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Various health concerns, explosions, injuries, and financial setbacks result from leaks occurring in transmission and distribution pipelines due to factors such as corrosion, material aging, climate-related effects, deliberate sabotage, and the transportation of petroleum products like crude oil, natural gas, and LNG. The unavailability of timely detection of the leakage and point of leak causes huge financial losses as we are already short of natural resources. Most of the equipment is installed at the inlet and outlet of the pipeline making it difficult to detect leakage and point of leak in the mid-span. To address this problem, a hybrid method consisting of volume-mass balance and Real-time Transient Modeling (RTTM) is used to develop an automated system that detects leakage in the pipeline, locate the point of leak, estimate the leak volume and to send an alert message. RTTM technique presents leak as a function of pressure and density of the working fluid. Real-time data is obtained from the pipeline using HK1100C and DS18B20 sensor by acquiring pressure and temperature of the working fluid respectively. Domestic gas meters are used to compute upstream and downstream flow rate, while NODMCU esp. 8266 is used as a microcontroller to transfer data from the pipeline to the IOT server Thing Speak with the aid of a built-in Wi-Fi module. The transient model is developed in LabVIEW where the analysis has been performed on pressure and flow variation inside the pipeline to detect the leak, calculate the leak location and estimate the leak volume.

Keywords: Leak Detection and Localization, Real Time Transient Modeling, Internet of Things, Labview, Volume-Mass Balance Method.