Fire warning of lithium battery energy storage power stations for environmental sustainable development

To enhance the precision of fire alerts for energy storage power stations and reduce the response time, a fire warning approach tailored for sustainable environmental development in lithium battery energy storage power stations is introduced. This method focuses on fires within lithium battery energy storage power stations, gathering data via temperature, smoke, and CO gas sensors, and employs the D-S evidence theory to fuse this data, quantifying uncertainty and facilitating information integration. Fire trends are simulated using the sigmoid membership function, with parameters dynamically adjusted to accurately discern fire indicators. Sensors relay data in real-time to the central computer, which promptly issues a warning upon confirming a fire. Experimental results indicate that the fire warning accuracy of the method proposed herein peaks at 95.3%, with a response time of no more than two seconds.