The Design and Development of Micro Grid Electrical Power Supply for Seismo Sensor with An Artificial Perceptron Neural Network

Abstract

This research presented the application of the neural network theory in order to solve the problems of the electrical power supply system used in seismo sensor equipment with no continuity vibration and the system requiring the multiple power supply resources for the alternative power. Therefore, the designed system applied a perceptron mathematical model as a function to stimulate this system in making a decision to choose the micro grid power sources between solar power cells at 300 watts, 24 volts and the external power source at 720 watts, 24 volts. From testing its performance, it indicated that when the power supply source system did not use the artificial perception neural network, the system slowly responded to the decision - making time in an average time of 120 milliseconds while the ripple voltage was increased at peak to peak 0.5 volts. This could affect the power system to the seismo sensor which had no stability and continuity. However, it was applied to the system which was likely more effective and reliable due to having a decision-making from the data set, size 100. Moreover, the decrease of the delay time was over 0.8 which caused the ripple voltage at peak-to-peak 0.26 volt. As a result, it could conclude that the studies were able to help the electrical power supply system for a seismo sensor have more effectiveness and it could be adapted for the smart grid system in the future.