Controller design for variable-speed permanent magnet wind turbine generators interfaced with Z-source inverter

Increased awareness of environmental concerns has caused greater interest in developing power sources based on renewable technologies, such as wind. Due to the intermittent nature of the wind speed, output voltage and frequency of the direct driven permanent magnet synchronous generators (PMSG) are normally unsteady. Recently proposed Z-source inverter has been considered as a potential solution for grid interfacing wind power generators, thanks to buck-boost function that the single stage Z-source inverter can offer. Two control methodologies, namely unified controller for isolated operation and a multi-loop controller for grid interfaced operation are investigated in this paper. Theoretical analysis of these two control schemes is presented and experimental results to verify the effectiveness of the control method are also included.

A Simple Multiple Pulsewidth Modulated AC Choppe

A simple multiple pulsewidth modulated (MPWM) ac chopper using power transistors for 3-ý power control is discussed. 120ý chopping period is used for main transistors so that the circuit can accommodate resistive and lagging or leading power factor loads. Only 1-ý sensing is used for 3-ý control. An alternate economical power and control schemes for 3-ý MPWM ac choppers suitable only for resistive loads is also suggested. The experimental results for 12 choppings per cycle are given.

A Simple Multiple pulsewidth Modulated AC Multiple Chopper

A simple multiple pulsewidth modulated (MPWM) ac chopper using power transistors for 3-¿ power control is discussed. 120° chopping period is used for main transistors so that the circuit can accommodate resistive and lagging or leading power factor loads. Only 1-¿ sensing is used for 3-¿ control. An alternate economical power and control schemes for 3-¿ MPWM ac choppers suitable only for resistive loads is also suggested. The experimental results for 12 choppings per cycle are given.

Molecular Dynamics Simulations of Homogeneous Nucleation

Nucleation is the first step in a phase transition where small nuclei of the new phase start appearing in the metastable old phase, such as the appearance of small liquid clusters in a supersaturated vapor. Nucleation is important in various industrial and natural processes, including atmospheric new particle formation: between 20 % to 80 % of atmospheric particle concentration is due to nucleation. These atmospheric aerosol particles have a significant effect both on climate and human health. Different simulation methods are often applied when studying things that are difficult or even impossible to measure, or when trying to distinguish between the merits of various theoretical approaches. Such simulation methods include, among others, molecular dynamics and Monte Carlo simulations. In this work molecular dynamics simulations of the homogeneous nucleation of Lennard-Jones argon have been performed. Homogeneous means that the nucleation does not occur on a pre-existing surface. The simulations include runs where the starting configuration is a supersaturated vapor and the nucleation event is observed during the simulation (direct simulations), as well as simulations of a cluster in equilibrium with a surrounding vapor (indirect simulations). The latter type are a necessity when the conditions prevent the occurrence of a nucleation event in a reasonable timeframe in the direct simulations. The effect of various temperature control schemes on the nucleation rate (the rate of appearance of clusters that are equally able to grow to macroscopic sizes and to evaporate) was studied and found to be relatively small. The method to extract the nucleation rate was also found to be of minor importance. The cluster sizes from direct and indirect simulations were used in conjunction with the nucleation theorem to calculate formation free energies for the clusters in the indirect simulations. The results agreed with density functional theory, but were higher than values from Monte Carlo simulations. The formation energies were also used to calculate surface tension for the clusters. The sizes of the clusters in the direct and indirect simulations were compared, showing that the direct simulation clusters have more atoms between the liquid-like core of the cluster and the surrounding vapor. Finally, the performance of various nucleation theories in predicting simulated nucleation rates was investigated, and the results among other things highlighted once again the inadequacy of the classical nucleation theory that is commonly employed in nucleation studies.

Adaptive optimal tuning of a general class of stable LTI systems with restricted inputs

The problem addressed is one of model reference adaptive control (MRAC) of asymptotically stable plants of unknown order with zeros located anywhere in the s-plane except at the origin. The reference model is also asymptotically stable and lacking zero(s) at s = 0. The control law is to be specified only in terms of the inputs to and outputs of the plant and the reference model. For inputs from a class of functions that approach a non-zero constant, the problem is formulated in an optimal control framework. By successive refinements of the sub-optimal laws proposed here, two schemes are finally design-ed. These schemes are characterized by boundedness, convergence and optimality. Simplicity and total time-domain implementation are the additional striking features. Simulations to demonstrate the efficacy of the control schemes are presented.

Controle por modo deslizante para sistemas não-lineares com atraso.

Nesta Dissertação são propostos dois esquemas de controle para sistemas não-lineares com atraso. No primeiro, o objetivo é controlar uma classe de sistemas incertos multivariáveis, de grau relativo unitário, com perturbações não-lineares descasadas dependentes do estado, e com atraso incerto e variante no tempo em relação ao estado. No segundo, deseja-se controlar uma classe de sistemas monovariáveis, com parâmetros conhecidos, grau relativo arbitrário, atraso arbitrário conhecido e constante na saída. Admitindo-se que o atraso na entrada pode ser deslocado para a saída, então, o segundo esquema de controle pode ser aplicado a sistemas com atraso na entrada. Os controladores desenvolvidos são baseados no controle por modo deslizante e realimentação de saída, com função de modulação para a amplitude do sinal de controle. Além disso, observadores estimam as variáveis de estado não-medidas. Em ambos os esquemas de controle propostos, garante-se propriedades de estabilidade globais do sistema em malha fechada. Simulações ilustram a eficácia dos controladores desenvolvidos.

Controle de um sistema de eletroestimulação funcional.

Esta Dissertação irá apresentar a utilização de técnicas de controle nãolinear, tais como o controle adaptativo e robusto, de modo a controlar um sistema de Eletroestimulação Funcional desenvolvido pelo laboratório de Engenharia Biomédica da COPPE/UFRJ. Basicamente um Eletroestimulador Funcional (Functional Electrical Stimulation FES) se baseia na estimulação dos nervos motores via eletrodos cutâneos de modo a movimentar (contrair ou distender) os músculos, visando o fortalecimento muscular, a ativação de vias nervosas (reinervação), manutenção da amplitude de movimento, controle de espasticidade muscular, retardo de atrofias e manutenção de tonicidade muscular. O sistema utilizado tem por objetivo movimentar os membros superiores através do estímulo elétrico de modo a atingir ângulos-alvo pré-determinados para a articulação do cotovelo. Devido ao fato de não termos conhecimento pleno do funcionamento neuro-motor humano e do mesmo ser variante no tempo, não-linear, com parâmetros incertos, sujeito a perturbações e completamente diferente para cada indivíduo, se faz necessário o uso de técnicas de controle avançadas na tentativa de se estabilizar e controlar esse tipo de sistema. O objetivo principal é verificar experimentalmente a eficácia dessas técnicas de controle não-linear e adaptativo em comparação às técnicas clássicas, de modo a alcançar um controle mais rápido, robusto e que tenha um desempenho satisfatório. Em face disso, espera-se ampliar o campo de utilização de técnicas de controle adaptativo e robusto, além de outras técnicas de sistemas inteligentes, tais como os algoritmos genéticos, provando que sua aplicação pode ser efetiva no campo de sistemas biológicos e biomédicos, auxiliando assim na melhoria do tratamento de pacientes envolvidos nas pesquisas desenvolvidas no Laboratório de Engenharia Biomédica da COPPE/UFRJ.

互联网上机器人的遥控理论与方法综述

给出了系统的研究模型,指出系统控制和设计必须考虑的3个关键问题:稳定性、透明性和时延处理.阐述了4个主要的稳定性分析方法:Lyapunov稳定性、输入输出稳定性、无源稳定性和基于事件的稳定性,总结了这些方法的优势和局限性.接着,给出了几种主要的控制策略,指出了现有控制方法的优缺点.最后,提出了进一步的主要研究方向.

机器人机械手的离散时间学习控制

本文为机器人机械手提出了一种基于离散时间的重复学习控制法，这种学习控制法利用机器人动力学模型的部分知识，从它的特性和实用观点看，这种控制法比现有的其它学习控制法更有吸引力．本文还给出了学习控制法的收敛性证明和计算机仿真结果。

基因芯片PCR温度模糊自整定PID控制算法的研究

在核酸扩增反应仪中,基因芯片核酸扩增反应过程要求实现温度高精度快速跟踪控制,常规温控方案和算法难以实现。将模糊推理系统与常规PID控制方式相结合,采用模糊自整定PID控制算法实现了温度快速跟踪控制。实验结果表明:模糊自整定PID控制算法比常规PID算法具有更强的鲁棒性,能够克服控制对象热惯性参数时变性的影响,降低了输出温度最大超调量,提高了稳态精度。

Shaping Inputs to Reduce Vibration in Flexible Space Structures

Future NASA plans to launch large space strucutres solicit the need for effective vibration control schemes which can solve the unique problems associated with unwanted residual vibration in flexible spacecraft. In this work, a unique method of input command shaping called impulse shaping is examined. A theoretical background is presented along with some insight into the methdos of calculating multiple mode sequences. The Middeck Active Control Experiment (MACE) is then described as the testbed for hardware experiments. These results are shown and some of the difficulties of dealing with nonlinearities are discussed. The paper is concluded with some conclusions about calculating and implementing impulse shaping in complex nonlinear systems.

Polarization effects in fiber-optic communication systems

In this thesis we perform a detailed analysis of the state of polarization (SOP) of light scattering process using a concatenation of ber-coil based polarization controllers (PCs). We propose a polarization-mode dispersion (PMD) emulator, built through the concatenation of bercoil based PCs and polarization-maintaining bers (PMFs), capable of generate accurate rst- and second-order PMD statistics. We analyze the co-propagation of two optical waves inside a highbirefringence ber. The evolution along the ber of the relative SOP between the two signals is modeled by the de nition of the degree of co-polarization parameter. We validate the model for the degree of co-polarization experimentally, exploring the polarization dependence of the four-wave mixing e ect into a ber with high birefringence. We also study the interaction between signal and noise mediated by Kerr e ect in optical bers. A model accurately describing ampli ed spontaneous emission noise in systems with distributed Raman gain is derived. We show that the noise statistics depends on the propagation distance and on the signal power, and that for distances longer than 120 km and signal powers higher than 6 mW it deviates signi catively from the Gaussian distribution. We explore the all-optical polarization control process based on the stimulated Raman scattering e ect. Mapping parameters like the degree of polarization (DOP), we show that the preferred ampli cation of one particular polarization component of the signal allows a polarization pulling over a wavelength range of 60 nm. The e ciency of the process is higher close to the maximum Raman gain wavelength, where the DOP is roughly constant for a wavelength range of 15 nm. Finally, we study the polarization control in quantum key distribution (QKD) systems with polarization encoding. A model for the quantum bit error rate estimation in QKD systems with time-division multiplexing and wavelength-division multiplexing based polarization control schemes is derived.

Optimal controllers with complex order derivatives

This paper studies the optimization of complex-order algorithms for the discrete-time control of linear and nonlinear systems. The fundamentals of fractional systems and genetic algorithms are introduced. Based on these concepts, complexorder control schemes and their implementation are evaluated in the perspective of evolutionary optimization. The results demonstrate not only that complex-order derivatives constitute a valuable alternative for deriving control algorithms, but also the feasibility of the adopted optimization strategy.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Measuring and comparing constraints to improved biosecurity amongst GB farmers, veterinarians and the auxiliary industries

Constraints to the introduction of enhanced biosecurity systems are rarely considered in sufficient detail when population medicine specialists initiate new control schemes. The main objective of our research was to investigate and compare the different attitudes constraining improvement in biosecurity for cattle and sheep farmers, practising veterinary surgeons and the auxiliary industries in Great Britain (GB). This study was carried out utilizing farmer focus groups, a questionnaire survey of veterinary practitioners and a telephone survey of auxiliary industry representatives. It appears that farmers and veterinarians have their own relatively clear definitions for biosecurity in relation to some major diseases threatening GB agriculture. Overall, farmers believe that other stakeholders, such as the government, should make a greater contribution towards biosecurity within GB. Conversely, veterinary practitioners saw their clients' ability or willingness to invest in biosecurity measures as a major constraint. Veterinary practitioners also felt that there was need for additional proof of efficacy and/or the potential economic benefits of proposed farm biosecurity practices better demonstrated. Auxiliary industries, in general, were not certain of their role in biosecurity although study participants highlighted zoonoses as part of the issue and offered that most of the constraints operated at farm level. (C) 2008 Elsevier B.V. All rights reserved.