Examinando por Autor "Dragicevic, Tomislav"
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Ítem A Unified Distributed Cooperative Control of DC Microgrids Using Consensus Protocol(Institute of Electrical and Electronics Engineers Inc., 2021-05) Li, Yu; Zhang, Zhenbin; Dragicevic, Tomislav; Rodriguez, JoseIn this work, we propose an effective and simple control approach for islanded DC microgrids that allows each distributed generator (DG) to achieve accurate voltage regulation and power-sharing. An improved dynamic consensus protocol, which is robust to measurement noise and states initialization, is employed to enable each agent to locally calculate the average bus voltage with a sparse cyber network. On this basis, we propose a cooperative controller that merges the voltage regulation and power-sharing objectives in a unified fashion. The proposed approach only uses neighbors' voltage information to regulates the average bus voltage to its nominal value while maintaining proportional power-sharing or optimal power dispatch. This significantly simplifies its implementation and reduces the communication bandwidth requirement. A global model of the DC microgrid considering the cyber network is established in the form of a state-space-model, where the reference voltage vector corresponds to the input and the average bus voltage vector denotes the state. Then, the input-to-state stability analysis is carried out. To the end, comprehensive hardware-in-the-loop (HiL) tests are conducted to validate the effectiveness of the proposed control strategy. The proposed control strategy exhibits plug-and-play capability, and it is resilient to message update rate and communication failure.Ítem Finite-Set Quasi-Sliding Mode Predictive Control of LC-Filtered Voltage Source Inverters(Institute of Electrical and Electronics Engineers Inc., 2022-12-01) Zheng, Changming; Gong, Zheng; Wu, Xiaojie; Dragicevic, Tomislav; Rodriguez, Jose; Blaabjerg, FredeThree-phase voltage source inverters (VSIs) with output LC filter are preferred topologies to provide voltage and frequency support for islanded ac microgrids. This article proposes a finite-set quasi-sliding model predictive control (FS-QSMPC) scheme for LC-filtered VSIs to improve the output-voltage quality. By explicitly including a predictive sliding-mode function into the cost function, reduced steady-state ripple and enhanced robustness are achieved compared to typical FS-MPC. Besides, theoretical analysis of stability and robustness for FS-QSMPC is given. Analytical tuning of the weighting factor is also derived to decrease the tuning effort. Comparative simulations and experiments verify the presented approach. © 1982-2012 IEEE.Ítem Latest Advances of Model Predictive Control in Electrical Drives - Part I: Basic Concepts and Advanced Strategies(Institute of Electrical and Electronics Engineers Inc., 2022-04-01) Rodriguez, Jose; Garcia, Cristian; Mora, Andres; Flores-Bahamonde, Freddy; Acuna, Pablo; Novak, Mateja; Zhang, Yongchang; Tarisciotti, Luca; Davari, S. Alireza; Zhang, Zhenbin; Wang, Fengxiang; Norambuena, Margarita; Dragicevic, Tomislav; Blaabjerg, Frede; Geyer, Tobias; Kennel, Ralph; Khaburi, Davood Arab; Abdelrahem, Mohamed; Zhang, Zhen; Mijatovic, Nenad; Aguilera, Ricardo P.The application of model predictive control in electrical drives has been studied extensively in the past decade. This article presents what the authors consider the most relevant contributions published in the last years, mainly focusing on three relevant issues: weighting factor calculation when multiple objectives are utilized in the cost function, current/torque harmonic distortion optimization when the power converter switching frequency is reduced, and robustness improvement under parameters uncertainties. Therefore, this article aims to enable readers to have a more precise overview while facilitating their future research work in this exciting area.Ítem Latest Advances of Model Predictive Control in Electrical Drives - Part II: Applications and Benchmarking With Classical Control Methods(Institute of Electrical and Electronics Engineers Inc., 2022-05-01) Rodriguez, Jose; Garcia, Cristian; Mora, Andres; Davari, S. Alireza; Rodas, Jorge; Valencia, Diego Fernando; Elmorshedy, Mahmoud; Wang, Fengxiang; Zuo, Kunkun; Tarisciotti, Luca; Flores-Bahamonde, Freddy; Xu, Wei; Zhang, Zhenbin; Zhang, Yongchang; Norambuena, Margarita; Emadi, Ali; Geyer, Tobias; Kennel, Ralph; Dragicevic, Tomislav; Khaburi, Davood Arab; Zhang, Zhen; Abdelrahem, Mohamed; Mijatovic, NenadThis article presents the application of model predictive control (MPC) in high-performance drives. A wide variety of machines have been considered: Induction machines, synchronous machines, linear motors, switched reluctance motors, and multiphase machines. The control of these machines has been done by introducing minor and easy-to-understand modifications to the basic predictive control concept, showing the high flexibility and simplicity of the strategy. The second part of the article is dedicated to the performance comparison of MPC with classical control techniques such as field-oriented control and direct torque control. The comparison considers the dynamic behavior of the drive and steady-state performance metrics, such as inverter losses, current distortion in the motor, and acoustic noise. The main conclusion is that MPC is very competitive concerning classic control methods by reducing the inverter losses and the current distortion with comparable acoustic noise.Ítem Model Predictive Control of LC-Filtered Voltage Source Inverters with Optimal Switching Sequence(Institute of Electrical and Electronics Engineers Inc., 2021-03) Zheng, Changming; Dragicevic, Tomislav; Zhang, Zhenbin; Rodriguez, Jose; Blaabjerg, FredeVoltage source inverters with output LC filter enable a sinusoidal output voltage with low harmonics, suitable for islanded ac microgrid or uninterruptible power supply applications. Conventional finite-set model predictive voltage control (MPVC) applies only a single switching vector per control period, leading to a variable switching frequency and significant output ripple. This article resolves these issues by proposing an improved MPVC with optimal switching sequence (OSS-MPVC). First, an improved vector switching sequence is defined, aiming to reduce the output-voltage ripple with a constant switching frequency. Then, to tackle the difficulty in extending the OSS to high-order systems due to the coupling effect of the output filter, a generalized 'one-step estimation' solution is proposed, which directly associates the control-variable gradients with the vector switching sequence. To further enhance the output-voltage tracking accuracy, intersample dynamics are taken into account in the cost function. The control delay and dead-time compensation are also considered. Simulations and experimental results verify the feasibility of the proposed method.Ítem Optimal Cost Function Parameter Design in Predictive Torque Control (PTC) Using Artificial Neural Networks (ANN)(Institute of Electrical and Electronics Engineers Inc., 2021-08) Novak, Mateja; Xie, Haotian; Dragicevic, Tomislav; Wang, Fengxiang; Rodriguez, Jose; Blaabjerg, FredeThe use of artificial neural networks (ANNs) for the selection of weighting factors in cost function of the finite-set model-predictive control (FS-MPC) algorithm can speed up selection without imposing additional computational burden to the algorithm and ensure that optimum weights are selected for the specific application. In this article, the ANN-based design process of the weighting factors is used for predictive torque control (PTC) in a motor drive. In the design process, the weighting factors in the cost function and the reference flux value are obtained using different fitness functions. The results show that different operating conditions of the drive will have new optimum parameters of the cost function; therefore, sweeping parameters like load torque or reference speed can optimize the PTC for the whole operating range of the drive. A good match of the performance metrics predicted by the ANN and the simulation model is also observed. The experiments demonstrate that the selected cost function parameters can provide a fast drive start and good performance during different loading conditions and also in reversing of the drive.Ítem Pareto Optimal Weighting Factor Design of Predictive Current Controller of a Six-Phase Induction Machine Based on Particle Swarm Optimization Algorithm(Institute of Electrical and Electronics Engineers Inc., 2022-02-01) Fretes, Hector; Rodas, Jorge; Doval-Gandoy, Jesus; Gomez, Victor; Gomez, Nicolas; Novak, Mateja; Rodriguez, Jose; Dragicevic, TomislavFinite-set model predictive control (FS-MPC) as predictive current control (PCC) is considered an exciting option for the stator current control of multiphase machines due to their control flexibility and easy inclusion of constraints. The weighting factors (WFs) of PCC must be tuned for the variables of interest, such as the machine losses x-y currents, typically performed by trial-and-error procedure. Tuning methods based on artificial neural network (ANN) or the coefficient of variation were proposed for three-phase inverter and motor drive applications. However, the extension of this concept to the multiphase machine application is not straightforward, and only empirical procedures have been reported. In this context, this article proposes an optimal method to tune the WF of the PCC based on the multiobjective particle swarm optimization (MOPSO) algorithm. A Pareto dominance concept is used for the MOPSO to find the optimal WF values for the PCC, comparing parameters of root-mean-square error of the stator tracking currents. The proposed method offers a systematic approach to the WF selection, with an algorithm of easy implementation with direct control over the size of the search space and the speed of convergence. Simulation and experimental results in steady-state and transient conditions are provided to validate the proposed offline tuning procedure of the PCC of a six-phase induction machine. The improvements of RMSE can be more than 500% for x-y subspace, with minor effect in α -β subspace. Finally, the proposed method is extended to a more complex cost function, and the results are compared with an ANN approach. © 2013 IEEE.