IoT-Based Smart Street Light Monitoring System with Kalman Filter Estimation

Abstract

Integrating an intelligent control and management system on solar streetlights has an advanced impact in improving its system efficiency. The study developed a system for collecting, analyzing, and monitoring information on streetlight infrastructure in remote areas using IOT-based technology utilizing a Kalman filter estimation method. A solar street light controller is integrated into the conventional intelligent streetlight PV system using a plurality of Gravity I2C digital wattmeter, and LILYGO TTGO T-Call V1.5 that includes a real-time collection and logging of data. The study utilized open-source software such as PHP framework and MySQL database to display the battery and solar panel status online. The Kalman filter algorithm with modified initialization was used to estimate the bus voltage and load current. Data acquisition is in a one-minute interval based on the IEC61724 standard. The study assists the local community units by integrating effective and intelligent monitoring of their solar street lights. Moreover, the study performs an advanced development in embedded systems for energy conservation of street lights system and reduction of the maintenance cost.