DEPARTMENT OF ELECTRICAL\ELECTRONICS ENGINEERING

This project focuses on remotely measuring and analyzing electrical parameters of a 3-phase transformer, with primary objectives including assessing line and phase voltage, Real and Reactive Power and power factor remotely, transmitting the data to the cloud, and conducting comprehensive data analysis. The methodology involved utilizing a 3-phase meter with a CT clamped to the three-phase output on the control panel, configured to communicate with a 4G IoT gateway for internet connectivity. Data was transmitted to the cloud, extracted, and processed using machine learning libraries such as pandas, numpy, seaborn, and scikit-learn on Kaggle web data analysis platform. A linear regression model was constructed to forecast sum of reactive power based on real power and reactive power values. The results of the project showcase the effectiveness of remote monitoring and data analysis techniques in predicting electrical parameters. The linear regression model exhibited high performance, with a mean squared error of 16.97 and an R-squared value of 0.98, indicating precise predictions. Minimal deviations were noted when comparing actual and predicted values. These findings underscore the potential of such techniques in enhancing system efficiency and reliability through informed decision-making.