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You searched for +publisher:"Rochester Institute of Technology" +contributor:("Abdulla Ismail"). Showing records 1 – 3 of 3 total matches.

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Rochester Institute of Technology

1. Al-Zabin, Omar. Rotor Current Control Design for DFIG-based Wind Turbine Using PI, FLC and Fuzzy PI Controllers.

Degree: MS, Electrical Engineering, 2019, Rochester Institute of Technology

Due to the rising demand for electricity with increasing world population, maximizing renewable energy capture through efficient control systems is gaining attention in literature. Wind energy, in particular, is considered the world’s fastest-growing energy source it is one of the most efficient, reliable and affordable renewable energy sources. Subsequently, well-designed control systems are required to maximize the benefits, represented by power capture, of wind turbines. In this thesis, a 2.0-MW Doubly-Fed Induction Generator (DFIG) wind turbine is presented along with new controllers designed to maximize the wind power capturer. The proposed designs mainly focus on controlling the DFIG rotor current in order to allow the system to operate at a certain current value that maximizes the energy capture at different wind speeds. The simulated model consists of a single two-mass wind turbine connected directly to the power grid. A general model consisting of aerodynamic, mechanical, electrical, and control systems are simulated using Matlab/Simulink. An indirect speed controller is designed to force the aerodynamic torque to follow the maximum power curve in response to wind variations, while a vector controller for current loops is designed to control the rotor side converter. The control system design techniques considered in this work are Proportional-Integral (PI), fuzzy logic, and fuzzy-PI controllers. The obtained results show that the fuzzy-PI controller meets the required specifications by exhibiting the best steady-state response, in terms of steady-state error and settling time, for some DFIG parameters such as rotor speed, rotor currents and electromagnetic torque. Although the fuzzy logic controller exhibits smaller peak overshoot and undershoot values when compared to the fuzzy-PI, the peak value difference is very small, which can be compensated using protection equipment such as circuit breakers and resistor banks. On the other hand, the PI controller shows the highest overshoot, undershoot and settling time values, while the fuzzy logic controller does not meet the requirements as it exhibits large, steady-state error values. Advisors/Committee Members: Abdulla Ismail.

Subjects/Keywords: DFIG-based wind turbine; Fuzzy PI controllers; Rotor current control

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Al-Zabin, O. (2019). Rotor Current Control Design for DFIG-based Wind Turbine Using PI, FLC and Fuzzy PI Controllers. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10325

Chicago Manual of Style (16th Edition):

Al-Zabin, Omar. “Rotor Current Control Design for DFIG-based Wind Turbine Using PI, FLC and Fuzzy PI Controllers.” 2019. Masters Thesis, Rochester Institute of Technology. Accessed March 07, 2021. https://scholarworks.rit.edu/theses/10325.

MLA Handbook (7th Edition):

Al-Zabin, Omar. “Rotor Current Control Design for DFIG-based Wind Turbine Using PI, FLC and Fuzzy PI Controllers.” 2019. Web. 07 Mar 2021.

Vancouver:

Al-Zabin O. Rotor Current Control Design for DFIG-based Wind Turbine Using PI, FLC and Fuzzy PI Controllers. [Internet] [Masters thesis]. Rochester Institute of Technology; 2019. [cited 2021 Mar 07]. Available from: https://scholarworks.rit.edu/theses/10325.

Council of Science Editors:

Al-Zabin O. Rotor Current Control Design for DFIG-based Wind Turbine Using PI, FLC and Fuzzy PI Controllers. [Masters Thesis]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/10325


Rochester Institute of Technology

2. AlMarri, Salem Bin Saqer. Real-Time Facial Emotion Recognition Using Fast R-CNN.

Degree: MS, Electrical Engineering, 2019, Rochester Institute of Technology

In computer vision and image processing, object detection algorithms are used to detect semantic objects of certain classes of images and videos. Object detector algorithms use deep learning networks to classify detected regions. Unprecedented advancements in Convolutional Neural Networks (CNN) have led to new possibilities and implementations for object detectors. An object detector which uses a deep learning algorithm detect objects through proposed regions, and then classifies the region using a CNN. Object detectors are computationally efficient unlike a typical CNN which is computationally complex and expensive. Object detectors are widely used for face detection, recognition, and object tracking. In this thesis, deep learning based object detection algorithms are implemented to classify facially expressed emotions in real-time captured through a webcam. A typical CNN would classify images without specifying regions within an image, which could be considered as a limitation towards better understanding the network performance which depend on different training options. It would also be more difficult to verify whether a network have converged and is able to generalize, which is the ability to classify unseen data, data which was not part of the training set. Fast Region-based Convolutional Neural Network, an object detection algorithm; used to detect facially expressed emotion in real-time by classifying proposed regions. The Fast R-CNN is trained using a high-quality video database, consisting of 24 actors, facially expressing eight different emotions, obtained from images which were processed from 60 videos per actor. An object detector’s performance is measured using various metrics. Regardless of how an object detector performed with respect to average precision or miss rate, doing well on such metrics would not necessarily mean that the network is correctly classifying regions. This may result from the fact that the network model has been over-trained. In our work we showed that object detector algorithm such as Fast R-CNN performed surprisingly well in classifying facially expressed emotions in real-time, performing better than CNN. Advisors/Committee Members: Abdulla Ismail.

Subjects/Keywords: Artificial intelligence; Convolutional neural network; Facial emotion recognition

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

AlMarri, S. B. S. (2019). Real-Time Facial Emotion Recognition Using Fast R-CNN. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10214

Chicago Manual of Style (16th Edition):

AlMarri, Salem Bin Saqer. “Real-Time Facial Emotion Recognition Using Fast R-CNN.” 2019. Masters Thesis, Rochester Institute of Technology. Accessed March 07, 2021. https://scholarworks.rit.edu/theses/10214.

MLA Handbook (7th Edition):

AlMarri, Salem Bin Saqer. “Real-Time Facial Emotion Recognition Using Fast R-CNN.” 2019. Web. 07 Mar 2021.

Vancouver:

AlMarri SBS. Real-Time Facial Emotion Recognition Using Fast R-CNN. [Internet] [Masters thesis]. Rochester Institute of Technology; 2019. [cited 2021 Mar 07]. Available from: https://scholarworks.rit.edu/theses/10214.

Council of Science Editors:

AlMarri SBS. Real-Time Facial Emotion Recognition Using Fast R-CNN. [Masters Thesis]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/10214


Rochester Institute of Technology

3. Khallaf, Mohamed. Enhanced MPPT Controllers for Smart Grid Applications.

Degree: MS, Electrical Engineering, 2019, Rochester Institute of Technology

Over the past years, the energy demand has been steadily growing and so methods of how to cope with this staggering increase are being researched and utilized. One method of injecting more energy to the grid is renewable energy, which has become in recent years an integral part of any country’s power generation plan. Thus, it is a necessity to enhance renewable energy resources and maximize their grid utilization, so that these resources can step up and reduce the over dependency of global energy production on depleting energy resources. This thesis focuses on solar power and effective means to enhance its efficiency through the use of different controllers. In this regard, substantial research efforts have been done. However, due to the current market and technological development, more options are made available that are able to boast the efficiency and utilization of renewables in the power mix. In this thesis, an enhanced maximum power point tracking (MPPT) controller has been designed as part of a Photovoltaic (PV) system to generate maximum power to satisfy load demand. The PV system is designed and simulated using MATLAB (consisting of a solar panel array, MPPT controller, boost converter, and a resistive load). The solar panel chosen for the array is Sun Power SPR- 440NE-WHT-D and the array is designed to produce 150 kW of power. The MPPT controller is designed using three different algorithms and the results are compared to identify each controller’s fortes and drawbacks. The three designed controllers used are based on Perturb and Observe (P&O) algorithm, Incremental Conductance (INC) with an Integral Regulator (IR) and Fuzzy Logic Control (FLC). Each controller was tested under two different scenarios; the first is when the panel array is subjected to constant amount of solar irradiance along with a constant atmospheric temperature and the second scenario has varying solar irradiance and atmospheric temperature. The performance of these controllers is analyzed and compared in terms of the output power efficiency, system dynamic response and finally the oscillations behavior. After analyzing the results, it is shown that Fuzzy Logic Controller design performed better compared to the other controllers as it had in most cases the highest mean power efficiency and fastest response. Advisors/Committee Members: Abdulla Ismail.

Subjects/Keywords: Fuzzy logic; Incremental conductance; MPPT; P&O; PV; Renewable energy

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Khallaf, M. (2019). Enhanced MPPT Controllers for Smart Grid Applications. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10018

Chicago Manual of Style (16th Edition):

Khallaf, Mohamed. “Enhanced MPPT Controllers for Smart Grid Applications.” 2019. Masters Thesis, Rochester Institute of Technology. Accessed March 07, 2021. https://scholarworks.rit.edu/theses/10018.

MLA Handbook (7th Edition):

Khallaf, Mohamed. “Enhanced MPPT Controllers for Smart Grid Applications.” 2019. Web. 07 Mar 2021.

Vancouver:

Khallaf M. Enhanced MPPT Controllers for Smart Grid Applications. [Internet] [Masters thesis]. Rochester Institute of Technology; 2019. [cited 2021 Mar 07]. Available from: https://scholarworks.rit.edu/theses/10018.

Council of Science Editors:

Khallaf M. Enhanced MPPT Controllers for Smart Grid Applications. [Masters Thesis]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/10018

.