This thesis aims to investigate an active power factor correction (PFC) circuit and its mathematical model, in order to develop a reliable and efficient simulation platform. By using the PI controller, we can control the inductor current and the output voltage of the boost converter. Finally, we constructed the circuit and analyzed the results to verify that our mathematical model is valid.

The generation of wind energy deliberately becomes a significant part of generated electrical power in developed nations. Factors like fluctuation in natural wind speed and the use of power electronics present issues related power quality in wind turbine application. Following to the fact that there have been remarkable increase of wind energy in the electrical energy production worldwide, the effect on power quality and power system stability caused by wind power is considered significant, and hence the evaluation of this effect is crucial and obligatory. In order to examine and evaluate the characteristics of power quality of grid-integration of wind power in a persistent and authentic manner, several guidelines were introduced and established. One of the widely used guideline to define power quality of wind turbine is IEC standard 61400-21. Moreover, power system operator demands wind turbines to tolerate a certain voltage dip in some countries. The wind turbines concepts such as doubly-fed induction generator wind turbine and the direct driven wind turbine wind turbine with a permanent magnet synchronous generator are considered as the most promising concepts among other wind turbine types since they can operate in wide range of wind speed. The major goal of this PhD work is to examine the power quality character aspects of these wind turbine concepts. The power quality problems were calculated according to that devised by IEC- 61400-21 and then compared afterwards. The research includes the evaluation of the following power quality characteristics: voltage dip response, current harmonics distortion, control of active and reactive power and voltage flicker. Besides the IEC-standard 61400-21, the study also looks into the short-circuit current and fault-ride through with specifications provided by some grid codes, as power system stability is greatly influenced by these aspects. In order to achieve the research's goal, a reliable dynamic model of wind turbine system and control are required. Thus a complete model for both wind turbines systems was developed in PSCAD/EMTDC simulation-program which is the fanatical power system analysis tool, which can achieve a complete simulation of the system dynamic behaviour from the wind turbine. Two controllers are adopted for wind turbine system, converter control and pitch angle control. The converter controlled by a vector control in order to regulate the active and the reactive power whereas the pitch control scheme is put to function to limit the aerodynamic power in high wind speed. The ability of providing adequate state steady and dynamic performances are what wind turbine assures, as examined by simulation results, and via this, problems related to power quality caused by integrating wind turbines to the grid can be studied by wind turbine model.

In this study, two new Multi-Objective Optimization (MOO) techniques are developed. The two new techniques, the Objective-Based Gradient Algorithm (OBGA) and the Principal Component Grid Algorithm (PCGA), were developed with the goals of improving the accuracy and efficiency of the Pareto domain approximation relative to current MOO techniques. Both methods were compared to current MOO techniques using several test problems. It was found that both the OBGA and PCGA systematically produced a more accurate Pareto domain than current MOO techniques used for comparison, for all problems studied. The OBGA requires less computation time than the current MOO methods for relatively simple problems whereas for more complex objective functions, the computation time was larger. On the other hand, the efficiency of the PCGA was higher than the current MOO techniques for all problems tested. The new techniques were also applied to complex chemical engineering problems. The OBGA was applied to an industrial reactor producing ethylene oxide from ethylene. The optimization varied four of the reactor input parameters, and the selectivity, productivity and a safety factor related to the presence of oxygen in the reactor were maximized. From the optimization results, recommendations were made based on the ideal reactor operating conditions, and the control of key reactor parameters. The PCGA was applied to a PI controller model to develop new tuning methods based on the Pareto domain. The developed controller tuning methods were compared to several previously developed controller correlations. It was found that all previously developed controller correlations showed equal or worse performance than that based on the Pareto domain. The tuning methods were applied to a fourth order process and a process with a disturbance, and demonstrated excellent performance.

In this study, two new Multi-Objective Optimization (MOO) techniques are developed. The two new techniques, the Objective-Based Gradient Algorithm (OBGA) and the Principal Component Grid Algorithm (PCGA), were developed with the goals of improving the accuracy and efficiency of the Pareto domain approximation relative to current MOO techniques. Both methods were compared to current MOO techniques using several test problems. It was found that both the OBGA and PCGA systematically produced a more accurate Pareto domain than current MOO techniques used for comparison, for all problems studied. The OBGA requires less computation time than the current MOO methods for relatively simple problems whereas for more complex objective functions, the computation time was larger. On the other hand, the efficiency of the PCGA was higher than the current MOO techniques for all problems tested. The new techniques were also applied to complex chemical engineering problems. The OBGA was applied to an industrial reactor producing ethylene oxide from ethylene. The optimization varied four of the reactor input parameters, and the selectivity, productivity and a safety factor related to the presence of oxygen in the reactor were maximized. From the optimization results, recommendations were made based on the ideal reactor operating conditions, and the control of key reactor parameters. The PCGA was applied to a PI controller model to develop new tuning methods based on the Pareto domain. The developed controller tuning methods were compared to several previously developed controller correlations. It was found that all previously developed controller correlations showed equal or worse performance than that based on the Pareto domain. The tuning methods were applied to a fourth order process and a process with a disturbance, and demonstrated excellent performance.

This paper proposes a sensorless control construction adapting to different speed with DSP2407 as the signal processing control core for rotation compressor. The sensorless control method obtains the rotor position by detecting the back electromotive force signals directly, then obtains better communications and the speed estimation by using digital signal which controlling power switches. Finally ,it carries out speed feedback control and current feedback control to improvt efficiency. Comparing adaptive-step control with traditional-step control and six-step control , the experiment result shows that adaptive-step control has better efficiency and lower vibration.

The thesis aims to the research of active power factor correction (PFC) circuit and develop a DSP-based digital controller. In the thesis, PI controller is the control core for the voltage loop¡Band current loop, and then achieve the function of the power factor correction of boost converter. Finally, we develop a boost converter and connect it to a DSP-based controller to measure the waveforms and verify the power factor correction. Furthermore, the research can be extended to a simulating platform which we can verify the power factor correction by just changing the control law on DSP .

Designing a filament winding machine involves two major components. First component is the designing of the mechanism which delivers appropriate winding pattern as specified by the user (i.e. the winding angle). The second component is the realization of an effective fiber tensioning system for ensuring consistent overall consolidation. Today, there exist a variety of sophisticated methods to achieve both these parameters; however the implementation of these techniques increases the price of a winding machine, making its acquisition very difficult for small scale applications. Sophisticated winding machine includes higher number of degrees of freedom, and high end CNC controlled algorithms. However for creating axi-symetric products with constant diameter, only a 2-axis winding is sufficient. On this note a novel cost effective method for fiber winding angle control and fiber winding tension control is designed, simulated, implemented into a low cost prototype 2-axis filament winding machine, and the scope for its? further improvement has also been discussed. The system designed, uses a cost effective PLC as the centralized controller of the system for implementing both fiber winding control and tension control through PID control. A novel actuator for tension control is designed and implemented. Also a comparison between the conventional PI controller and a Mamdani type Fuzzy inference based control system for controlling the fiber tension is made. In this process a novel technique for making PI controller capable of handling known nonlinearities in the system is introduced. Experimental system responses of the designed prototype are analyzed.

In this study, two new Multi-Objective Optimization (MOO) techniques are developed. The two new techniques, the Objective-Based Gradient Algorithm (OBGA) and the Principal Component Grid Algorithm (PCGA), were developed with the goals of improving the accuracy and efficiency of the Pareto domain approximation relative to current MOO techniques. Both methods were compared to current MOO techniques using several test problems. It was found that both the OBGA and PCGA systematically produced a more accurate Pareto domain than current MOO techniques used for comparison, for all problems studied. The OBGA requires less computation time than the current MOO methods for relatively simple problems whereas for more complex objective functions, the computation time was larger. On the other hand, the efficiency of the PCGA was higher than the current MOO techniques for all problems tested. The new techniques were also applied to complex chemical engineering problems. The OBGA was applied to an industrial reactor producing ethylene oxide from ethylene. The optimization varied four of the reactor input parameters, and the selectivity, productivity and a safety factor related to the presence of oxygen in the reactor were maximized. From the optimization results, recommendations were made based on the ideal reactor operating conditions, and the control of key reactor parameters. The PCGA was applied to a PI controller model to develop new tuning methods based on the Pareto domain. The developed controller tuning methods were compared to several previously developed controller correlations. It was found that all previously developed controller correlations showed equal or worse performance than that based on the Pareto domain. The tuning methods were applied to a fourth order process and a process with a disturbance, and demonstrated excellent performance.

The aim of this thesis is to understand the current implementation, how different hardware and output frequency affects the hydraulic actuators in the current platform and Then an improve the controller should be presented. This needs to be both faster then the current controller and should not use more CPU recurses then necessary. With the understanding of current controller, three new regulators where implemented and tested. One uses a PI regulator and the other two uses an adaptive algorithm to generate the control signal. All where faster than the current one and the PI-implementation uses the lowest amount of CPU recurses, on the other hand this needs to be calibrated for the different hardware and output frequency’s. ThetwoadaptivecontrollersrequiresahigheramountofCPUrecurses, instead it requires less calibration to work.

In this study, two new Multi-Objective Optimization (MOO) techniques are developed. The two new techniques, the Objective-Based Gradient Algorithm (OBGA) and the Principal Component Grid Algorithm (PCGA), were developed with the goals of improving the accuracy and efficiency of the Pareto domain approximation relative to current MOO techniques. Both methods were compared to current MOO techniques using several test problems. It was found that both the OBGA and PCGA systematically produced a more accurate Pareto domain than current MOO techniques used for comparison, for all problems studied. The OBGA requires less computation time than the current MOO methods for relatively simple problems whereas for more complex objective functions, the computation time was larger. On the other hand, the efficiency of the PCGA was higher than the current MOO techniques for all problems tested. The new techniques were also applied to complex chemical engineering problems. The OBGA was applied to an industrial reactor producing ethylene oxide from ethylene. The optimization varied four of the reactor input parameters, and the selectivity, productivity and a safety factor related to the presence of oxygen in the reactor were maximized. From the optimization results, recommendations were made based on the ideal reactor operating conditions, and the control of key reactor parameters. The PCGA was applied to a PI controller model to develop new tuning methods based on the Pareto domain. The developed controller tuning methods were compared to several previously developed controller correlations. It was found that all previously developed controller correlations showed equal or worse performance than that based on the Pareto domain. The tuning methods were applied to a fourth order process and a process with a disturbance, and demonstrated excellent performance.