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In this paper, a deadbeat predictive control (DBPC) technique for doubly-fed induction generators (DFIGs) in wind turbine applications is proposed. The major features of DBPC scheme are its quick dynamic performance and its fixed switching frequency. However, the basic concept of DBPC is computing the reference voltage for the next sample from the mathematical model of the generator. Therefore, the DBPC is highly sensitive to variations of the parameters of the DFIG. To reduce this sensitivity, a disturbance observer is designed in this paper to improve the robustness of the proposed DBPC scheme. The proposed observer is very simple and easy to be implemented in real-time applications. The proposed DBPC strategy is implemented in the laboratory. Several experiments are performed with and without mismatches in the DFIG parameters. The experimental results proved the superiority of the proposed DBPC strategy over the traditional DBPC technique.
Mohamed Abdelrahem; Christoph Hackl; Ralph Kennel; Jose Rodriguez. Low Sensitivity Predictive Control for Doubly-Fed Induction Generators Based Wind Turbine Applications. Sustainability 2021, 13, 9150 .
AMA StyleMohamed Abdelrahem, Christoph Hackl, Ralph Kennel, Jose Rodriguez. Low Sensitivity Predictive Control for Doubly-Fed Induction Generators Based Wind Turbine Applications. Sustainability. 2021; 13 (16):9150.
Chicago/Turabian StyleMohamed Abdelrahem; Christoph Hackl; Ralph Kennel; Jose Rodriguez. 2021. "Low Sensitivity Predictive Control for Doubly-Fed Induction Generators Based Wind Turbine Applications." Sustainability 13, no. 16: 9150.
The smart-grid has requirements of flexible automation, efficiency, reliability, resiliency and scalability. These are necessitated by the increasing penetration of power-electronics converters that interface distributed renewable energy systems which energize the fast-evolving electric power network. Microgrids (MGs) have been identified as modular grids with the potential to effectively satisfy these characteristics when enhanced with advanced control capabilities. Model predictive control (MPC) facilitates the multivariable control of power electronic systems while accommodating physical constraints without the necessity for a cascaded structure. These features result in fast control dynamic response and good performance for systems involving non-linearities. This paper is a survey of the recent advances in MPC-based converters in MGs. Schemes for the primary control of MG parameters are presented. We also present opportunities for the MPC converter control of autonomous MGs (power quality and inertia enhancement), and transportation electrification. Finally, we demonstrate MPC’s capabilities through hardware-in-the-loop (HiL) results for a proposed adaptive MPC scheme for grid-forming converters.
Zhenbin Zhang; Oluleke Babayomi; Tomislav Dragicevic; Rasool Heydari; Cristian Garcia; Jose Rodriguez; Ralph Kennel. Advances and opportunities in the model predictive control of microgrids: Part I–Primary layer. International Journal of Electrical Power & Energy Systems 2021, 134, 107339 .
AMA StyleZhenbin Zhang, Oluleke Babayomi, Tomislav Dragicevic, Rasool Heydari, Cristian Garcia, Jose Rodriguez, Ralph Kennel. Advances and opportunities in the model predictive control of microgrids: Part I–Primary layer. International Journal of Electrical Power & Energy Systems. 2021; 134 ():107339.
Chicago/Turabian StyleZhenbin Zhang; Oluleke Babayomi; Tomislav Dragicevic; Rasool Heydari; Cristian Garcia; Jose Rodriguez; Ralph Kennel. 2021. "Advances and opportunities in the model predictive control of microgrids: Part I–Primary layer." International Journal of Electrical Power & Energy Systems 134, no. : 107339.
This paper proposes an effective solver for implicit Continuous Set Model Predictive Control for the current control loop of synchronous motor drives with input constraints, allowing for reaching the maximum voltage feasible set. The related quadratic programming problem requires an iterative solver to find the optimal solution. The real-time certification of the algorithm is of paramount importance to move the technology toward industrial-scale applications by the proposed solver. The total number of operations can be computed in the worst-case scenario, thus the maximum computational time is known a priori. The solver is deeply illustrated, showing its feasibility for real-time applications in the microseconds range by means of experimental tests. Promising results are obtained with respect to well known general purpose solvers.
Riccardo Torchio; Andrea Favato; Paolo Gherardo Carlet; Francesco Toso; Ruggero Carli; Mattia Bruschetta; Silverio Bolognani; Jose Rodriguez. Fast Solver for Implicit Continuous Set Model Predictive Control of Electric Drives. 2021, 1 .
AMA StyleRiccardo Torchio, Andrea Favato, Paolo Gherardo Carlet, Francesco Toso, Ruggero Carli, Mattia Bruschetta, Silverio Bolognani, Jose Rodriguez. Fast Solver for Implicit Continuous Set Model Predictive Control of Electric Drives. . 2021; ():1.
Chicago/Turabian StyleRiccardo Torchio; Andrea Favato; Paolo Gherardo Carlet; Francesco Toso; Ruggero Carli; Mattia Bruschetta; Silverio Bolognani; Jose Rodriguez. 2021. "Fast Solver for Implicit Continuous Set Model Predictive Control of Electric Drives." , no. : 1.
This paper proposes an effective solver for implicit Continuous Set Model Predictive Control for the current control loop of synchronous motor drives with input constraints, allowing for reaching the maximum voltage feasible set. The related quadratic programming problem requires an iterative solver to find the optimal solution. The real-time certification of the algorithm is of paramount importance to move the technology toward industrial-scale applications by the proposed solver. The total number of operations can be computed in the worst-case scenario, thus the maximum computational time is known a priori. The solver is deeply illustrated, showing its feasibility for real-time applications in the microseconds range by means of experimental tests. Promising results are obtained with respect to well known general purpose solvers.
Riccardo Torchio; Andrea Favato; Paolo Gherardo Carlet; Francesco Toso; Ruggero Carli; Mattia Bruschetta; Silverio Bolognani; Jose Rodriguez. Fast Solver for Implicit Continuous Set Model Predictive Control of Electric Drives. 2021, 1 .
AMA StyleRiccardo Torchio, Andrea Favato, Paolo Gherardo Carlet, Francesco Toso, Ruggero Carli, Mattia Bruschetta, Silverio Bolognani, Jose Rodriguez. Fast Solver for Implicit Continuous Set Model Predictive Control of Electric Drives. . 2021; ():1.
Chicago/Turabian StyleRiccardo Torchio; Andrea Favato; Paolo Gherardo Carlet; Francesco Toso; Ruggero Carli; Mattia Bruschetta; Silverio Bolognani; Jose Rodriguez. 2021. "Fast Solver for Implicit Continuous Set Model Predictive Control of Electric Drives." , no. : 1.
Oswaldo Menendez; Fernando Alfredo Auat Cheein; Jose Rodriguez. Displacement Current-Based Energy Harvesters in Power Grids: Topologies and Performance Evaluation. IEEE Industrial Electronics Magazine 2021, PP, 2 -16.
AMA StyleOswaldo Menendez, Fernando Alfredo Auat Cheein, Jose Rodriguez. Displacement Current-Based Energy Harvesters in Power Grids: Topologies and Performance Evaluation. IEEE Industrial Electronics Magazine. 2021; PP (99):2-16.
Chicago/Turabian StyleOswaldo Menendez; Fernando Alfredo Auat Cheein; Jose Rodriguez. 2021. "Displacement Current-Based Energy Harvesters in Power Grids: Topologies and Performance Evaluation." IEEE Industrial Electronics Magazine PP, no. 99: 2-16.
The smart-grid has requirements of flexible automation, efficiency, reliability, resiliency and scalability. These are necessitated by the increasing penetration of power-electronics converters that interface distributed renewable energy systems which energize the fast-evolving electric power network. Microgrids (MGs) have been identified as modular grids with the potential to effectively satisfy these characteristics when enhanced with advanced control capabilities. Model predictive control (MPC) facilitates the multivariable control of power electronic systems while accommodating physical constraints without the necessity for a cascaded structure. These features result in fast control dynamic response and good performance for systems involving non-linearities. This paper is a survey of the recent advances in MPC-based converters in MGs. Schemes for the primary control of MG parameters are presented. We also present opportunities for the MPC converter control of autonomous MGs (power quality and inertia enhancement), and transportation electrification. Finally, we demonstrate MPC’s capabilities through hardware-in-the-loop (HiL) results for a proposed adaptive MPC scheme for grid-forming converters.
Zhenbin Zhang; Oluleke Babayomi; Tomislav Dragicevic; Rasool Heydari; Cristian Garcia; Jose Rodriguez; Ralph Kennel. Advances and opportunities in the model predictive control of microgrids: Part I–primary layer. International Journal of Electrical Power & Energy Systems 2021, 134, 107411 .
AMA StyleZhenbin Zhang, Oluleke Babayomi, Tomislav Dragicevic, Rasool Heydari, Cristian Garcia, Jose Rodriguez, Ralph Kennel. Advances and opportunities in the model predictive control of microgrids: Part I–primary layer. International Journal of Electrical Power & Energy Systems. 2021; 134 ():107411.
Chicago/Turabian StyleZhenbin Zhang; Oluleke Babayomi; Tomislav Dragicevic; Rasool Heydari; Cristian Garcia; Jose Rodriguez; Ralph Kennel. 2021. "Advances and opportunities in the model predictive control of microgrids: Part I–primary layer." International Journal of Electrical Power & Energy Systems 134, no. : 107411.
In this paper, a low complexity finite-control-set model predictive control (FCS-MPC) based on the discrete space vector modulation is proposed for T-type three-phase three-level (3P-3L) converters. Different from the conventional FCS-MPC, 48 virtual voltage vectors (VVs) of the converter are constructed by real voltage vectors based on the discrete space vector modulation. Thus, the performance of 3P-3L converters is significantly improved and the peak amplitude of high-order harmonics concentrates at the sampling frequency. Furthermore, two-stage FCS-MPC base on virtual VVs is proposed to reduce the computation burden. Its first-stage selects one of six virtual VVs that minimizes the current tracking error. Then, these candidate VVs located in the same sector as the optimal virtual VV selected in the first-stage are evaluated in the second-stage optimization. Thus, the computational efficiency has been greatly improved. To verify the validity of the proposed control method and show its superiority over the conventional FCS-MPC, experimental results are presented.
Yong Yang; Huiqing Wen; Mingdi Fan; Xinan Zhang; Liqun He; Rong Chen; Menxi Xie; Margarita Norambuena; Jose Gae Rodriguez. Low Complexity Finite-control-set MPC Based on Discrete Discrete Space Vector Modulation for T-type Three-phase Three-level Converters. IEEE Transactions on Power Electronics 2021, PP, 1 -1.
AMA StyleYong Yang, Huiqing Wen, Mingdi Fan, Xinan Zhang, Liqun He, Rong Chen, Menxi Xie, Margarita Norambuena, Jose Gae Rodriguez. Low Complexity Finite-control-set MPC Based on Discrete Discrete Space Vector Modulation for T-type Three-phase Three-level Converters. IEEE Transactions on Power Electronics. 2021; PP (99):1-1.
Chicago/Turabian StyleYong Yang; Huiqing Wen; Mingdi Fan; Xinan Zhang; Liqun He; Rong Chen; Menxi Xie; Margarita Norambuena; Jose Gae Rodriguez. 2021. "Low Complexity Finite-control-set MPC Based on Discrete Discrete Space Vector Modulation for T-type Three-phase Three-level Converters." IEEE Transactions on Power Electronics PP, no. 99: 1-1.
Sliding-mode control (SMC) has been widely used in grid-connected converter system (GCC) systems because of its robustness to parameter variations and external disturbances. However, chattering in SMC may deteriorate the tracking accuracy and can easily excite high-frequency unmodeled dynamics. To solve this problem, this paper presents a fuzzy-fractional-order nonsingular terminal sliding-mode controller (Fuzzy-FONTSMC) for the grid current control of LCL-GCCs. First, the system modeling, design of the integer-order NTSMC controller and state estimation based on the Kalman filter to minimize the sampling sensors are described. Second, the Fuzzy-FONTSMC controller is introduced for optimal fraction-order selection and chattering mitigation, this controller exhibits fast convergence with high tracking accuracy and strong robustness. Finally, the Lyapunov theorem is used to analyze the system stability. Experimental comparisons on a 10-kVA laboratory prototype validate the superior performance and effectiveness of the proposed method under many scenarios.
Bo Long; Pengjie Lu; Kil To Chong; Jose Rodriguez; Josep M. Guerrero. Robust Fuzzy-Fractional-Order Nonsingular Terminal Sliding-Mode Control of An LCL-Type Grid-Connected Converter. IEEE Transactions on Industrial Electronics 2021, PP, 1 -1.
AMA StyleBo Long, Pengjie Lu, Kil To Chong, Jose Rodriguez, Josep M. Guerrero. Robust Fuzzy-Fractional-Order Nonsingular Terminal Sliding-Mode Control of An LCL-Type Grid-Connected Converter. IEEE Transactions on Industrial Electronics. 2021; PP (99):1-1.
Chicago/Turabian StyleBo Long; Pengjie Lu; Kil To Chong; Jose Rodriguez; Josep M. Guerrero. 2021. "Robust Fuzzy-Fractional-Order Nonsingular Terminal Sliding-Mode Control of An LCL-Type Grid-Connected Converter." IEEE Transactions on Industrial Electronics PP, no. 99: 1-1.
Finite control set model predictive control (FCS-MPC) has been widely recognized in the field of electrical drive control during the past decades, due to its merits of quick dynamic response and low switching frequency. However, it is inherently penalized by the high tracking deviations in the steady state as well as exhaustive search among the switching sequences. To cope with this issue, a low-complexity gradient descent based finite control set predictive current control (GD-FCSPCC) combined with backtracking optimized iteration approach is proposed in this paper, aiming to improve the control performance by effectively tracking the reference value. Firstly, FCS-PCC is reformulated as a quadratic programming (QP) problem from a geometric perspective. Consequently, the convexity of QP problem is proved to underlying the gradient descent to minimize the tracking error in an effective manner. Thus, the control objectives are determined by optimizing the deviation between the gradient descent and the stator current derivative in a cascade structure, to reduce the number of enumerated sequences. The procedures are repeated in the iteration periods optimized via a backtracking search method, until the stopping criterion is satisfied. The effectiveness of the proposed GD-FCSPCC is experimentally validated on a 2.2 kW induction machine testbench.
Haotian Xie; Fengxiang Wang; Qian Xun; Yingjie He; Jose Rodriguez; Ralph Kennel. A Low-Complexity Gradient Descent Solution with Backtracking Iteration Approach for Finite Control Set Predictive Current Control. IEEE Transactions on Industrial Electronics 2021, PP, 1 -1.
AMA StyleHaotian Xie, Fengxiang Wang, Qian Xun, Yingjie He, Jose Rodriguez, Ralph Kennel. A Low-Complexity Gradient Descent Solution with Backtracking Iteration Approach for Finite Control Set Predictive Current Control. IEEE Transactions on Industrial Electronics. 2021; PP (99):1-1.
Chicago/Turabian StyleHaotian Xie; Fengxiang Wang; Qian Xun; Yingjie He; Jose Rodriguez; Ralph Kennel. 2021. "A Low-Complexity Gradient Descent Solution with Backtracking Iteration Approach for Finite Control Set Predictive Current Control." IEEE Transactions on Industrial Electronics PP, no. 99: 1-1.
The papers in this special section focus on model predictive control (MPC) in energy conversion systems. MPC refers to a broad range of control strategies that make explicit use of a model of the system/device to be controlled optimally. In order to obtain the optimal control signal (or sequence of control signals), MPC optimizes a certain cost function at regular intervals. Due to its unique capabilities to deal with constraints on actuators and system states as well as its theoretical basis, MPC has been widely received and successfully used for many decades, mostly for control of slow industrial plants. However, with continuous advances of control theory and increasing computational capabilities of modern microprocessors, this control strategy has recently became a technically feasible solution for control of energy conversion systems that operate at much faster times scales.
Tomislav Dragicevic; Alessandra Parisio; Jose Rodriguez; Colin Jones; Daniel Quevedo; Luca Ferrarini; Matthias Preindl; Qobad Shafiee; Thomas Morstyn. Guest Editorial Model Predictive Control in Energy Conversion Systems. IEEE Transactions on Energy Conversion 2021, 36, 1311 -1312.
AMA StyleTomislav Dragicevic, Alessandra Parisio, Jose Rodriguez, Colin Jones, Daniel Quevedo, Luca Ferrarini, Matthias Preindl, Qobad Shafiee, Thomas Morstyn. Guest Editorial Model Predictive Control in Energy Conversion Systems. IEEE Transactions on Energy Conversion. 2021; 36 (2):1311-1312.
Chicago/Turabian StyleTomislav Dragicevic; Alessandra Parisio; Jose Rodriguez; Colin Jones; Daniel Quevedo; Luca Ferrarini; Matthias Preindl; Qobad Shafiee; Thomas Morstyn. 2021. "Guest Editorial Model Predictive Control in Energy Conversion Systems." IEEE Transactions on Energy Conversion 36, no. 2: 1311-1312.
Switched Reluctance Motors (SRMs) have become a popular alternative to replace permanent magnet machines in high-performance emerging applications such as automotive and aerospace. However, its market attractiveness is limited by the difficulty in control given its nonlinear behaviour. Model predictive control (MPC) is a promising solution to deal with this problem as per its notable features to deal with complex systems, nonlinearities and constraints. Still, the applications in SRMs are at an early stage compared to other drives. This paper aims to discuss the recent advancements and challenges in MPC for SRMs and a vision of its future developments and applications. The article describes the main difficulties in SRM control and the different approaches adopted to date by MPC to solve them. It also analyzes the control objectives that should still be considered in SRM drives, their particular challenges and how recent MPC developments in other AC drives can be adapted to the SRM case. The paper then proposes a roadmap of future works to achieve a unified and reliable control strategy that boosts SRM to outperform other drives, relating the control objectives to its potential applications.
Diego F. Valencia; Rasul Tarvirdilu-Asl; Cristian Garcia; Jose Rodriguez; Ali Emadi. Vision, Challenges, and Future Trends of Model Predictive Control in Switched Reluctance Motor Drives. IEEE Access 2021, 9, 69926 -69937.
AMA StyleDiego F. Valencia, Rasul Tarvirdilu-Asl, Cristian Garcia, Jose Rodriguez, Ali Emadi. Vision, Challenges, and Future Trends of Model Predictive Control in Switched Reluctance Motor Drives. IEEE Access. 2021; 9 ():69926-69937.
Chicago/Turabian StyleDiego F. Valencia; Rasul Tarvirdilu-Asl; Cristian Garcia; Jose Rodriguez; Ali Emadi. 2021. "Vision, Challenges, and Future Trends of Model Predictive Control in Switched Reluctance Motor Drives." IEEE Access 9, no. : 69926-69937.
Voltage source Multilevel Inverters (MLIs) are vital components for medium voltage and high-power applications due to their advantages like modularity and better power quality. However, the number of components used is significant. In this paper, an improved asymmetrical multilevel inverter topology is proposed producing 17-levels output voltage utilizing two dc sources. The circuit is developed to reduce the number of isolated dc-sources used without reducing output levels. The circuit utilizes six two-quadrant switches, three four-quadrant switches and four capacitors. The capacitors are self-balancing and do not require extra attention, i.e. the control system is simple for the proposed MLI. Detailed analysis of the topology under linear and non-linear loading conditions is carried out. Comparison with other similar topologies shows that the proposed topology is superior in device count, power quality, Total Standing Voltage (TSV), and cost factor. The performance of the topology is validated for different load conditions through MATLAB/Simulink environment and the prototype developed in the laboratory. Furthermore, thermal analysis of the circuit is done, and the losses are calculated via PLECS software. The topology offers a total harmonic distortion (THD) of 4.79% in the output voltage, with all the lower order harmonics being less than 5% complying with the IEEE standards.
M. Saad Bin Arif; Uvais Mustafa; Shahrin Bin Md Ayob; Jose Rodriguez; Abdul Nadeem; Mohamed Abdelrahem. Asymmetrical 17-Level Inverter Topology With Reduced Total Standing Voltage and Device Count. IEEE Access 2021, 9, 69710 -69723.
AMA StyleM. Saad Bin Arif, Uvais Mustafa, Shahrin Bin Md Ayob, Jose Rodriguez, Abdul Nadeem, Mohamed Abdelrahem. Asymmetrical 17-Level Inverter Topology With Reduced Total Standing Voltage and Device Count. IEEE Access. 2021; 9 ():69710-69723.
Chicago/Turabian StyleM. Saad Bin Arif; Uvais Mustafa; Shahrin Bin Md Ayob; Jose Rodriguez; Abdul Nadeem; Mohamed Abdelrahem. 2021. "Asymmetrical 17-Level Inverter Topology With Reduced Total Standing Voltage and Device Count." IEEE Access 9, no. : 69710-69723.
There has been an increasing interest in using model predictive control (MPC) for power electronic applications. However, the exponential increase in computational complexity and demand of computing resources hinders the practical adoption of this highly promising control technique. In this paper, a new MPC approach using an artificial neural network (termed ANN-MPC) is proposed to overcome these barriers. The ANN-MPC approach can significantly reduce the computing need and allow the use of more accurate high-order system models due to the simple mathematical expression of ANN. This is particularly important for multi-level and multi-phase power systems as their number of switching states increases exponentially. Furthermore, the ANN-MPC approach can retain the robustness for system parameter uncertainties by flexibly setting the constraint conditions. The basic concept, ANN structure, off-line training method, and online operation of ANN-MPC are described in detail. The computing resource requirement of the ANN-MPC and conventional MPC are analyzed and compared. The ANN-MPC concept is validated by both simulation and experimental results on two kW-class flying capacitor multilevel converters. It is demonstrated that the FPGA-based ANN-MPC controller can significantly reduce the FPGA resource requirement while offering a control performance same as the conventional MPC.
Daming Wang; Z. John Shen; Xin Yin; Sai Tang; Xifei Liu; Chao Zhang; Jun Wang; Jose Rodriguez; Margarita Norambuena. Model Predictive Control Using Artificial Neural Network for Power Converters. IEEE Transactions on Industrial Electronics 2021, PP, 1 -1.
AMA StyleDaming Wang, Z. John Shen, Xin Yin, Sai Tang, Xifei Liu, Chao Zhang, Jun Wang, Jose Rodriguez, Margarita Norambuena. Model Predictive Control Using Artificial Neural Network for Power Converters. IEEE Transactions on Industrial Electronics. 2021; PP (99):1-1.
Chicago/Turabian StyleDaming Wang; Z. John Shen; Xin Yin; Sai Tang; Xifei Liu; Chao Zhang; Jun Wang; Jose Rodriguez; Margarita Norambuena. 2021. "Model Predictive Control Using Artificial Neural Network for Power Converters." IEEE Transactions on Industrial Electronics PP, no. 99: 1-1.
In this paper, an efficient model predictive control (MPC) using virtual voltage vectors for three-phase three-level converters is proposed. The proposed MPC achieves constant switching frequency by applying four voltage vectors (VVs), including one virtual VV and three other VVs, in each control cycle. In addition, to reduce the computational burden, two-stage MPC approach is adopted. The first stage chooses one of six medium voltage vectors that minimizes the cost function. Then, in the second stage, these voltage vectors including virtual voltage vectors which locate the same sector with the optimal medium voltage vector, are involved in the MPC optimization. The advantages of the proposed MPC over the classical MPC have been validated through experimental results.
Yong Yang; Huiqing Wen; Rong Chen; Mingdi Fan; Xinan Zhang; Margarita Norambuena; Jose Rodriguez. An Efficient Model Predictive Control Using Virtual Voltage Vectors for Three-phase Three-level Converters with Constant Switching Frequency. IEEE Transactions on Industrial Electronics 2021, PP, 1 -1.
AMA StyleYong Yang, Huiqing Wen, Rong Chen, Mingdi Fan, Xinan Zhang, Margarita Norambuena, Jose Rodriguez. An Efficient Model Predictive Control Using Virtual Voltage Vectors for Three-phase Three-level Converters with Constant Switching Frequency. IEEE Transactions on Industrial Electronics. 2021; PP (99):1-1.
Chicago/Turabian StyleYong Yang; Huiqing Wen; Rong Chen; Mingdi Fan; Xinan Zhang; Margarita Norambuena; Jose Rodriguez. 2021. "An Efficient Model Predictive Control Using Virtual Voltage Vectors for Three-phase Three-level Converters with Constant Switching Frequency." IEEE Transactions on Industrial Electronics PP, no. 99: 1-1.
Measurement errors are inevitable in the current sensors, which cause speed ripple including one and twice the stator electrical frequency. For the undesired harmonics, the adaptive selected harmonic elimination (ASHE) algorithm outputs the sum of the sinusoidal signals multiplied by the adaptive weights. In order to solve the current measurement error (CME) issue, this paper adopts the ASHE algorithm. According to the deterministic functional relationship of the surface-mounted permanent magnet synchronous motor (SPMSM), the adopted algorithm extracts the harmonics of the steady state speed error, and outputs the q-axis current compensation. However, there is no explicit connection between speed and d-axis current, so their relationship is uncertain. The remaining d-axis CMEs result in poor current performance. Therefore, this paper proposes an improved ASHE algorithm, which compensates the dq-axis CMEs simultaneously depending on their mutual deterministic connection. The proposed method does not need motor parameters, additional sensors or complex calculation process. Finally, the effectiveness of the method is verified by the SPMSM platform. Experimental results also show that the proposed method reduces speed ripple and suppresses the negative effects of CMEs.
Kai Zhang; Mingdi Fan; Yong Yang; Zhongkui Zhu; Cristian Garcia; Jose Gae Rodriguez. An Improved Adaptive Selected Harmonic Elimination Algorithm for Current Measurement Error Correction of PMSMs. IEEE Transactions on Power Electronics 2021, 36, 13128 -13138.
AMA StyleKai Zhang, Mingdi Fan, Yong Yang, Zhongkui Zhu, Cristian Garcia, Jose Gae Rodriguez. An Improved Adaptive Selected Harmonic Elimination Algorithm for Current Measurement Error Correction of PMSMs. IEEE Transactions on Power Electronics. 2021; 36 (11):13128-13138.
Chicago/Turabian StyleKai Zhang; Mingdi Fan; Yong Yang; Zhongkui Zhu; Cristian Garcia; Jose Gae Rodriguez. 2021. "An Improved Adaptive Selected Harmonic Elimination Algorithm for Current Measurement Error Correction of PMSMs." IEEE Transactions on Power Electronics 36, no. 11: 13128-13138.
An accurate definition of a system model significantly affects the performance of model-based control strategies, for example, model predictive control (MPC). In this paper, a model-free predictive control strategy is presented to mitigate all ramifications of the model’s uncertainties and parameter mismatch between the plant and controller for the control of power electronic converters in applications such as microgrids. A specific recurrent neural network structure called state-space neural network (ssNN) is proposed as a model-free current predictive control for a three-phase power converter. In this approach, NN weights are updated through particle swarm optimization (PSO) for faster convergence. After the training process, the proposed ssNN-PSO combined with the predictive controller using a performance criterion overcomes parameter variations in the physical system. A comparison has been carried out between the conventional MPC and the proposed model-free predictive control in different scenarios. The simulation results of the proposed control scheme exhibit more robustness compared to the conventional finite-control-set MPC.
Sanaz Sabzevari; Rasool Heydari; Maryam Mohiti; Mehdi Savaghebi; Jose Rodriguez. Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters. Energies 2021, 14, 2325 .
AMA StyleSanaz Sabzevari, Rasool Heydari, Maryam Mohiti, Mehdi Savaghebi, Jose Rodriguez. Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters. Energies. 2021; 14 (8):2325.
Chicago/Turabian StyleSanaz Sabzevari; Rasool Heydari; Maryam Mohiti; Mehdi Savaghebi; Jose Rodriguez. 2021. "Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters." Energies 14, no. 8: 2325.
Finite control set model predictive control (FCS-MPC) is a simple method and has an appropriate dynamic response for the drive applications. Applying additional control objects, e.g., the maximum torque per ampere (MTPA), is easy in FCS-MPC because of its characteristics. A direct application of FCS-MPC to MTPA is the predictive direct angle control method. Though this method eased the MTPA process, the good result is highly sensitive to the proper selection of the weighting factor. Furthermore, finding the best phase angle needs a complicated optimization process. In this paper, the application of simplified predictive control is proposed for angle control. By this proposed method, not only the weighting factor is eliminated but also the constraints of the motor are considered in the control strategy. In this way, the phase angle is automatically controlled in the proper value due to the torque while tedious computation is avoided. Therefore, the proposed method is valid in a wide range of operating points while no optimization process is performed due to changes in speed and torque. This proposed method is evaluated by simulations and experiments.
Arash Fereidooni; S. Alireza Davari; Cristian Garcia; Jose Rodriguez. Simplified Predictive Stator Current Phase Angle Control of Induction Motor With a Reference Manipulation Technique. IEEE Access 2021, 9, 54173 -54183.
AMA StyleArash Fereidooni, S. Alireza Davari, Cristian Garcia, Jose Rodriguez. Simplified Predictive Stator Current Phase Angle Control of Induction Motor With a Reference Manipulation Technique. IEEE Access. 2021; 9 ():54173-54183.
Chicago/Turabian StyleArash Fereidooni; S. Alireza Davari; Cristian Garcia; Jose Rodriguez. 2021. "Simplified Predictive Stator Current Phase Angle Control of Induction Motor With a Reference Manipulation Technique." IEEE Access 9, no. : 54173-54183.
This paper studies the Model Predictive Control (MPC) for a twisted buck-boost inverter based on unfolding circuit. The focus is on the practical implementation of the MPC algorithm for the microcontroller designed for application in power electronics. Selection of proper cost function parameters along with a continuous control set reduced prediction horizon, at the same time keeping good quality of the grid current. The results showed that simplified differential equations and a multicore microcontroller contribute to the sample time reduction, which in turn increases the sampling frequency with the corresponding increase in the output current quality. The simulation and experimental results confirmed theoretical predictions. In conclusion, the MPC technique suits for reducing zero-crossing distortion and in applications based on unfolding circuit.
Oleksandr Matiushkin; Oleksandr Husev; Jose Rodriguez; Hector Young; Indrek Roasto. Feasibility Study of Model Predictive Control for Grid-Connected Twisted Buck-Boost Inverter. IEEE Transactions on Industrial Electronics 2021, PP, 1 -1.
AMA StyleOleksandr Matiushkin, Oleksandr Husev, Jose Rodriguez, Hector Young, Indrek Roasto. Feasibility Study of Model Predictive Control for Grid-Connected Twisted Buck-Boost Inverter. IEEE Transactions on Industrial Electronics. 2021; PP (99):1-1.
Chicago/Turabian StyleOleksandr Matiushkin; Oleksandr Husev; Jose Rodriguez; Hector Young; Indrek Roasto. 2021. "Feasibility Study of Model Predictive Control for Grid-Connected Twisted Buck-Boost Inverter." IEEE Transactions on Industrial Electronics PP, no. 99: 1-1.
Energy structures from non-conventional energy source has become highly demanded nowadays. In this way, the maximum power extraction from photovoltaic (PV) systems has attracted the attention, therefore an optimization technique is necessary to improve the performance of solar systems. This article proposes the use of ABC (artificial bee colony) algorithm for the maximum power point tracking (MPPT) of a PV system using a DC-DC converter. The procedure of the ABC MPPT algorithm is using data values from PV module, the P-V characteristic is identified and the optimal voltage is selected. Then, the MPPT strategy is applied to obtain the voltage reference for the outer PI control loop, which in turn provides the current reference to the predictive digital current programmed control. A real-time and high-speed simulator (PLECS RT Box 1) and a digital signal controller (DSC) are used to implement the hardware-in-the-loop system to obtain the results. The general system does not have a high computational cost and can be implemented in a commercial low-cost DSC (TI 28069M). The proposed MPPT strategy is compared to the conventional perturb and observe method, results show the proposed method archives a much superior performance.
Catalina Gonzalez-Castano; Carlos Restrepo; Samir Kouro; Jose Rodriguez. MPPT Algorithm Based on Artificial Bee Colony for PV System. IEEE Access 2021, 9, 43121 -43133.
AMA StyleCatalina Gonzalez-Castano, Carlos Restrepo, Samir Kouro, Jose Rodriguez. MPPT Algorithm Based on Artificial Bee Colony for PV System. IEEE Access. 2021; 9 ():43121-43133.
Chicago/Turabian StyleCatalina Gonzalez-Castano; Carlos Restrepo; Samir Kouro; Jose Rodriguez. 2021. "MPPT Algorithm Based on Artificial Bee Colony for PV System." IEEE Access 9, no. : 43121-43133.
This paper proposes an efficient and optimal reduced control set model predictive flux control (RCS-MPFC) for a three-level neutral-point-clamped voltage source inverter (3L-NPC VSI) fed induction motor. The proposed algorithm reduces the computational time in the prediction stage without causing any suboptimality. The optimal voltage vector selected by the proposed method produces the same cost function value as that of the conventional FCS-MPFC which requires enumerating all 27 voltage vectors. Therefore, the proposed algorithm achieves the same performance as the conventional method in the entire range of operation of IM drives while the computational effort is significantly reduced. Experimental results verify the effectiveness of the proposed algorithm and its superior performance compared to the existing RCS-MPFC scheme.
Ilham Osman; Dan Xiao; Muhammed Fazlur Rahman; Margarita Norambuena; Jose Rodriguez. An Optimal Reduced-Control-Set Model Predictive Flux Control for 3L-NPC Fed Induction Motor Drive. IEEE Transactions on Energy Conversion 2021, PP, 1 -1.
AMA StyleIlham Osman, Dan Xiao, Muhammed Fazlur Rahman, Margarita Norambuena, Jose Rodriguez. An Optimal Reduced-Control-Set Model Predictive Flux Control for 3L-NPC Fed Induction Motor Drive. IEEE Transactions on Energy Conversion. 2021; PP (99):1-1.
Chicago/Turabian StyleIlham Osman; Dan Xiao; Muhammed Fazlur Rahman; Margarita Norambuena; Jose Rodriguez. 2021. "An Optimal Reduced-Control-Set Model Predictive Flux Control for 3L-NPC Fed Induction Motor Drive." IEEE Transactions on Energy Conversion PP, no. 99: 1-1.