Automation and Autonomous Systems

M. Geetha ¹, P. Paruthi Ilam Vazhuthi ², B. Arunkumar ¹

Affiliations
1 V.R.S College of Engineering and Technology, Arasur, IN
2 V.R.S College of Engineering and Technology, Arasur, IN

Abstract

This paper presents the development of a methodology to manage and trace the vehicle velocity, temperature and position of the vehicle from the vehicular system .We proposed a automatic speed control system through analysis and monitoring through digitalized image sensing of the vehicle ahead and use color segmentation algorithms to have a position of the vehicle ahead, aiming predict a need of preventive maintenance and mostly avoids collision between the vehicles. In this proposed system, accelerometer which will identify vibration, temperature sensor to identify the temperatures at critical points of vehicle, a microcontroller hardware which will make acquisition and processing of signals from sensors. The acquired parameters such as Vehicular speed, position and temperature are stored in a SD card and finally The vehicles sends information to the Emergency services, respective mobile numbers. The post processing is performed remotely through a computer that receives the data submitted via GSM presenting them to the user via graphical interface. This leads to minimize collision in highways.

Keywords

ARM7, MEMS Accelerometer, Collision Avoidance, Speed Control.

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S. Arun Prakash ¹, M. Geetha ²

Affiliations
1 Department of Electrical and Electronics Engineering, University College of Engineering, Ramanathapuram, IN
2 Department of Electrical and Electronics Engineering, Anna University, Regional Centre, Madurai, IN

Abstract

In this paper, the probabilistic neural network (PNN) is proposed as the classifier to discriminate between the magnetizing inrush and the phase to phase fault of a power transformer. The probabilistic neural network (PNN), is investigated for the protection of power transformer. Current waveforms due to different types of events, such as phase to phase faults and circuit breaker switching were generated using power transformer simulated in MATLAB simulation software. Simulated data were used to train the classifiers. To evaluate the developed algorithm for various operating condition of the transformer, including internal faults, are obtained by modeling the transformer in MATLAB Simulink. The data from the simulation are trained and tested by using Probabilistic Neural Network for discrimination of magnetic inrush and phase to phase fault.

Keywords

Inrush Current, Phase to Phase Fault (Internal Fault), Power Transformer, Probabilistic Neural Network (PNN).

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