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Rahulgiri A. Goswami1 , Girish Jadhav2
» doi: 10.48047/ecb/2023.12.9.259


Non-Technical Losses (NTL) in electrical distribution systems can have serious repercussions on energy suppliers and national economies, mainly involving the theft of electricity. Consumer fraud, illegal tapping of power lines, meter bypassing, and manipulation of energy meters are some common examples of non-technical losses. One of the important factors that determines the effectiveness of smart meters is the ability of all stakeholders to collect, transmit, analyze, and interpret data. Meter tampering or energy theft can be found and detected using artificial neural networks. The findings can potentially be used to improve bigger real systems. Over the past decades, AT&C has suffered heavy losses in the Indian energy sector. Combined loss, i.e., AT&C [AT&C stands for Aggregated Technical and Commercial Loss], including technical and commercial loss. Technical losses are unavoidable power flow losses due to network design in transmission and distribution systems. These losses represent a significant portion of India's overall loss of productivity and economic power. An in-depth study shows that these technical and Non-technical losses are a serious problem in India, accounting for 4% of the annual total. The main interest of researchers lies in this concept. It also recommends monitoring, analyzing and highlighting signs of non-technical damage, especially in these areas. Further, a MATLAB/Simulink Simulation has been carried out on a grid to examine power or energy theft by the different loads using data collected from the same grid and using Long Short-Term Memory (LSTM) Neural Network training in Python.

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