Research on Key Parameter Identification and Complete Monitoring Technology for Substation Firefighting Systems

With the continuous development of power systems, substations, as core facilities for power transmission and distribution, have become increasingly important in terms of safety. Fires, as one of the common emergency incidents in substations, not only pose a threat to the facilities but can also severely impact the surrounding environment and personnel safety. In response to the limitations of traditional firefighting systems in data processing and fire warning, this paper proposes a monitoring technology solution for substation firefighting systems based on multiple linear regression, the Internet of Things (IoT), and big data analysis. By establishing a multiple regression model and combining real-time monitoring data from substations, it accurately identifies key parameters related to the occurrence of fires and performs real-time monitoring of the data. IoT technology and LoRa wireless sensor networks are utilized to efficiently and cost-effectively obtain remote data, which is then processed and analyzed on a cloud platform. Additionally, Kalman filtering technology is employed to optimize data quality, eliminate noise, and improve accuracy. This study uses a Support Vector Machine (SVM) model to predict and classify fire risks, providing real-time fire warnings and decision support for substations. Experimental results show that this technological solution has significant advantages in fire warning and emergency response, effectively enhancing the safety and fire prevention capabilities of substations.