Transmission Line Protection and Hybrid Fault Detection Using IOT
Transmission lines are the most crucial component of the power system in the current day. Its loyalty and power needs are growing at an exponential rate. Transmission lines' primary function is to move electricity from the source region to the distribution network. Because transmission lines account for 85-87 percentage of power system problems, transmission line protection is a critical concern. One of the biggest issues facing corporations nowadays is energy leakage. The only solution to this issue is to develop a system that can automatically identify transmission line faults and notify authorities of a specific location. The apparatus in this study employs a sensor to measure the transmission line's voltage flow and determine if it is changing. Relays may be used to automatically regulate a fault if one is found, and the system is also coupled with Internet of Things mechanisms to notify the responsible party of the location. Using readings from Phase Measurement Units (PMUs), this study suggests a unique hybrid method for detecting and identifying transmission line faults in an interconnected network. Positive voltage measurement from PMUs and fault impact detection using the relay-based cut-off approach form the foundation of the suggested fault detection and identification procedure. There are two phases to the implementation of the suggested fault detection and identification algorithm: Positive Voltage Magnitude (PVM) is used in the first stage of fault detection, and relay cut-off is used in the second step to identify the problem site. In the case of a defect in a particular phase, the information is shown on the display and transmitted to a web page via an Internet of Things (IoT) NODE MCU (Esp8266). The microcontroller IC receives the output from the opto-coupler, which senses the voltage. The microprocessor manages the voltage signal and output to the display and IoT module.
Authors
- N A
- Saran Meas
- R. Satheeshkumar
- V P Madhan
Publication Details
- Published
- 2025-04-29
- DOI
- https://doi.org/10.1109/aimla63829.2025.11041502
- Citations
- 1
- Primary Topic
- Power Systems Fault Detection
- Type
- article
- Field-Weighted Citation Impact
- 1.21