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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
URL: https://scholarworks.rit.edu/theses/10325 
► Due to the rising demand for electricity with increasing world population, maximizing renewable energy capture through efficient control systems is gaining attention in literature.…
(more)
▼ 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 (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 January 19, 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. 19 Jan 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 Jan 19].
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
URL: https://scholarworks.rit.edu/theses/10214
► In computer vision and image processing, object detection algorithms are used to detect semantic objects of certain classes of images and videos. Object detector…
(more)
▼ 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 (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 January 19, 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. 19 Jan 2021.
Vancouver:
AlMarri SBS. Real-Time Facial Emotion Recognition Using Fast R-CNN. [Internet] [Masters thesis]. Rochester Institute of Technology; 2019. [cited 2021 Jan 19].
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
URL: https://scholarworks.rit.edu/theses/10018
► Over the past years, the energy demand has been steadily growing and so methods of how to cope with this staggering increase are being…
(more)
▼ 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 (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 January 19, 2021.
https://scholarworks.rit.edu/theses/10018.
MLA Handbook (7th Edition):
Khallaf, Mohamed. “Enhanced MPPT Controllers for Smart Grid Applications.” 2019. Web. 19 Jan 2021.
Vancouver:
Khallaf M. Enhanced MPPT Controllers for Smart Grid Applications. [Internet] [Masters thesis]. Rochester Institute of Technology; 2019. [cited 2021 Jan 19].
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
4.
Hamadi, Amer Mahdy.
Autonomous Quadrotor Control Using Convolutional Neural Networks.
Degree: MS, Electrical Engineering, 2019, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/9995
► Quadrotors are considered nowadays one of the fastest growing technologies. It is entering all fields of life making them a powerful tool to serve…
(more)
▼ Quadrotors are considered nowadays one of the fastest growing technologies. It is entering all fields of life making them a powerful tool to serve humanity and help in developing a better life style. It is crucial to experiment all possible ways of controlling quadrotors, starting from classical methodologies to cutting edge modern technologies to serve their purpose. In most of the times quadrotors would have combination of several technologies on board. The attitude angles and altitude control used in this thesis are based mainly on PID control which is modeled and simulated on MATLAB and Simulink. To control the quadrotor behavior for two different tasks, Obstacle Avoidance and Command by Hand Gesture, the use of Convolutional Neural Networks (CNN) was proposed, since this new
technology had shown very impressive results in image recognition in recent years.
A considerable amount of training images (datasets) were created for the two tasks. Training and testing of the CNN were performed for these datasets, and real time flight experiments were performed, using a ground station, a Arduino microcontroller and interface circuit connected to the quadrotor. Results of the experiments show an excellent error rates for both tasks. The system performance reflects a major advantage of scalability for classification for new classes and other complex tasks, towards an autonomous flying and more intelligent behavior of quadrotors.
Advisors/Committee Members: Abdulla Ismail.
Subjects/Keywords: None provided
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APA (6th Edition):
Hamadi, A. M. (2019). Autonomous Quadrotor Control Using Convolutional Neural Networks. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9995
Chicago Manual of Style (16th Edition):
Hamadi, Amer Mahdy. “Autonomous Quadrotor Control Using Convolutional Neural Networks.” 2019. Masters Thesis, Rochester Institute of Technology. Accessed January 19, 2021.
https://scholarworks.rit.edu/theses/9995.
MLA Handbook (7th Edition):
Hamadi, Amer Mahdy. “Autonomous Quadrotor Control Using Convolutional Neural Networks.” 2019. Web. 19 Jan 2021.
Vancouver:
Hamadi AM. Autonomous Quadrotor Control Using Convolutional Neural Networks. [Internet] [Masters thesis]. Rochester Institute of Technology; 2019. [cited 2021 Jan 19].
Available from: https://scholarworks.rit.edu/theses/9995.
Council of Science Editors:
Hamadi AM. Autonomous Quadrotor Control Using Convolutional Neural Networks. [Masters Thesis]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/9995
5.
Emara, Samar.
Control of PV Connected Power Grid using LQR and Fuzzy Logic Control.
Degree: MS, Electrical Engineering, 2018, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/9734
► As the contribution of renewable energy to the current power grid is becoming an essential part of the global energy system, it is of…
(more)
▼ As the contribution of renewable energy to the current power grid is becoming an essential part of the global energy system, it is of critical importance to study the effects of this increased penetration of the renewable sources on the power system. Focusing on solar energy, its intermittent nature makes it difficult to predict the output when connecting to the power grid. Therefore, well-structured control methods should be used to assure a continuous and steady system performance with regard to the system frequency variation.
In this thesis, a PV system is modelled and connected to a grid served by a conventional thermal power system with 45% penetration level. Then, the system frequency errors due to load changes are studied in this PV connected power grid. Appropriate and effective controllers are designed to regulate these errors to keep the system response within the required specifications.
In addition, single-area as well as two-area interconnected power systems are considered in this research. The power exchange among the two areas will add another significant parameter that is essential in the efficient operation of the system and that affects the behavior of the system in terms of the frequency error response.
Two advanced control methods, namely Linear Quadratic Regulator (LQR) and Fuzzy Logic Control (FLC) are applied to control the single-area and the two-area systems. The appropriate controllers are designed, assessed and the responses are analyzed and compared. These designed controllers demonstrated a superior performance in the controlled system by achieving the required specifications of undershoot, settling time and steady state error for the system frequency. For the single-area PV connected power system, the LQR controller gave the best response in comparison to the two other types of controllers, while in the two-area system the fuzzy logic controller was the most suitable as it met the specifications to the best possible extent.
Advisors/Committee Members: Abdulla Ismail.
Subjects/Keywords: Feedback control; Fuzzy logic; Linear quadratic regulator; Load frequency control; Photovoltaic; Power system
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APA (6th Edition):
Emara, S. (2018). Control of PV Connected Power Grid using LQR and Fuzzy Logic Control. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9734
Chicago Manual of Style (16th Edition):
Emara, Samar. “Control of PV Connected Power Grid using LQR and Fuzzy Logic Control.” 2018. Masters Thesis, Rochester Institute of Technology. Accessed January 19, 2021.
https://scholarworks.rit.edu/theses/9734.
MLA Handbook (7th Edition):
Emara, Samar. “Control of PV Connected Power Grid using LQR and Fuzzy Logic Control.” 2018. Web. 19 Jan 2021.
Vancouver:
Emara S. Control of PV Connected Power Grid using LQR and Fuzzy Logic Control. [Internet] [Masters thesis]. Rochester Institute of Technology; 2018. [cited 2021 Jan 19].
Available from: https://scholarworks.rit.edu/theses/9734.
Council of Science Editors:
Emara S. Control of PV Connected Power Grid using LQR and Fuzzy Logic Control. [Masters Thesis]. Rochester Institute of Technology; 2018. Available from: https://scholarworks.rit.edu/theses/9734
6.
Baburajan, Silpa.
Pitch Control of Wind Turbine through PID, Fuzzy and adaptive Fuzzy-PID controllers.
Degree: MS, Electrical Engineering, 2017, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/9633
► As the penetration of the wind energy into the electrical power grid is extensively increased, the influence of the wind turbine systems on the…
(more)
▼ As the penetration of the wind energy into the electrical power grid is extensively increased, the influence of the wind turbine systems on the frequency and voltage stability becomes more and more significant. Wind turbine rotor bears different types of loads; aerodynamic loads, gravitational loads and centrifugal loads. These loads cause fatigue and vibration in blades, which cause degradation to the rotor blades. These loads can be overcome and the amount of collected power can be controlled using a good pitch controller (PC) which will tune the attack angle of a wind turbine rotor blade into or out of the wind. Each blade is exposed to different loads due to the variation of the wind speed across the rotor blades. For this reason, individual electric drives can be used in future to control the pitch of the blades in a process called Individual Pitch Control. In this thesis work, an enhanced pitch angle control strategy based on fuzzy logic control is proposed to cope with the nonlinear characteristics of wind turbine as well as to reduce the loads on the blades. A mathematical model of wind turbine (pitch control system) is developed and is tested with three controllers -PID, Fuzzy, and Adaptive Fuzzy-PID. After comparing all the three proposed strategies, the simulation results show that the Adaptive Fuzzy-PID controller has the best performance as it regulates the pitch system as well as the disturbances and uncertain factors associated with the system.
Advisors/Committee Members: Abdulla Ismail.
Subjects/Keywords: Adaptive fuzzy PID controller; Fuzzy controller; PID controller; Pitch angle; Wind energy
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APA (6th Edition):
Baburajan, S. (2017). Pitch Control of Wind Turbine through PID, Fuzzy and adaptive Fuzzy-PID controllers. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9633
Chicago Manual of Style (16th Edition):
Baburajan, Silpa. “Pitch Control of Wind Turbine through PID, Fuzzy and adaptive Fuzzy-PID controllers.” 2017. Masters Thesis, Rochester Institute of Technology. Accessed January 19, 2021.
https://scholarworks.rit.edu/theses/9633.
MLA Handbook (7th Edition):
Baburajan, Silpa. “Pitch Control of Wind Turbine through PID, Fuzzy and adaptive Fuzzy-PID controllers.” 2017. Web. 19 Jan 2021.
Vancouver:
Baburajan S. Pitch Control of Wind Turbine through PID, Fuzzy and adaptive Fuzzy-PID controllers. [Internet] [Masters thesis]. Rochester Institute of Technology; 2017. [cited 2021 Jan 19].
Available from: https://scholarworks.rit.edu/theses/9633.
Council of Science Editors:
Baburajan S. Pitch Control of Wind Turbine through PID, Fuzzy and adaptive Fuzzy-PID controllers. [Masters Thesis]. Rochester Institute of Technology; 2017. Available from: https://scholarworks.rit.edu/theses/9633
Rochester Institute of Technology
7.
Naghshineh, Nastaran.
Control of Thermal Power System Using Adaptive Fuzzy Logic Control.
Degree: MS, Electrical Engineering, 2017, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/9615
► Controlling thermal power systems increases the overall system efficiency and satisfies the desired requirements. In such a large system, fuel reduction of even a…
(more)
▼ Controlling thermal power systems increases the overall system efficiency and satisfies the desired requirements. In such a large system, fuel reduction of even a small percentage leads to large energy saving. Hence, power systems are gaining significant attention from engineers and scientists.
In this thesis, the uncontrolled power system for single area, two area, and three area is modelled using state space representation. Frequency deviation is simulated using MATLAB and SIMULINK. PID control is added to the system to analyze the effect of conventional control on system output response. Adaptive fuzzy logic control is added to the uncontrolled system using MATLAB Fuzzy Inference System and its effect on the system output response is measured in terms of overshoot/undershoot percentage, settling time, and steady state frequency error. Effect of adaptive fuzzy logic control is analyzed on single area, two area, and three area power system. Tie-line power exchange among areas is investigated before and after implementation of PID and adaptive fuzzy logic control.
For the purpose of comparison in this thesis, a conventional PID control and an adaptive fuzzy logic control is applied to two different thermal power systems. The simulations demonstrate that adaptive fuzzy logic control is proved to be more efficient and reliable than conventional PID control in power system control problem.
Advisors/Committee Members: Abdulla Ismail.
Subjects/Keywords: Adaptive fuzzy logic control; Control; Fuzzy logic; Multi-area power system; PID; Thermal power system
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APA (6th Edition):
Naghshineh, N. (2017). Control of Thermal Power System Using Adaptive Fuzzy Logic Control. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9615
Chicago Manual of Style (16th Edition):
Naghshineh, Nastaran. “Control of Thermal Power System Using Adaptive Fuzzy Logic Control.” 2017. Masters Thesis, Rochester Institute of Technology. Accessed January 19, 2021.
https://scholarworks.rit.edu/theses/9615.
MLA Handbook (7th Edition):
Naghshineh, Nastaran. “Control of Thermal Power System Using Adaptive Fuzzy Logic Control.” 2017. Web. 19 Jan 2021.
Vancouver:
Naghshineh N. Control of Thermal Power System Using Adaptive Fuzzy Logic Control. [Internet] [Masters thesis]. Rochester Institute of Technology; 2017. [cited 2021 Jan 19].
Available from: https://scholarworks.rit.edu/theses/9615.
Council of Science Editors:
Naghshineh N. Control of Thermal Power System Using Adaptive Fuzzy Logic Control. [Masters Thesis]. Rochester Institute of Technology; 2017. Available from: https://scholarworks.rit.edu/theses/9615
8.
Sattar, Muhammad Awais.
Adaptive Fuzzy Control of Quadrotor.
Degree: MS, 2017, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/9618
► In this thesis, intelligent controllers are designed to control attitude for quadrotor UAV (Unmanned Aerial Vehicle).Quadrotors have a variety of applications in real time…
(more)
▼ In this thesis, intelligent controllers are designed to control attitude for quadrotor UAV (Unmanned Aerial Vehicle).Quadrotors have a variety of applications in real time e.g. surveillance, inspection, search, rescue and reducing the human force in undesirable conditions. Quadrotors are generally unstable systems; the kinematics of quadrotor resembles the kinematics of inverted pendulum. In order to avoid the possibility of any kind of damages, the mathematical model of quadrotor should be developed and after that, the different control techniques can be implemented. This thesis presents a detailed simulation model for a Quadrotor. For the control purpose, three classical and modern control strategies are separately implemented which are PID, Fuzzy, and Adaptive Fuzzy PID for four basic motions roll, pitch, yaw, and Z/ Height. For better performance, error reduction and easy tuning, this thesis introduces individual controllers for all basic motion of a Quadrotor. The modeling and control is done using MATLAB/Simulink. The main objective of this thesis is to get the desired output with respect to the desired the input. At the end, simulation results are compared to check which controller acts the best for the developed Quadrotor model
Advisors/Committee Members: Abdulla Ismail.
Subjects/Keywords: Adaptive fuzzy PD; Dynamics; Fuzzy logic control; PD control; Roll pitch yaw; UAV
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APA ·
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APA (6th Edition):
Sattar, M. A. (2017). Adaptive Fuzzy Control of Quadrotor. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9618
Chicago Manual of Style (16th Edition):
Sattar, Muhammad Awais. “Adaptive Fuzzy Control of Quadrotor.” 2017. Masters Thesis, Rochester Institute of Technology. Accessed January 19, 2021.
https://scholarworks.rit.edu/theses/9618.
MLA Handbook (7th Edition):
Sattar, Muhammad Awais. “Adaptive Fuzzy Control of Quadrotor.” 2017. Web. 19 Jan 2021.
Vancouver:
Sattar MA. Adaptive Fuzzy Control of Quadrotor. [Internet] [Masters thesis]. Rochester Institute of Technology; 2017. [cited 2021 Jan 19].
Available from: https://scholarworks.rit.edu/theses/9618.
Council of Science Editors:
Sattar MA. Adaptive Fuzzy Control of Quadrotor. [Masters Thesis]. Rochester Institute of Technology; 2017. Available from: https://scholarworks.rit.edu/theses/9618
. 
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