Abstract:
In modern setups, the detection and prompt
acknowledgment of panel trips are crucial for ensuring the
smooth operation and safety of critical systems. This presents a
solution for implementing a Panel Trip Acknowledgment
System using ESP32, WiFi client-server, and Telegram. The
proposed system leverages the capabilities of ESP32, which
provide built-in WiFi functionality, making them ideal for
wireless communication that utilizes IOT. A client-server is
established, where the ESP32 acts as a client, continuously
monitoring the status of various panels within the industrial
setup that incorporates power backup capabilities. Whenever a
panel trip is detected, the ESP32 client sends a notification to a
central server. To facilitate real-time notifications and easy
accessibility, a Telegram bot is integrated into the system. The
server, upon receiving a panel trip notification, triggers the
Telegram bot to send an alert to authorized personnel or groups.
This ensures that the relevant individuals are informed about
the panel trip, enabling them to take appropriate action and
minimize downtime. A mathematical model has been created to
evaluate and improve the performance of the Panel Trip
Acknowledgment System. Important components of the system
are included in this model. The model determines the likelihood
that a panel trip will occur during a monitoring interval and the
likelihood that a panel trip will be detected during that time. It
also calculates how long it takes to notice a panel trip and alert
the appropriate people. The mathematical model offers
important insights into the system's performance in various
operational settings when combined with simulation data. The
effectiveness of panel trip detection and warning can be
increased by using this modeling approach to optimize crucial
parameters like monitoring intervals and transmission periods.
The research results enhance the Panel Trip Acknowledgment
System's effectiveness in guaranteeing operational continuity
and safety by assisting in its design and implementation in
industrial settings. This study lays the groundwork for the
implementation of cutting-edge IoT-based solutions for critical
system monitoring and quick reaction in industrial settings.
