Election commission still uses manual system during election for vote casting in this modern age of digitalization. In parliamentary elections it is consider introducing Electronic voting machines (EVM) though Evm is not entirely automated and has many limitations. Integrated database system containing all voters' information and a convenient user interface of an automated biometric voting system had designed, in this work. Automatically casted votes will be counted simultaneously of the voting process and result will be displayed. Using Lab VIEW enables the data base storage and file management process to easy to build and applicable in all other fields compare to other methods. Lab VIEW is mainly for data acquisition, data base storage, extraction of the dat from data base and displaying it and also for report generation to store election voting information in files for future purpose. Main advantage of this project is not using internet connection for database extraction and storing purpose. This paper presents 2nd stage of verification by proving Aadhar information of the voter extracted by the use of fingerprints and that going to display on screen. The Aadhar information can be monitored. In future IOT can be implemented to send election report etc. There management of the election commission will be improved by ceasing fraudulent activities, corruptions, ensuring security, transparency, fairness, accuracy, trustworthy and keeping backup trails of voting system can be seen in this paper.

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Loss of Excitation (LOE) is the most considerable fault in Synchronous generators since it affects both the generators and power network. The traditional protection method for LOE is based on impedance trajectory of the machine with negative offset mho relay. Meanwhile the traditional method experiences malfunctions and speed dip in LOE detection. This paper presents machine learning approach to detect LOE fault as well as classification logic to discriminate LOE fault from power swing conditions due to Line fault. This paper utilizes Hotelling’s-T2 statistical method to calculate Hotelling’s-T2 based Fault Indices (HT2 -FI) for fault detection and Support Vector Machine (SVM) for classification. The time series data of electrical quantities such as Terminal voltage and Reactive Power of the generator are extracted from simulated Single Machine Infinite Bus test system and used as input data. This data is involved in calculation of HT2 –FI and in development of classification logic. The proposed method is simulated and verified for complete, partial LOE conditions and power swing conditions. Simulation outcomes depict the remarkable signs of the proposed method in LOE identification from power swing. Comparative assessment also reports that the method is capable of saving time in detecting LOE.