Full- & Reduced-Order State-Space Modeling of Wind Turbine Systems with Permanent-Magnet Synchronous Generator

Wind energy is an integral part of nowadays energy supply and one of the fastest growing sources of electricity in the world today. Accurate models for wind energy conversion systems (WECSs) are of key interest for the analysis and control design of present and future energy systems. Existing control-oriented WECSs models are subject to unstructured simplifications, which have not been discussed in literature so far. Thus, this technical note presents are thorough derivation of a physical state-space model for permanent magnet synchronous generator WECSs. The physical model considers all dynamic effects that significantly influence the system's power output, including the switching of the power electronics. Alternatively, the model is formulated in the $(a,b,c)$- and $(d,q)$-reference frame. Secondly, a complete control and operation management system for the wind regimes II and III and the transition between the regimes is presented. The control takes practical effects such as input saturation and integral windup into account. Thirdly, by a structured model reduction procedure, two state-space models of WECS with reduced complexity are derived: a non-switching model and a non-switching reduced-order model. The validity of the models is illustrated and compared through a numerical simulation study.Comment: 23 pages, 11 figure

Predictive control in matrix converters. Part I, Principles, topologies and applications

This paper presents an overview of the predictive control principles applied to matrix converters and also the different topologies where this control technique is applied. It will be shown that the predictive strategy is a promising alternative to control matrix converters due to its simplicity and flexibility to include additional aspects in the control being suitable for different industrial applications

Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p

Development of a digital twin for real-time simulation of a combustion engine-based power plant with battery storage and grid coupling

Coordinated control of combustion engine-based power plants with battery storage is the next big thing for optimising renewable energy. Digital twins can enable such sophisticated control but currently are too simplistic for the required insight. This study explores the feasibility of a fully physics-based combustion engine model in real-time co-simulation with an electrical power plant model, including battery storage. A detailed, crank-angle resolved, one-dimensional model of a large-bore stationary engine is reduced to a fast-running model (FRM). This engine digital twin is coupled with a complete power plant control model, developed in Simulink. Real-time functions are tested on a dedicated rapid-prototyping system using a target computer. Measurement data from the corresponding power plant infrastructure provide validation for the digital twin. The model-in-the-loop simulations show real-time results from both the standalone combustion and electric submodels mostly within 5% of measured values. The model coupling for fully predictive simulation was tested on a desktop computer, showing expected functionality and validity within 4% and 8% of the respective measured generator and converter outputs. However, execution time of the FRM needs reducing when moving to final hardware-in-the-loop implementation of a complete power plant model.© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Hysteresis-based design of dynamic reference trajectories to avoid saturation in controlled wind turbines

The main objective of this paper is to design a dynamic reference trajectory based on hysteresis to avoid saturation in controlled wind turbines. Basically, the torque controller and pitch controller set-points are hysteretically manipulated to avoid saturation and drive the system with smooth dynamic changes. Simulation results obtained from a 5MW wind turbine benchmark model show that our proposed strategy has a clear added value with respect to the baseline controller (a well-known and accepted industrial wind turbine controller). Moreover, the proposed strategy has been tested in healthy conditions but also in the presence of a realistic fault where the baseline controller caused saturation to nally conduct to instability.Peer ReviewedPostprint (author's final draft

COMMON MODE VOLTAGE ELIMINATION IN THREE-PHASE FOUR-LEG INVERTERS UTILIZING PULSE DENSITY MODULATION

Common mode (CM) electromagnetic interference (EMI) is a phenomenon that negatively affects power electronics to include voltage source inverters. Typically, CM EMI reduction is achieved through passive measures such as CM chokes and passive filters. This thesis research explores removing the need for these passive devices in three-phase, four-leg grid-following inverters by eliminating CM EMI using pulse density modulation (PDM) in conjunction with model predictive control (MPC) and delta modulation. A physics-based model of the equipment under test (EUT), utilizing state-space modeling, was analyzed using computer simulations and a laboratory prototype, utilizing SiC switching devices, was designed to validate the model. The physics-based model of the proposed control system was converted to Verilog, a hardware description language (HDL) utilizing MATLAB HDL coder in order to control the laboratory prototype via a field-programmable gate array (FPGA). Simulated and experimental results demonstrate that both the unbalanced load requirements in MIL-STD-1399 and the conducted emission limits in MIL-STD-461G are met with the proposed controller, while the grid-following converter supplies a desired current to the load.Office of Naval Research, Arlington VA 22203-1995Outstanding ThesisLieutenant, United States NavyApproved for public release. Distribution is unlimited

Deep neural learning based distributed predictive control for offshore wind farm using high fidelity LES data

The paper explores the deep neural learning (DNL) based predictive control approach for offshore wind farm using high fidelity large eddy simulations (LES) data. The DNL architecture is defined by combining the Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN) for feature extraction and prediction of the offshore wind farm. This hybrid CNN-LSTM model is developed based on the dynamic models of the wind farm and wind turbines as well as higher-fidelity LES data. Then, distributed and decentralized model predictive control (MPC) methods are developed based on the hybrid model for maximizing the wind farm power generation and minimizing the usage of the control commands. Extensive simulations based on a two-turbine and a nine-turbine wind farm cases demonstrate the high prediction accuracy (97% or more) of the trained CNN-LSTM models. They also show that the distributed MPC can achieve up to 38% increase in power generation at farm scale than the decentralized MPC. The computational time of the distributed MPC is around 0.7s at each time step, which is sufficiently fast as a real-time control solution to wind farm operations

On-line Condition Monitoring, Fault Detection and Diagnosis in Electrical Machines and Power Electronic Converters

The objective of this PhD research is to develop robust, and non-intrusive condition monitoring methods for induction motors fed by closed-loop inverters. The flexible energy forms synthesized by these connected power electronic converters greatly enhance the performance and expand the operating region of induction motors. They also significantly alter the fault behavior of these electric machines and complicate the fault detection and protection. The current state of the art in condition monitoring of power-converter-fed electric machines is underdeveloped as compared to the maturing condition monitoring techniques for grid-connected electric machines. This dissertation first investigates the stator turn-to-turn fault modelling for induction motors (IM) fed by a grid directly. A novel and more meaningful model of the motor itself was developed and a comprehensive study of the closed-loop inverter drives was conducted. A direct torque control (DTC) method was selected for controlling IM’s electromagnetic torque and stator flux-linkage amplitude in industrial applications. Additionally, a new driver based on DTC rules, predictive control theory and fuzzy logic inference system for the IM was developed. This novel controller improves the performance of the torque control on the IM as it reduces most of the disadvantages of the classical and predictive DTC drivers. An analytical investigation of the impacts of the stator inter-turn short-circuit of the machine in the controller and its reaction was performed. This research sets a based knowledge and clear foundations of the events happening inside the IM and internally in the DTC when the machine is damaged by a turn fault in the stator. This dissertation also develops a technique for the health monitoring of the induction machine under stator turn failure. The developed technique was based on the monitoring of the off-diagonal term of the sequence component impedance matrix. Its advantages are that it is independent of the IM parameters, it is immune to the sensors’ errors, it requires a small learning stage, compared with NN, and it is not intrusive, robust and online. The research developed in this dissertation represents a significant advance that can be utilized in fault detection and condition monitoring in industrial applications, transportation electrification as well as the utilization of renewable energy microgrids. To conclude, this PhD research focuses on the development of condition monitoring techniques, modelling, and insightful analyses of a specific type of electric machine system. The fundamental ideas behind the proposed condition monitoring technique, model and analysis are quite universal and appeals to a much wider variety of electric machines connected to power electronic converters or drivers. To sum up, this PhD research has a broad beneficial impact on a wide spectrum of power-converter-fed electric machines and is thus of practical importance

Empowering wave energy with control technology: Possibilities and pitfalls

With an increasing focus on climate action and energy security, an appropriate mix of renewable energy technologies is imperative. Despite having considerable global potential, wave energy has still not reached a state of maturity or economic competitiveness to have made an impact. Challenges include the high capital and operational costs associated with deployment in the harsh ocean environment, so it is imperative that the full energy harnessing capacity of wave energy devices, and arrays of devices in farms, is realised. To this end, control technology has an important role to play in maximising power capture, while ensuring that physical system constraints are respected, and control actions do not adversely affect device lifetime. Within the gamut of control technology, a variety of tools can be brought to bear on the wave energy control problem, including various control strategies (optimal, robust, nonlinear, etc.), data-based model identification, estimation, and forecasting. However, the wave energy problem displays a number of unique features which challenge the traditional application of these techniques, while also presenting a number of control ‘paradoxes’. This review articulates the important control-related characteristics of the wave energy control problem, provides a survey of currently applied control and control-related techniques, and gives some perspectives on the outstanding challenges and future possibilities. The emerging area of control co-design, which is especially relevant to the relatively immature area of wave energy system design, is also covered

Thermal Stress Based Model Predictive Control of Power Electronic Converters in Electric Drives Applications

Power electronics is used increasingly in a wide range of application fields such as variable speed drives, electric vehicles and renewable energy systems. It has become a crucial component for the further development of emerging application fields such as lighting, more-electric aircrafts and medical systems. The reliable operation over the designed lifetime is essential for any power electronic system, particularly because the reliability of power electronics is becoming a prerequisite for the system safety in several key areas like energy, medicine and transportation. The thermal stress of power electronic components is one of the most important causes of their failure. Proper thermal management plays an important role for more reliable and cost effective energy conversion. As one of the most vulnerable and expensive components, power semiconductors, are the focus of this thesis. Active thermal control is a possibility to control the junction temperatures of power semiconductors in order to reduce the thermal stress. For this purpose the finite control-set model predictive control (FCS-MPC) is chosen. In FCS-MPC the switching vector is selected using a multi-parameter optimization that can include non-linear electric and thermal stress related models. This switching vector is directly applied to the physical system. This allows the direct control of the switching-state and the current through each semiconductor at each time instant. For cost-effective control of the thermal stress a measure for the degradation of the semiconductor's lifetime is necessary. Existing lifetime models in literature are based on the thermal cycling amplitudes and maximum values of recorded junction temperature profiles. For online estimation of the degradation, a method to detect the junction temperatures of the semiconductors during operation is designed and validated. An existing and proven lifetime model is adapted for online estimation of the thermal stress. An algorithm for the FCS-MPC is written that utilizes this model to drive the inverter with reduced stress and equalize the degradation of the semiconductors in a power module. The algorithm is demonstrated in simulation and validated in experiment. A technique to find the optimal trade-off between reduction of the thermal stress and allowing additional losses in the system is given. The effect of rotor flux variation of the machine on the junction temperatures of the driving inverter is investigated. It can be used as another parameter to control the junction temperature. This allows increasing the maximal thermal cycling amplitude that can be compensated by an active thermal controller. A suitable controller is proposed and validated in experiment. The integration of this technique into the FCS-MPC is presented