Showing papers by "Azura Che Soh published in 2016"

DC-link capacitor voltage control for single-phase shunt active power filter with step size error cancellation in self-charging algorithm

Abstract: This study presents an improved self-charging algorithm by introducing a new feature known as step size error cancellation for better performance of DC-link capacitor voltage control in single-phase shunt active power filter (SAPF). Previous works of self-charging algorithms were focused only for steady-state operation by using either proportional-integral (PI) or fuzzy logic control (FLC). However, in a certain operation of any power system, dynamic operation may also happen. Thus, by introducing step size error cancellation as an additional feature to the self-charging algorithm, both steady state and dynamic operations can be covered. For evaluation and comparison analysis, self-charging with PI and FLC algorithms have been developed too. All the algorithms were simulated in MATLAB-Simulink, respectively, together with the single-phase SAPF. For hardware implementation, the proposed algorithm was programmed in TMS320F28335 digital signal processing board. The other two conventional self-charging algorithms were also programmed for comparison purposes. From the results and analysis, the proposed self-charging with step size error cancellation shows the best performance with high accuracy, fast response time and less overshoot and undershoot. It performs well in both steady state and dynamic operations as compared with both previous self-charging techniques which only work well in steady-state operation.

Fundamental Active Current Adaptive Linear Neural Networks for Photovoltaic Shunt Active Power Filters

Abstract: This paper presents improvement of a harmonics extraction algorithm, known as the fundamental active current (FAC) adaptive linear element (ADALINE) neural network with the integration of photovoltaic (PV) to shunt active power filters (SAPFs) as active current source. Active PV injection in SAPFs should reduce dependency on grid supply current to supply the system. In addition, with a better and faster harmonics extraction algorithm, the SAPF should perform well, especially under dynamic PV and load conditions. The role of the actual injection current from SAPF after connecting PVs will be evaluated, and the better effect of using FAC ADALINE will be confirmed. The proposed SAPF was simulated and evaluated in MATLAB/Simulink first. Then, an experimental laboratory prototype was also developed to be tested with a PV simulator (CHROMA 62100H-600S), and the algorithm was implemented using a TMS320F28335 Digital Signal Processor (DSP). From simulation and experimental results, significant improvements in terms of total harmonic distortion (THD), time response and reduction of source power from grid have successfully been verified and achieved.

Simplified Adaptive Linear Neuron Harmonics Extraction Algorithm for Dynamic Performance of Shunt Active Power Filter

Abstract: This paper presents a single-phase shunt active power filter with artificial intelligence based algorithms by introducing a new Simplified Adaptive Linear Neuron (ADALINE) algorithm for harmonics extraction. The proposed harmonics extraction is an innovative work over the established algorithm, known as Modified Widrow-Hoff ADALINE algorithm. Three significant major improvements are made by modifying weight updating technique which leads to introduction of fundamental active current updating technique, removing cosine component according to symmetrical theory of periodic signal, and minimizing large average square error by removing sum of elements. The SAPF with the proposed harmonics extraction algorithm was simulated in MATLAB/Simulink. For practical validation, the experimental laboratory prototype was also developed, and the algorithm was implemented using Digital Signal Processor TMS320F28335. Both simulation and experimental tests cover different types of nonlinear loads and dynamic operation of loads. From simulation and experimental results, significant improvements in terms of Total Harmonic Distortion and response time are shown together with comparison analysis with the established algorithm.

Adaptive Hybrid Fuzzy-Proportional Plus Crisp-Integral Current Control Algorithm for Shunt Active Power Filter Operation

Abstract: An adaptive hybrid fuzzy-proportional plus crisp-integral current control algorithm (CCA) for regulating supply current and enhancing the operation of a shunt active power filter (SAPF) is presented. It introduces a unique integration of fuzzy-proportional (Fuzzy-P) and crisp-integral (Crisp-I) current controllers. The Fuzzy-P current controller is developed to perform gain tuning procedure and proportional control action. This controller inherits the simplest configuration; it is constructed using a single-input single-output fuzzy rule configuration. Thus, an execution of few fuzzy rules is sufficient for the controller’s operation. Furthermore, the fuzzy rule is developed using the relationship of currents only. Hence, it simplifies the controller development. Meanwhile, the Crisp-I current controller is developed to perform integral control action using a controllable gain value; to improve the steady-state control mechanism. The gain value is modified and controlled using the Fuzzy-P current controller’s output variable. Therefore, the gain value will continuously be adjusted at every sample period (or throughout the SAPF operation). The effectiveness of the proposed CCA in regulating supply current is validated in both simulation and experimental work. All results have proven that the SAPF using the proposed CCA is capable to regulate supply current during steady-state and dynamic-state operations. At the same time, the SAPF is able to enhance its operation in compensating harmonic currents and reactive power. Furthermore, the implementation of the proposed CCA has resulted more stable dc-link voltage waveform.

Improved ADALINE harmonics extraction algorithm for boosting performance of photovoltaic shunt active power filter under dynamic operations

Abstract: This paper presents improved harmonics extraction based on Adaptive Linear Neuron (ADALINE) algorithm for single phase photovoltaic (PV) shunt active power filter (SAPF). The proposed algorithm, named later as Improved ADALINE, contributes to better performance by removing cosine factor and sum of element that are considered as unnecessary features inside the existing algorithm, known as Modified Widrow-Hoff (W-H) ADALINE. A new updating technique, named as Fundamental Active Current, is introduced to replace the role of the weight factor inside the previous updating technique. For evaluation and comparison purposes, both proposed and existing algorithms have been developed. The PV SAPF with both algorithms was simulated in MATLABSimulink respectively, with and without operation or connection of PV. For hardware implementation, laboratory prototype has been developed and the proposed algorithm was programmed in TMS320F28335 DSP board. Steady state operation and three critical dynamic operations, which involve change of nonlinear loads, off-on operation between PV and SAPF, and change of irradiances, were carried out for performance evaluation. From the results and analysis, the Improved ADALINE algorithm shows the best performances with low total harmonic distortion, fast response time and high source power reduction. It performs well in both steady state and dynamic operations as compared to the Modified W-H ADALINE algorithm.

Quality identification of used cooking oil based on feature fusion of gas sensor and color

Abstract: In Malaysia, a recognition system to identify the hazardous substance in used cooking oil is still at very minimal stage. The presents of HNE in reused cooking oil increases the health concern in our modern community and assessment of the oil quality had received much attention, since such assessment is useful in determining the discarding point of the oils. This paper primarily present the result of quality identification system of used cooking oil through an image analysis that uses RGB color extraction technique and odor extraction techniques to extract a set of feature vectors. The result obtained after implementing an ANN technique to identify quality level of the oil shows the outcome of the trained ANN was successful because of a high regression coefficient of 0.98. Furthermore, the system had successfully classified the randomly selected ten (10) samples for testing correctly.