Small Scale Hydropower Generator Electrical System Modelling Based on Real-Measurements

Document Type

Article

Publication Date

2016

Publication Title

European Journal of Advances in Engineering and Technology

ISSN

2394-658X

Abstract

It has been realized that using renewable resources will be better for the world in the future. The advantage of the hydro turbine is using renewable energy to provide electricity because the water is released back into the source from which the water came. This small scale hydroelectric system can provide cheap electricity without producing greenhouse gases and polluting the atmosphere. The proposed system can generate electricity at a constant rate as long as there is a source of water flowing downward. Also if there is no demand for electricity, the generator can be turned off to conserve electricity for later needs. This paper proposes design and modelling of a small scale micro-hydro power electrical system capable of supplying a house near flowing water with sustainable power. A small scale hydropower turbine system and a larger system using a DC power supply generator are built. Real small hydro-generator associated with electric generator is used with a simple load, rectifier, and dc-dc converter. Larger system will use programmable power supply attached with rectifier to act as the larger hydro-turbine system is used. DC to DC converter is used to regulate the voltage level. Instead of using a battery to store energy, supercapacitors and static capacitors are used to store the energy. Smart dc load equipment is used to act as the compatible dc loads for smart homes. Artificial Neural Network (ANN) is used with feed forward back-propagation technique to implement Charging and discharging ANN models for load range up to 150 W. These models are checked and verified by comparing actual and predicted ANN values, with good error values and excellent regression factors (0.997: 1) to imply accuracy. Finally, the Simulink models are generated and deduced to use them without training the neural units each time. The discharging ANN models are introduced with Time and Resistance ranges as inputs and Voltage, Current and Power ranges as outputs for both the static capacitor and supercapacitor associated with our system. Also, charging Models are proposed using the same technique with Time and Voltage as inputs and Energy and Current as outputs.

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