Thesis

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    FAULT DETECTION AND LOCALIZATION METHOD FOR INVERTER OPEN-CIRCUIT CONDITION IN THREE-PHASE INDUCTION MOTOR DRIVES
    (2019) AMIR RASYADAN BIN KELANA
    Recent advancement in semiconductor technologies have extended the use of induction motors in a wide area of variable speed applications. Traditional fixed speed drives are becoming obsolete and majority of the applications now require power electronic inverter-based drives for a more efficient and precise speed control. Nonetheless, the use of power electronic came with an increased possibility of the drive system failure, mainly caused by the switching device itself. There are two common types of inverter faults which are short-circuit and open-circuit fault. Short-circuit fault detection methods are mostly hardware based while open-circuit faults require proper algorithm to be detected. The aim of this research work is to provide a simulation analysis on the inverter fault detection algorithm to detect open-circuit fault together with fault localization algorithm to locate the open-circuit switch in a three-phase induction motor drive. Computer simulation model was built using MATLAB Simulink to simulate the operation of three-phase induction motor drive under healthy and faulty inverter conditions. The sub models of the drive system include an induction motor direct-quadrature (DQ) model, a voltage source inverter (VSI) driven by an open-loop SPWM and a graphical user interface (GUI) to allow user control on the input parameters during simulation run. Dynamic behavior of the motor speed, phase current and electromagnetic torque under healthy and the combinations of single and double open-circuit fault conditions were simulated. The simulated phase currents were then pre-processed to filter the signal from multiple harmonic content induced by the PWM-switching process before being used for features extraction. This work focuses on current vector trajectory based fault detection and localization (FDL) method, two separate algorithms are used to detect the faulty inverter leg and to localize the faulty switch within the faulty leg. Fault detection was done using the modified slope calculation method of the phase currents in the complex DQ plane, while the xx localization strategy is based on the observation of the phase currents polarity. 21 combinations of single and double open-circuit faults were simulated. The results have shown that the FDL method is capable to detect and localize all the 21 fault conditions. Since the fault pattern may appear at different current angle, the detection and localization speed depend on the fault occurrence angle which varies within one phase current period. The algorithm also proved to work under variable load and frequency conditions. Overall, this work has shown the working principle and the capability of the current vector trajectory based FDL algorithm to detect and to localize the 21 open-circuit fault conditions. The simulation analysis done in this work would be useful for future development of the FDL algorithm.
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    OPTIMIZATION OF EMBEDDED PLATFORM IN COLLABORATIVE CAMERA APPLICATION FOR DISTRIBUTED FACE DETECTION
    (2018-05) MOHD KHAIRI BIN KAMARUDDIN
    This thesis presented an optimization technique for embedded platform in a collaborative camera for distributed face tracking system. In this work various method,hardware and its configuration for face detection and recognition has been reviewed and implemented. Local Binary Pattern is used as feature extraction method for face detection algorithm. Then Fisherface (Linear Discriminant Analysis) has been implemented for face recognition algorithm. In terms of embedded platform, Raspberry Pi2 has been chosen as a preferred platform to run the system because it is compatible with OpenMP multiprocessing method. The advantage of Raspberry Pi 2 is it has the most number of core and the lowest cost when compare to the other embedded platform which was tested. Several enhancements have been done to the embedded platform which enable multiprocessing for OpenCV libraries using OpenMP and Random Access Memory tuning whereby mounting the log files of the operating system to the cache memory. Every node contains algorithms using Local Binary Pattern as feature extraction method for face detection and Fisherface as recognition technique. Alternate loop has been applied to the system avoiding vision algorithm for every two out of three times. By doing this, the processing time is reduced without compromising the performance of the nodes. This enhancement also further gives a big impact on reducing face detection speed rate. The result shows the optimization of the embedded platform allow the program to increase the number of frame per second from 3 frame per second to 13 frame per second without compromising the number of detection per second which is from 4 to 3 detections. Futher work on the algorithm shows its capability of the collaborative camera to share and centralize the logging information between cameras regarding the moving of samples and gives a warning if suspicious unregistered sample intrude to surveillance area.