Design and control of a direct drive slotless permanent magnet alternating current generator for low speed Bristol cylinder wave device

Global demand for renewable energy is at an all-time high. Renewable energy can be extracted from naturally available resources such solar, wind, tides, geothermal heat, sea waves and the others. The percentage of renewable energy in the energy resources is increasing at an ever increasing rate. While much renewable energy is large scale, it is also suitable for rural and remote areas. The challenges facing today’s renewable energy supply industry are many, especially in the wave energy field which is still underdeveloped. The number of commercialised wave energy devices is very limited and the concepts implemented for harnessing wave energy are very different between the different devices and often struggle to be effective or survive ocean-going conditions. Thus, major research is required to find new and effective methods for harnessing wave energy which are able to supply power to the grid with high conversion rate and good reliability. The proposed Bristol cylinder device, in theory, should be able to harness sea wave energy and to convert it into useful electricity, and this device is studied in detail here. This device is still new in terms of practical application in ocean conditions. It needs power electronics and effective controllers for high-efficiency power extraction and to be successfully integrated into the power grid. When the device was first investigated in the 1970s, power electronics and variable-speed brushless permanent-magnet machinery was simply not developed to the level it is today, hence the revisiting of this device several decades later. A successful Bristol cylinder wave device which can extract renewable energy may well impact on the renewable energy sector. The wave characteristics were studied and simulated using Airy Linear Wave Theory and Stoke’s Second Order Theory. The dynamic characteristics of the Bristol cylinder are investigated when interacting with waves, together with the control necessary to make it a functioning device. A lab scale wave tank suitable to test the Bristol cylinder is designed. A surface magnet permanent magnet synchronous generator (PMSG) design is considered in this research project. This generator configuration shows its suitability in producing high conversion-rate power when working in a low speed environment. The sizing exercise is performed to determine the size of the lab scale PMSG. Analytical analysis and finite element analysis is performed to study the performance of the designed PMSG. A study of the effect of the armature length with the corresponding incident wave is done. Field oriented control (FOC) is applied to control the speed of the generator. FOC is shown to be suitable for stable control of the generator speed. Simulations using MATLAB are utilized and Simulink is used to construct the model and evaluate the potential performance of the control system design. In this thesis, theoretical analyses and simulations of the generator performances are carried out for several generator topologies and sizes. The grid side converter controller technique is also simulated in MATLAB/Simulink and the performance evaluated

H-infinity Variable-Pitch Control for Wind Turbines Based on Takagi-Sugeno Fuzzy Theory

When the wind speed is above the rated value, the output power of the wind turbine should be maintained at the rated value in order to prevent the power generation system from overheating. In addition, the natural wind speed will fluctuate randomly in a large range of values, making the traditional control effect not ideal. This paper presents a novel H-infinity (H∞) pitch control strategy for Wind Turbine Generators (WTGs), which can make the rotor speed and output power constant when the wind speed changes in a large range. In order to shorten response time and reduce overshoot, in the specific solution, the control method combines the H∞ theory and the Takagi-Sugeno (T-S) fuzzy theory. Firstly, the linearized models of several operating points were obtained with the T-S fuzzy theory. Then, a robust controller was designed for each linear sub-system based on the H∞ control theory. Furthermore, the controllers of the sub-systems were superimposed into a global controller for the entire system through the membership function. Finally, modeling and simulation were carried out in MATLAB/SIMULINK. The simulation results show that when the wind speed changes above the rated speed, the rotor speed can be maintained at the rated value, and the output power also can be maintained at the rated value. Compared with the optimal control, the response speed of this method is faster and the overshoot is smaller. It provides a new idea for the pitch angle control of wind turbine

Design of Broadband Dual-Frequency Microstrip Patch Antenna with Modified Sierpinski Fractal Geometry

Fractal antennas have the characteristic of radiating in multiple frequencies through the property of self similarity that fractal shapes possess. By connecting fractal shaped antennas, wideband coverage can be achieved. Microstrip patch antennas with Sierpinski fractal geometry can be tuned, by design, to work exactly at the bands of interest, through judicious choice of the fractal designs and iteration. Therefore, a broadband dual-frequency microstrip patch antenna with modified Sierpienski fractal geometry is designed by using Microwave Office 2002 simulation software. The broadband and multiple frequency characteristics of fractal antennas will be demonstrated. The performance of microstrip patch antenna with the classic and modified Sierpinski fractal geometries will be presente

Online Person Identification based on Multitask Learning

In the digital world, everything is digitized and data are generated consecutively over the times. To deal with this situation, incremental learning plays an important role. One of the important applications that needs an incremental learning is person identification. On the other hand, password and code are no longer the only way to prevent the unauthorized person to access the information and it tends to be forgotten.  Therefore, biometric characteristics system is introduced to solve the problems. However, recognition based on single biometric may not be effective, thus, multitask learning is needed. To solve the problems, incremental learning is applied for person identification based on multitask learning. Considering that the complete data is not possible to be collected at one time, online learning is adopted to update the system accordingly. Linear Discriminant Analysis (LDA) is used to create a feature space while Incremental LDA (ILDA) is adopted to update LDA. Through multitask learning, not only human faces are trained, but fingerprint images are trained in order to improve the performance. The performance of the system is evaluated by using 50 datasets which includes both male and female datasets. Experimental results demonstrate that the learning time of ILDA is faster than LDA. Apart from that, the learning accuracies are evaluated by using K-Nearest Neighbor (KNN) and achieve more than 80% for most of the simulation results. In the future, the system is suggested to be improved by using better sensor for all the biometrics. Other than that, incremental feature extraction is improved to deal with some other online learning problems

A study on Gamification toward Engineering Students’ Engagement in the University Level

Most engineering students may struggle with complicated theories, heavy assignments, lack of motivation and disengagement in the classroom. According to studies, student engagement in the classroom is critical in the learning process. It can increase students' attention and motivate them to practice critical thinking skill. It may also promote positive learning experiences. By this, the learning outcomes in technical understanding and application definitely can be improved. However, how to increase student engagement, particularly for engineering courses in the classroom? Educators are introducing several student-centred teaching methods to replace the traditional direct instruction teaching methods, such as inquiry-based learning, project-based learning, service-based learning and others. This study is to explore the gamification toward the engineering student to increase their engagement in class. A class with 109 students in the second year course of Electrical Engineering Technology (EET) are invited to participate in the study. A gamified learning model with 4 stage games is created in the online platform and participates by the EET students' willingness. This gamified learning model has implemented rank, interactive map and video guide. A survey related to gamification is collected from the EET students who completed the gamified learning model. This survey is mainly to obtain feedback on the gamification experience of the students. The results are generally positive and indicate that gamification can improve engineering student engagement and enjoyment toward the learning process

Online Person Identification based on Multitask Learning

In the digital world, everything is digitized and data are generated consecutively over the times. To deal with this situation, incremental learning plays an important role. One of the important applications that needs an incremental learning is person identification. On the other hand, password and code are no longer the only way to prevent the unauthorized person to access the information and it tends to be forgotten. Therefore, biometric characteristics system is introduced to solve the problems. However, recognition based on single biometric may not be effective, thus, multitask learning is needed. To solve the problems, incremental learning is applied for person identification based on multitask learning. Considering that the complete data is not possible to be collected at one time, online learning is adopted to update the system accordingly. Linear Discriminant Analysis (LDA) is used to create a feature space while Incremental LDA (ILDA) is adopted to update LDA. Through multitask learning, not only human faces are trained, but fingerprint images are trained in order to improve the performance. The performance of the system is evaluated by using 50 datasets which includes both male and female datasets. Experimental results demonstrate that the learning time of ILDA is faster than LDA. Apart from that, the learning accuracies are evaluated by using K-Nearest Neighbor (KNN) and achieve more than 80% for most of the simulation results. In the future, the system is suggested to be improved by using better sensor for all the biometrics. Other than that, incremental feature extraction is improved to deal with some other online learning problems

Person Verification Based on Multimodal Biometric Recognition

Nowadays, person recognition has received significant attention due to broad applications in the security system. However, most person recognition systems are implemented based on unimodal biometrics such as face recognition or voice recognition. Biometric systems that adopted unimodal have limitations, mainly when the data contains outliers and corrupted datasets. Multimodal biometric systems grab researchers’ consideration due to their superiority, such as better security than the unimodal biometric system and outstanding recognition efficiency. Therefore, the multimodal biometric system based on face and fingerprint recognition is developed in this paper. First, the multimodal biometric person recognition system is developed based on Convolutional Neural Network (CNN) and ORB (Oriented FAST and Rotated BRIEF) algorithm. Next, two features are fused by using match score level fusion based on Weighted Sum-Rule. The verification process is matched if the fusion score is greater than the pre-set threshold t. The algorithm is extensively evaluated on UCI Machine Learning Repository Database datasets, including one real dataset with state-of-the-art approaches. The proposed method achieves a promising result in the person recognition system

Optimal constant power control of wind turbine generators based on Takagi-Sugeno fuzzy model

Resource shortages and environmental pollution of the world are becoming more and more serious, wind Turbine generation as a kind of clean energy power generation has attracted much attention. The difficulty of wind power generation lies in the randomness of natural wind energy, which leads to the instability of the output power of Wind Turbine Generators (WTGs). The traditional variable pitch controller cannot realize global precision control of the wind power system. To solve the problem, this paper combines Takagi-Sugeno (T-S) fuzzy model with optimal control theory to design a robust controller. The unique novelty of the controller is that when the wind speed is higher than the rated, no matter how much the wind speed is, even if it is far from the balance point of the system, the controller can make the output power at a constant value, that is, increase the global stability of the wind power system. Specifically, the wind power system was divided into several linear subsystems, and the state feedback controller of each subsystem was designed through optimal control. Then, the subsystem controllers were merged by superposing membership functions, forming a controller of the entire system. Finally, wind turbine generators (WTGs) were modeled and simulated in Matlab/Simulink. The results show that the proposed controller kept the output power of WTGs constant under the global step wind of 13–25 m/s and the stochastic wind of 14–15 m/s. Our design enables the precision control at any wind speed, and improves the stability of power output of WTGs

DATA's initial thoughts on the National Curriculum review A consultation document

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