The research on Fault Type Classification in Electric Distribution Systems aims to develop algorithms for identifying faults influenced by electric vehicle charging demand. By using machine learning for accurate fault diagnosis, the study seeks to improve system reliability and optimize grid management and maintenance strategies.
This project presents an IoT-based Automatic Braking Control and Monitoring System for Electric Vehicles (EVs) designed to enhance safety through real-time environmental sensing and system response. The system integrates a Dallas temperature sensor, gas sensor, potentiometer, and voltage and current sensors to monitor critical parameters such as temperature, gas levels, and battery performance. If the temperature or gas concentration exceeds predefined thresholds, the system automatically halts the DC motor, activates a buzzer, and sends an alert via a GSM module. Additionally, the voltage and current sensors continuously measure the battery’s voltage and current, with the system triggering alerts if voltage drops below a critical level, turning off LEDs and activating both the buzzer and GSM alert. A potentiometer is included for manual voltage adjustment during testing. All sensor data is uploaded to the ThingSpeak IoT platform using a NodeMCU module for real-time remote monitoring, data logging, and further system optimization. This integration of sensors and IoT technology ensures enhanced safety, proactive alerts, and reliable control of the EV’s braking and power systems.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

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