16,398 research outputs found
Linear Model Predictive Control of Induction Machine
This article presents new control algorithm for induction machine based on linear model predictive control (MPC). Controller works in similar manners as field oriented control (FOC), but control is performed in stator coordinates. This reduces computational demands as Park’s transformation is absent and induction machine mathematical model in stator coordinates contains less nonlinear elements. Another aim of proposed controller was to achieve fast torque response
Self-Commissioning of Inverter Nonlinear Effects in AC Drives
The paper presents a novel technique for an accurate identification of the inverter nonlinear effects, such as the dead-time and on-state voltage drops. The proposed technique is very simple and it is based only on a current control scheme. If the inverter load is an AC motor, the inverter effects can be identified at drive startup using as measured quantities the motor currents and the inverter DC link voltage. The identified inverter error is stored in a Look-Up Table (LUT) that can be subsequently used by the vector control algorithm. The proposed method has been tested on a 1 kVA inverter prototype and the obtained results demonstrate the feasibility of the proposed solutio
Data-driven discovery of the heat equation in an induction machine via sparse regression
Discovery of natural laws through input-output data analysis has been of considerable interest during the past decade. Various approach among which the increasingly growing body of sparsity-based algorithms have been recently proposed for the purpose of free-form system identification. There has however been limited discussion on the performance of these approaches when applied on experimental datasets. The aim of this paper is to study the capability of this technique for identifying the heat equation as the natural law of heat distribution from experimental data, obtained using a Totally-Enclosed-Fan-Cooled (TEFC) induction machine, with and without active cooling. The results confirm the usefulness of the algorithm as a method to identify the underlying natural law in a physical system in the form of a Partial Differential Equation (PDE)
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 -
and -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
Model Prediction-Based Approach to Fault Tolerant Control with Applications
Abstract— Fault-tolerant control (FTC) is an integral component in industrial processes as it enables the system to continue robust operation under some conditions. In this paper, an FTC scheme is proposed for interconnected systems within an integrated design framework to yield a timely monitoring and detection of fault and reconfiguring the controller according to those faults. The unscented Kalman filter (UKF)-based fault detection and diagnosis system is initially run on the main plant and parameter estimation is being done for the local faults. This critical information\ud
is shared through information fusion to the main system where the whole system is being decentralized using the overlapping decomposition technique. Using this parameter estimates of decentralized subsystems, a model predictive control (MPC) adjusts its parameters according to the\ud
fault scenarios thereby striving to maintain the stability of the system. Experimental results on interconnected continuous time stirred tank reactors (CSTR) with recycle and quadruple tank system indicate that the proposed method is capable to correctly identify various faults, and then controlling the system under some conditions
Min-Max Predictive Control of a Five-Phase Induction Machine
In this paper, a fuzzy-logic based operator is used instead of a traditional cost function for
the predictive stator current control of a five-phase induction machine (IM). The min-max operator
is explored for the first time as an alternative to the traditional loss function. With this proposal,
the selection of voltage vectors does not need weighting factors that are normally used within
the loss function and require a cumbersome procedure to tune. In order to cope with conflicting
criteria, the proposal uses a decision function that compares predicted errors in the torque producing
subspace and in the x-y subspace. Simulations and experimental results are provided, showing how
the proposal compares with the traditional method of fixed tuning for predictive stator current control.Ministerio de EconomĂa y Competitividad DPI 2016-76493-C3-1-R y 2014/425UniĂłn Europea DPI 2016-76493-C3-1-R y 2014/425Universidad de Sevilla DPI 2016-76493-C3-1-R y 2014/42
A generalized Gaussian process model for computer experiments with binary time series
Non-Gaussian observations such as binary responses are common in some
computer experiments. Motivated by the analysis of a class of cell adhesion
experiments, we introduce a generalized Gaussian process model for binary
responses, which shares some common features with standard GP models. In
addition, the proposed model incorporates a flexible mean function that can
capture different types of time series structures. Asymptotic properties of the
estimators are derived, and an optimal predictor as well as its predictive
distribution are constructed. Their performance is examined via two simulation
studies. The methodology is applied to study computer simulations for cell
adhesion experiments. The fitted model reveals important biological information
in repeated cell bindings, which is not directly observable in lab experiments.Comment: 49 pages, 4 figure
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