Power Transmission Line Voice Based Parameter Fault Detection System
Author(s):
Chetana Chhagan Patil, Minakshi Kawarsing Banjara, Roshni Shivadas Pawara, Kavita Subhas Bagul, Vaibhav Shubhash Kale
Keywords:
AI; BMS; Energy consumption abnormalities; Automated Fault Detection
Abstract
The main aim of this project is to design a a system which helps in continuous monitoring of industrial parameters. The proposed system is capable of monitoring four important parameters of an industry like voltage, current, temperature and light intensity. The abnormality in the four parameters is announced through voice circuit. The controlling device of the whole system is a Microcontroller. Temperature sensor, Voltage sensor, Current sensor, LDR (Light Dependent Resistor) is interfaced to the Microcontroller. The Microcontroller reads the analog values from four sensors, digitizes them and starts analyzing of data. The data is continuously displayed through a LCD display. If any parameter is abnormal, the system gives a voice announcement. To perform the intelligent task, Microcontroller is loaded with intelligent program written using embedded ‘C’ language. Sectors such as industry, agriculture, services, and transportation. Studies of energy consumption in building sectors have reported that energy savings of 10% to 30% can be obtained by using artificial intelligence (AI), the system would be capable of detecting and analyzing anomalies in energy usage pattern assessing, diagnosing and suggesting the best solution in suitable time. This paper proposes to integrate and hybridize between AI techniques and big data algorithms which can enhance monitoring and controlling building systems, increasing comfort and decreasing efficiently the running costs. In addition, the authors suggest a tool which aims to automatically detect abnormal energy consumption by using AI and big data which are produced by the Building Management System (BMS).
Article Details
Unique Paper ID: 165044

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 1937 - 1942
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