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This paper presents a data-driven approach to maximize the power of a wind farm by developing a dynamic mode decomposition with input and output for reduced order model (DMDior)-based reduced order model (ROM) for model predictive control (MPC). The main goal of this research is to efficiently model and manage the complex flow field within a wind farm to enhance power production. We leveraged DMDior to transform extensive high-dimensional flow data into an accurate yet simplified ROM, which successfully represents the essential dynamic features of wind flow, including the critical interactions between turbines and their adaptive response to environmental changes. Based on this ROM, the MPC framework was carefully designed. MPC uses this model to dynamically adjust the yaw angle of a wind turbine to optimally match changing wind patterns to maximize power output. The system also incorporates an adaptive Kalman filter designed for the state estimation in MPC applications. This estimation is critical to the effective execution of the MPC in each iteration. This ensures that the MPC operates based on the most up-to-date and accurate representation of the wind farm’s state, improving the overall reliability and efficiency of the control strategy. This approach demonstrates a practical and effective way to increase the power output of a wind farm, with experimental results indicating a power increase of about 4.72%.

The aim of this study is to introduce the maximum torque per total ampere (MTPTA) control strategy for brushless doubly fed induction machine drives taking iron loss into account. In this regard, the condition for realizing the MTPTA is investigated in the presence of iron loss by a theoretical approach. It is shown the proposed strategy is realized when the MTPTA criterion tracks zero as reference signal. As a result, the current is not only shared between the stator windings fairly but also total stator currents are minimized for a given torque. The experimental results confirm the performance of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Electromagnetic (EM) systems are prone to parameter variations and their impact help in predictive analysis. In this letter, proper orthogonal decomposition is proposed as a fast uncertainty quantification method, which can efficiently handle a large number of stochastic parameters, without any restriction on the levels of variations. The performance of the approach is evaluated in a frequency domain stochastic EM problem employing vector finite element method and having permittivity variations. The computational time increases by only 15% even with a fivefold increase in the stochastic variables. [ABSTRACT FROM AUTHOR]

Reduced-order converter models have attracted attention in microgrid applications for being less computationally expensive and simpler to analyze. However, most of the models already developed in the literature only include basic control loops, while control additions such as virtual impedances and the effect of internal control loops are typically neglected. Also, the frequency range in which these models are applicable has not been thoroughly studied. In this paper, a low-order reduced-order model of a droop-controlled converter that includes internal control loops and virtual impedances is derived. The validity of the assumptions used to reduce the model is analyzed and a criterion for deciding the frequency range in which the model can be used is proposed. Differences between transient and quasi-stationary virtual impedances are highlighted by using the proposed modelling method. In particular, it is analytically shown that quasi-stationary virtual impedances have a larger stability region compared to transient virtual impedances. Simulation and experimental results based on a 60 kVA converter are used to validate the main contributions of this work. [ABSTRACT FROM AUTHOR]

Accurate prediction of the mechanical failure of structural components plays an important role in the design of engineering structures. However, the fracture process is challenging to model numerically due to the existence of an elastic singularity at the crack tip. The phase field model (PFM) is one promising approach for modeling brittle fracture using an auxiliary field variable to regularize discontinuities associated with sharp cracks. Unfortunately, it is generally computationally expensive due to the need to solve a fourth-order partial differential equation. In order to reduce the computational burden for parameterized problems, different reduced order modeling approaches such as proper orthogonal decomposition (POD) and discrete empirical interpolation method (DEIM), have been proposed. However, these frameworks are model-based and intrusive approaches and need in-depth efforts in modifying existing complex simulation codes. In this work, a tensor train (TT) approach is proposed in combination with Gaussian process regression (GPR) for modeling and predicting the dynamics of fracture in composite materials. As opposed to POD and DEIM, the TT-GPR approach is a fully data-driven and non-intrusive approach. In this work, we study the fracture of a brittle elastic 2D rectangular slab with a pre-existing crack under Mode I (crack opening) loading conditions. The high dimensional training data for the TT model was generated using PFM with the finite difference method. The predictions by the TT-GPR model are compared with the results from the finite difference method. The TT-GPR is robust enough to predict the catastrophic evolution of the crack growth trend with an accuracy of up to 95% with computational load reduced by two orders of magnitude.

Due to the accelerated growth in the field of power electronics and controller design techniques, the usage of the active magnetic bearing has picked up in industries. Active magnetic bearing helps the rotor to rotate freely without any physical contact. In brief, this paper develops a model of an active magnetic bearing using the finite element method, and its associated reduced order model, followed by the development of a robust control strategy. COMSOL software is used to perform three-dimensional simulation of an active magnetic bearing system. The state space system matrices are extracted from the finite element method, and a linear time-invariant state-space system is generated in MATLAB. Since the original system is large, the reduced order model is constructed. Then, based upon the reduced order model, a sliding mode control is designed to improve the regulation performance of an active magnetic bearing under unmodeled uncertainties. The stability analysis of closed-loop reduced order model with unmodeled uncertainties guarantees the finite time convergence of system states using Lyapunov theory. Further, it is proved that the same control law will also provide satisfactory performance for the original model using the reduced order model as an observer. The numerical simulation is carried out to illustrate the effective performance of the proposed controller for the reduced model as well as the original model with multiple initial conditions. The proposed work offers an alternative approach of using the reduced order model instead of the original model for the controller design of an active magnetic bearing.

This article introduces an equivalent multiple dq-frame model of the modular multilevel converter (MMC) that is derived from a dynamic phasor based small-signal state-space MMC model in the stationary αβ z frame. When compared to a model in the stationary ABC frame, the order of the model in the αβ z frame can be reduced for balanced operation, during which some voltage, and current harmonics are inherently separated in an αβ z representation. The proposed method enables further model order reduction through a generalized transformation towards multiple dq-frames. The development of the model, and the generalized transformation are explained in detail, and the obtained equivalent multiple dq-frame models are verified against a nonlinear averaged model in MATLAB/Simulink. An eigenvalue-based small-signal stability analysis highlights the effect of higher-order harmonics in system-level small-signal stability studies, and two case studies of active harmonic suppression illustrate how the presented model allows an in-depth investigation of the impact of extended control functionalities on the small-signal stability. [ABSTRACT FROM AUTHOR]

This paper analyzes the effect of the operation frequency of an inductive charging system on the heat generation and its impact on the overall system efficiency. This is done considering the change of the material properties due to both frequency and temperature changes. The analysis is achieved using FEA modeling software ANSYS workbench. For faster subsequent optimization, an order-reduced model is implemented in a system-level simulation. Various tests show the good agreement of the results between the reduced order model and those of dynamically coupled-field solvers. Our implementation allows for precise calculation of the losses of the different system parts. Furthermore, an experiment has been conducted to validate the simulation results. [ABSTRACT FROM AUTHOR]

The operation range of today’s micro-electromechanical systems (MEMS) continues to enter the geometric nonlinear regime due to miniaturization and performance requirements. One example is the MEMS Coriolis Vibratory Gyroscope (CVG), where the drive mode operates beyond the structural width, causing a change in resonance frequency upon displacement amplitude. Getting good estimates for this change in frequency has been a major issue in the development of CVGs over the past 20 years. In this paper, we present simulation methods and strategies to extract the nonlinear frequencies of such actuated MEMS from the finite element model of the device. The methods are explained in detail and benchmarked with two MEMS structures. As result, we find that the new methods fulfill the accuracy and performance requirements to aid MEMS designers in developing and optimizing the mechanical structure of their devices. [2018-0136] [ABSTRACT FROM AUTHOR]

In the domain of numerical computation, proper generalized decomposition (PGD), which consist in approximating the solution by a truncated sum of separable functions, is applied more and more in mechanics, and has shown its efficiency in terms of computation time and memory requirements. We propose to evaluate the PGD method to solve three-dimensional (3-D) quasi-static field problems coupling with an external electric circuit. The numerical model, obtained from the PGD formulation, is used to study the 3-D examples. The results are compared with those obtained when solving the full original problem. It is shown in this paper that the computation time rate versus the number of time steps is very small compared with the one in a classical time-stepping method, and can be very efficient to solve problems when small time steps are required. [ABSTRACT FROM AUTHOR]

This work develops a virtual rider that can be used to make a multi-body two-wheeled vehicle follow a specified ground path with a prescribed velocity profile. The virtual rider system is based on a simplified motorcycle model, the sliding plane motorcycle, which is composed of a single rigid body with two ground contact points. This reduced order nonlinear system was presented in an earlier work, together with a dynamic inversion procedure for computing a state-control trajectory corresponding to the desired task. This dynamic inversion procedure is combined in this work with a maneuver regulation controller to yield a nonlinear feedback control strategy. A transverse coordinate system that is consistent with the mechanical symmetries of ground vehicles is constructed and used in the development of the maneuver regulation controller. An inverse optimal control strategy, which also exploits the mechanical symmetries, is developed to shape the dynamic response of the closed loop system. Numerical results with the virtual rider driving a multi-body vehicle through a demanding maneuver with lateral accelerations reaching 1 g are presented. [ABSTRACT FROM PUBLISHER]

Electrical impedance tomography is a highly unstable problem with respect to measurement and modeling errors. This instability is especially severe when absolute imaging is considered. With clinical measurements, accurate knowledge about the body shape is usually not available, and therefore an approximate model domain has to be used in the computational model. It has earlier been shown that large reconstruction artefacts result if the geometry of the model domain is incorrect. In this paper, we adapt the so-called approximation error approach to compensate for the modeling errors caused by inaccurately known body shape. This approach has previously been shown to be applicable to a variety of modeling errors, such as coarse discretization in the numerical approximation of the forward model and domain truncation. We evaluate the approach with a simulated example of thorax imaging, and also with experimental data from a laboratory setting, with absolute imaging considered in both cases. We show that the related modeling errors can be efficiently compensated for by the approximation error approach. We also show that recovery from simultaneous discretization related errors is feasible, allowing the use of computationally efficient reduced order models. [ABSTRACT FROM AUTHOR]

This letter describes a new hybrid technique for enhancing the efficiency of the finite difference time domain method (FDTD) by incorporation with in Yee's mesh a macromodel generated by an order reduction algorithm applied to the finite difference frequency domain (FDFD) wave equation defined on a higher resolution grid. This approach increases the accuracy of the calculations and reduces the overall simulation time [ABSTRACT FROM PUBLISHER]