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Partial discharge detection and location technique based on segmented correlation trimmed mean algorithm for power cable
No full textDoctor of Philosophy in Electrical Systems EngineeringPower cable may suffer from insulation degradation after a certain period of time because of environment, mechanical and electrical factors. Partial discharge (PD) at void or cavity of power cable’s insulation will lead to the power system breakdown in the near future. Nowadays, many PD location devices had been invented to estimate PD location on power cable. New technology has enabled PD estimation to evolve from offline PD estimation to online PD estimation. Advanced signal processing technique can be implemented in those devices in order to estimate PD location accurately. In this thesis, segmented correlation trimmed mean (SCTM) algorithm is proposed to estimate PD location on medium voltage (MV) power cable. The algorithm uses segmented correlation
technique and trimmed mean data filtering technique to enhance the accuracy of the
estimated PD location. Two experiments have been performed to test the program
execution time and accuracy against noise of the algorithm. The algorithm had been tested
in Matrix Laboratory (MATLAB) environment which consists modelled PD signals and
different levels of white Gaussian noise (WGN) and discrete spectral interference (DSI).
Discrete wavelet transform (DWT) de-nosing technique has been used for noise
suppression. The first experiment is performed by increasing the sampling number of
measured signal while recording the program execution time of the algorithm. The second
experiment is performed by increasing the level of WGN and DSI while recording the
maximum percentage error of the estimated PD location. The results from both
experiments are compared with the existing multi-end correlation (MEC) algorithm. The
results shown that the SCTM algorithm has longer time but lower maximum percentage
error of the estimated PD location than MEC algorithm. In conclusion, SCTM algorithm
is more suitable to apply in PD location estimation system for power cable due to its lower
maximum percentage error
Design of a hybrid controller for solar and ocean wave energy harvester
No full textDoctor of Philosophy in Electrical Systems EngineeringThis thesis presents an approach of hybrid system implementation between Photovoltaic
and ocean waves. These renewable energy sources are abundant, clean and beneficial
compared to existing fossil fuel. Common method of extracting energy from these
sources normally utilized a single energy source for energy production. Through hybrid
system, two or more sources integration is possible. Merging of multiples energy
sources will complement and support any inadequacy attribute accompanying these
sources. However, system complexity will increase as source increases resulting in
complicated system. Thus a proper controlling method is required for effective source
management. Therefore, this research was initiated to develop a controller for two
system harvester modules, Photovoltaic and wave energy converter for hybrid power
system. The controller should fully exploit energy potential characteristic by harnessing
it to the maximum. This research provides an effective method of harvesting
Photovoltaic and ocean waves. Photovoltaic source is dependable toward sun intensity
while the ocean waves’ intermittent energy is unsuitable through conventional
harvesting method. The established controller will integrate Photovoltaic and ocean
waves and compensate power fluctuations. Proper integration was successfully executed
through buck and boost converter module. The wave energy converter module was
developed using ratchet mechanism and the generator unit was extracted from mini
ceiling fan motor. An additional monitoring system was added and performs wireless
transmission to operator. The developed Photovoltaic and Wave Energy Converter
(WEC) sources progress rapidly with average power produced are 76.91mW and
82.237W respectively. The proposed controller excels in performance and produce
effective hybrid energy management with measured with power extraction efficiency at
58%. The hybrid system was successfully executed within the prescribe scope
boundaries
Design and development of a motor imagery based interfaces of wheelchair in a simulated virtual environment
No full textMaster of Science in Mechatronic EngineeringPatients suffering from diseases like motor neuron diseases (MND), or trauma such as spinal cord injury (SCI), and amputation are not able to move. Presented is a work on combining the power wheelchair designed to aid the movement of disabled patient and a Brain Computer Interface (BCI) can be used to replace conventional joystick so that it can be controlled without using hands. By using the BCI, the brain signal emanated during Motor Imagery (MI) tasks can be converted into control signal for power wheelchair maneuvering. In this research, five subjects are requested to perform six Kinesthetic Motor Imagery tasks plus one relax task and the Electroencephalography (EEG) signals are recorded. Elliptic filter was used to remove power line noise. The
proposed feature, combined feature of Fractal Dimension with Mel-frequency Cepstral
Coefficients has outperformed the others. It was able to improve the classification
performance to a satisfactory level especially for the subject 3 which yielded relatively
poor result by using four other feature extraction methods. The classifiers network
parameters were experimentally selected and the Levenberg-Marquardt training
algorithm was used to train the networks. The Multilayer Perceptron Neural Network
(MLPNN) outperformed Elman Recurrent Neural Network and Nonlinear
Autoregressive Exogenous model (NARX) with average accuracy of 91.7%. The
developed network models was further tested and evaluated with two simulated virtual
environment created by using MATLAB graphical user interface (GUI). The simulation
results suggested that step by step control is better than continuous control of
wheelchair, and also the proposed feature, combined feature of FD with MFCCs and
MLPNN can be used to classify Motor Imagery signal for directional control of
powered wheelchair
