 fires occurred in ESS facilities installed nationwide in 2019, and the government has urgently stopped ESS operation and formed a joint public-private investigation committee to clarify the cause. Among the various causes of ESS fires that have been raised, the ESS design should be considered. This paper reported an edge safety-based on BLE 5.0 that is capable of detecting off-gas and detecting temperature, humidity, vibration, and smoke early in the golden time of approximately 10 minutes from the occurrence of thermal runaway inside the ESS to a fire. A system and ESS integrated safety management solution were proposed. In this study, internal monitoring of an ESS was possible by integrating directly with existing and new ESS. The administrator can observe all conditions on the web through the cloud-based ESS integrated safety management system. Improvements/Applications: This paper analyzed and developed a cloud-based ESS integrated safety management system. It will be helpful in ESS fire management in the future and will be useful in designing artificial intelligence-based safety management solutions.
Highlights
As environmental problems, such as fine dust and global warming, have arisen, and the demand for clean energy has increased
To analyze the correlation of the sensor data, a deep learning-based ESS fire prediction and detection algorithm was applied to analyze the correlation of five sensor data and real-time RNN Recurrent Neural Network
ESSintegrated management is possible in real-time, and ESS integrated monitoring, individual monitoring, and emergency control functions are provided
Summary
As environmental problems, such as fine dust and global warming, have arisen, and the demand for clean energy has increased. An increasing number of new and renewable distributed power sources are being installed. The currently operating ESSs have a range of problems because they are installed without an integrated control method or a systematic protection system between the facilities for new and renewable linkages and rapid on-site supply.
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