As one of alternative sources of renewable energy, wind energy has an excellence prospect in Indonesia, particularly in coastal and hilly areas which have potential wind to generate electricity for residential uses. There is urgent need to locally develop low cost inverter of wind generator system for residential use. Recent developments in power electronic converters and embedded computing allow improvement of power electronic converter devices that enable integration of microcontrollers in its design. In this project, an inverter circuit with suitable control scheme design was developed. The circuit was to be used with a selected topology of Wind Energy Conversion System (WECS) to convert electricity generated by a 500W direct-drive permanent magnet type wind generator which is typical for residential use. From single phase AC output of the generator, a rectifier circuit is designed to convert AC to DC voltage. Then a DC-DC boost converter is used to step up the voltage to a nominal DC voltage suitable for domestic use. The proposed inverter then will convert the DC voltage to sinusoidal AC. The duty cycle of sinusoidal Pulse-Width Modulated (SPWM) signal controlling switches in the inverter was generated by a microcontroller. The lab-scale experimental rig involves simulation of wind generator by running a geared DC motor coupled with 500W wind generator where the prototype circuit was connected at the generator output. The experimental circuit produced single phase 240V sinusoidal AC voltage with frequency of 50Hz. Measured total harmonics distortion (THD) of the voltage across load was 4.0% which is within the limit of 5% as recommended by IEEE Standard 519-1992. Muhida et al. / Mechatronics, Electrical Power, and Vehicular Technology 03 (2012)
Ground penetrating radar (GPR) is one of the promising non-destructive imaging tools investigations for shallow subsurface exploration such as locating and mapping the buried utilities. In practical applications, GPR images could be noisy due to the system noise, the heterogeneity of the medium, and mutual wave interactions thus, it is a complex task to recognizing the hyperbolic signature of buried objects from GPR images. Therefore, this paper aims to develop nonlinear feature extraction technique of using Empirical Mode Decomposition (EMD) in recognizing the four geometrical shapes (cubic, cylindrical, disc and spherical) from GPR images. A pre-processing step of isolating hyperbolic signature from different background was first employed by mean of Region of Interest (ROI). The hyperbolic signature that describes the shapes was extracted using EMD decomposition to obtain a set of significant features. In this framework, the hyperbolic pattern was decomposed of using EMD, to produce a small set of intrinsic mode functions (IMF) via sifting process. The IMF properties of the signature that exhibit the unique pattern was used as potential features to differentiate the geometrical shapes of buried objects. The extracted IMF features were then fed into machine learning classifier namely Support Vector Machines. To evaluate the effectiveness of the proposed method, a set data collection of GPR-images has been acquired. The experimental results show that the recognition rate of using IMF features was achieved 99.12% accuracy in recognizing the shapes of buried objects whose shows the promising result.
This paper presents a review on Ground Penetrating Radar (GPR) detection and mapping of buried utilities which have been widely used as non-destructive investigation and efficiently in terms of usage. The reviews cover on experimental design in GPR data collection and survey, pre-processing, extracting hyperbolic feature using image processing and machine learning techniques. Some of the issues and challenges facing by the GPR interpretation particularly in extracting the hyperbolas pattern of underground utilities have also been highlighted.
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