Mitigation of Harmonics and Inter-Harmonics with LVRT and HVRT Enhancement in Grid-Connected Wind Energy Systems Using Genetic Algorithm-Optimized PWM and Fuzzy Adaptive PID Control

© 2021 Author(s). This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1063/5.0015579The growing installed wind capacity over the last decade has led many energy regulators to define specific grid codes for wind energy generation systems connecting to the electricity grid. These requirements impose strict laws regarding the Low Voltage Ride Though (LVRT) and High Voltage Ride Though (HVRT) capabilities of wind turbines during voltage disturbances. The main aim of this paper is to propose LVRT and HVRT strategies that allow wind systems to remain connected during severe grid voltage disturbances. Power quality issues associated with harmonics and inter-harmonics are also discussed and a control scheme for the grid-side converter is proposed to make the Wind Energy Conversion System insensitive to external disturbances and parametric variations. The Selective Harmonic Elimination Pulse Width Modulation technique based on Genetic Algorithm optimization is employed to overcome over-modulation problems, reduce the amplitudes of harmonics, and thus reduce the Total Harmonic Distortion in the current and voltage waveforms. Furthermore, to compensate for the fluctuations of the wind speed due to turbulence at the blades of the turbine, a fuzzy Proportional-Integral-Derivative controller with adaptive gains is proposed to control the converter on the generator side.Peer reviewedFinal Accepted Versio

Advanced Mathematics and Computational Applications in Control Systems Engineering

Control system engineering is a multidisciplinary discipline that applies automatic control theory to design systems with desired behaviors in control environments. Automatic control theory has played a vital role in the advancement of engineering and science. It has become an essential and integral part of modern industrial and manufacturing processes. Today, the requirements for control precision have increased, and real systems have become more complex. In control engineering and all other engineering disciplines, the impact of advanced mathematical and computational methods is rapidly increasing. Advanced mathematical methods are needed because real-world control systems need to comply with several conditions related to product quality and safety constraints that have to be taken into account in the problem formulation. Conversely, the increment in mathematical complexity has an impact on the computational aspects related to numerical simulation and practical implementation of the algorithms, where a balance must also be maintained between implementation costs and the performance of the control system. This book is a comprehensive set of articles reflecting recent advances in developing and applying advanced mathematics and computational applications in control system engineering

Real Time Estimation, Quantization, And Remote Control Of Permanent Magnet Dc Motors

Establishing real-time models for electric motors is of importance for capturing authentic dynamic behavior of the motors to improve control performance, enhance robustness, and support diagnosis. Quantized sensors are less expensive and remote controlled motors mandate signal quantization. Such limitations on observations introduce challenging issues in motor parameter estimation. This dissertation develops estimators for model parameters of permanent magnet direct current motors (PMDC) using quantized speed measurements. A typical linearized model structure of PMDC motors is used as a benchmark platform to demonstrate the technology, its key properties, and benefits. Convergence properties are established. Simulations and experimental studies are performed to illustrate potential applications of the technology. Remotely-controlled Permanent Magnet DC (PMDC) motors must transmit speed measurements and receive control commands via communication channels. Sampling, quantization, data transfer, and signal reconstruction are mandatory in such networked systems, and introduce additional dynamic subsystems that substantially affect feedback stability and performance. The intimate interaction among sampling periods, signal estimation step sizes, and feedback dynamics entails careful design considerations in such systems. This dissertation investigates the impact of these factors on PMDC motor performance, by rigorous analysis, simulation case studies, and design trade-off examination. The findings of this dissertation will be of importance in providing design guidelines for networked mobile systems, such as autonomous vehicles, mobile sensors, unmanned aerial vehicles which often use electric motors as main engines

Vibration, Control and Stability of Dynamical Systems

From Preface: This is the fourteenth time when the conference “Dynamical Systems: Theory and Applications” gathers a numerous group of outstanding scientists and engineers, who deal with widely understood problems of theoretical and applied dynamics. Organization of the conference would not have been possible without a great effort of the staff of the Department of Automation, Biomechanics and Mechatronics. The patronage over the conference has been taken by the Committee of Mechanics of the Polish Academy of Sciences and Ministry of Science and Higher Education of Poland. It is a great pleasure that our invitation has been accepted by recording in the history of our conference number of people, including good colleagues and friends as well as a large group of researchers and scientists, who decided to participate in the conference for the first time. With proud and satisfaction we welcomed over 180 persons from 31 countries all over the world. They decided to share the results of their research and many years experiences in a discipline of dynamical systems by submitting many very interesting papers. This year, the DSTA Conference Proceedings were split into three volumes entitled “Dynamical Systems” with respective subtitles: Vibration, Control and Stability of Dynamical Systems; Mathematical and Numerical Aspects of Dynamical System Analysis and Engineering Dynamics and Life Sciences. Additionally, there will be also published two volumes of Springer Proceedings in Mathematics and Statistics entitled “Dynamical Systems in Theoretical Perspective” and “Dynamical Systems in Applications”

Large Grid-Connected Wind Turbines

This book covers the technological progress and developments of a large-scale wind energy conversion system along with its future trends, with each chapter constituting a contribution by a different leader in the wind energy arena. Recent developments in wind energy conversion systems, system optimization, stability augmentation, power smoothing, and many other fascinating topics are included in this book. Chapters are supported through modeling, control, and simulation analysis. This book contains both technical and review articles

Improved Wind Turbine Control Strategies for Maximizing Power Output and Minimizing Power Flicker

For reducing the cost of energy (COE) for wind power, controls techniques are important for enhancing energy yield, reducing structural load and improving power quality. This thesis presents the control strategies studies for wind turbine both from the perspectives of both maximizing power output and reducing power flicker and structural load, First, a self-optimizing robust control scheme is developed with the objective of maximizing the power output of a variable speed wind turbine with doubly-fed induction generator (DFIG) operated in Region 2. Wind power generation can be divided into two stages: conversion from aerodynamic power to rotor (mechanical) power and conversion from rotor power to the electrical (grid) power. In this work, the maximization of power generation is achieved by a two-loop control structure in which the power control for each stage has intrinsic synergy. The outer loop is an Extremum Seeking Control (ESC) based generator torque regulation via the rotor power feedback. The ESC can search for the optimal torque constant to maximize the rotor power without wind measurement or accurate knowledge of power map. The inner loop is a vector-control based scheme that can both regulate the generator torque requested by the ESC and also maximize the conversion from the rotor power to grid power. In particular, an ∞ controller is synthesized for maximizing, with performance specifications defined based upon the spectrum of the rotor power obtained by the ESC. Also, the controller is designed to be robust against the variations of some generator parameters. The proposed control strategy is validated via simulation study based on the synergy of several software packages including the TurbSim and FAST developed by NREL, Simulink and SimPowerSystems. Then, a bumpless transfer scheme is proposed for inter-region controller switching scheme in order to reduce the power fluctuation and structural load under fluctuating wind conditions. This study considers the division of Region 2, Region 2.5 and Region 3 in the neighborhood of the rated wind speed. When wind, varies around the rated wind speed, the switching of control can lead to significant fluctuation in power and voltage supply, as well as structural loading. To smooth the switch and improve the tracking, two different bumpless transfer methods, Conditioning and Linear Quadratic techniques, are employed for different inter-region switching situations. The conditioning bumpless transfer approach adopted for switching between Region 2 maximum power capture controls to Region 2.5 rotor speed regulation via generator torque. For the switch between Region 2.5 and Region 3, the generator torque windup at rated value and pitch controller become online to limit the load of wind turbine. LQ technique is posed to reduce the discontinuity at the switch between torque controller and pitch controller by using an extra compensator. The flicker emission of the turbine during the switching is calculated to evaluate power fluctuation. The simulation results demonstrated the effectiveness of the proposed scheme of inter-region switching, with significant reduction of power flicker as well as the damage equivalent load

Engineering Education and Research Using MATLAB

MATLAB is a software package used primarily in the field of engineering for signal processing, numerical data analysis, modeling, programming, simulation, and computer graphic visualization. In the last few years, it has become widely accepted as an efficient tool, and, therefore, its use has significantly increased in scientific communities and academic institutions. This book consists of 20 chapters presenting research works using MATLAB tools. Chapters include techniques for programming and developing Graphical User Interfaces (GUIs), dynamic systems, electric machines, signal and image processing, power electronics, mixed signal circuits, genetic programming, digital watermarking, control systems, time-series regression modeling, and artificial neural networks

Nonlinear Model Predictive Control for Industrial Manufacturing Processes with Reconfigurable Machine Tools

Manufacturing companies are faced with challenges to respond to volatile market demands quickly and flexibly while maintaining a cost-effective level of production. Instead of flexible working times, we adopt Reconfigurable Machine Tools (RMTs) to compensate for unpredictable events in case of bottleneck. To include these tools effectively on the operational layer, we propose a complementing feedback approach using model predictive control (MPC) together with genetic algorithm and branch and bound to achieve a better compliance with logistics objectives and a sustainable demand oriented capacity allocation. Further, the system stability is guaranteed by a trajectory-based unconstrained MPC scheme associated with the principle of flexible Lyapunov functions. The effectiveness and plug-and-play availability of the proposed method is demonstrated via a four-workstation job-shop system, which shows that the work in process can be practically asymptotically stabilized by usage of RMTs

A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

The objective of this dissertation is to develop data-driven frequency-domain methods for designing robust controllers through the use of convex optimization algorithms. Many of today's industrial processes are becoming more complex, and modeling accurate physical models for these plants using first principles may be impossible. With the increased developments in the computing world, large amounts of measured data can be easily collected and stored for processing purposes. Data can also be collected and used in an on-line fashion. Thus it would be very sensible to make full use of this data for controller design, performance evaluation, and stability analysis. The design methods imposed in this work ensure that the dynamics of a system are captured in an experiment and avoids the problem of unmodeled dynamics associated with parametric models. The devised methods consider robust designs for both linear-time-invariant (LTI) single-input-single-output (SISO) systems and certain classes of nonlinear systems. In this dissertation, a data-driven approach using the frequency response function of a system is proposed for designing robust controllers with H∞ performance. Necessary and sufficient conditions are derived for obtaining H∞ performance while guaranteeing the closed-loop stability of a system. A convex optimization algorithm is implemented to obtain the controller parameters which ensure system robustness; the controller is robust with respect to the frequency-dependent uncertainties of the frequency response function. For a certain class of nonlinearities, the proposed method can be used to obtain a best-linear-approximation with an associated frequency dependent uncertainty to guarantee the stability and performance for the underlying linear system that is subject to nonlinear distortions. The concepts behind these design methods are then used to devise necessary and sufficient conditions for ensuring the closed-loop stability of systems with sector-bounded nonlinearities. The conditions are simple convex feasibility constraints which can be used to stabilize systems with multi-model uncertainty. Additionally, a method is proposed for obtaining H∞ performance for an approximate model (i.e., describing function) of a sector-bounded nonlinearity. This work also proposes several data-driven methods for designing robust fixed-structure controllers with H∞ performance. One method considers the solution to a non-convex problem, while another method convexifies the problem and implements an iterative algorithm to obtain the local solution (which can also consider H2 performance). The effectiveness of the proposed method(s) is illustrated by considering several case studies that require robust controllers for achieving the desired performance. The main applicative work in this dissertation is with respect to a power converter control system at the European Organization for Nuclear Research (CERN) (which is used to control the current in a magnet to produce the desired field in controlling particle trajectories in accelerators). The proposed design methods are implemented in order to satisfy the challenging performance specifications set by the application while guaranteeing the system stability and robustness using data-driven design strategies

Advanced Control Strategies for Automatic Drug Delivery to Regulate Anesthesia during Surgery

Ph.DDOCTOR OF PHILOSOPH