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Prof. Ralph Kennel
Technische Universitaet Muenchen, Chair of Electrical Drive Systems and Power Electronics

Basic Info

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Research Keywords & Expertise

0 predictive control
0 Contactless Energy Transmission Systems
0 power electornics
0 Sensorless control of electric machines
0 Drives and electrical machines

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predictive control
Sensorless control of electric machines

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Short Biography

Ralph M. Kennel received his Dr.-Ing. (Ph.D.) degree from the University of Kaiserslautern in 1984. From 1983 to 1999 he worked for Robert BOSCH GmbH (Germany). From 1999 to 2008 he was Professor for Electrical Machines and Drives at Wuppertal University (Germany). Since 2008 he has been Professor for Electrical Drive Systems and Power Electronics at Technische Universitaet Muenchen (Germany). Professor Kennel was awarded a doctor honoris causa (Dr. h.c.) from Universitatea Stefan cel Mare in Suceava (Romania), received the Harry Owen Distinguished Service Award from PELS, and the Distinguished Service and Outstanding Achievement Awards from EPE.

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Journal article
Published: 16 August 2021 in Sustainability
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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.

ACS Style

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 Style

Mohamed 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 Style

Mohamed 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.

Review article
Published: 02 August 2021 in International Journal of Electrical Power & Energy Systems
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Zhenbin 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.

Research article electrical engineering
Published: 12 July 2021 in Arabian Journal for Science and Engineering
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Photovoltaic (PV) power systems are integrated with high penetration levels into the grid. This in turn encourages several modifications for grid codes to sustain grid stability and resilience. Recently, constant power management and regulation is a very common approach, which is used to limit the PV power production. Thus, this article proposes dual-mode power generation algorithm for grid-connected PV systems. The developed system considers the two-stage PV configuration for implementation, where the dual-mode power generation technique is executed within the DC–DC conversion (boost) stage. Most of the techniques adopted for dual-mode power operation employ the conventional perturb and observe method, which is known with unsatisfactory performance at fast-changing atmospheric conditions. Considering this issue, this study suggests a modified maximum power point tracker for power extraction. Furthermore, a new adaptive DC-link controller is developed to improve the DC-link voltage profile at different operating conditions. The adaptive DC-link controller is compared with the traditional PI controller for voltage regulation. The inverter control is accomplished using finite-set model predictive control with two control objectives, namely reference current tracking and switching frequency minimization. The overall control methodology is evaluated at different atmospheric and operating conditions using MATLAB/Simulink software.

ACS Style

Mostafa Ahmed; Ibrahim Harbi; Ralph Kennel; Mohamed Abdelrahem. Dual-Mode Power Operation for Grid-Connected PV Systems with Adaptive DC-link Controller. Arabian Journal for Science and Engineering 2021, 1 -15.

AMA Style

Mostafa Ahmed, Ibrahim Harbi, Ralph Kennel, Mohamed Abdelrahem. Dual-Mode Power Operation for Grid-Connected PV Systems with Adaptive DC-link Controller. Arabian Journal for Science and Engineering. 2021; ():1-15.

Chicago/Turabian Style

Mostafa Ahmed; Ibrahim Harbi; Ralph Kennel; Mohamed Abdelrahem. 2021. "Dual-Mode Power Operation for Grid-Connected PV Systems with Adaptive DC-link Controller." Arabian Journal for Science and Engineering , no. : 1-15.

Journal article
Published: 02 July 2021 in Electronics
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In this article, a deadbeat predictive control (DB-PC) strategy for permanent-magnet synchronous generators (PMSGs)-based modern wind turbines is proposed. The main advantages of the DB-PC technique are its excellent dynamics and its constant switching frequency. However, the main idea of DB-PC is obtaining the actuation voltage for the next sample from the mathematical model of the generator. Therefore, the DB-PC is highly sensitive to mismatches in the parameters of the PMSG. In order to obviate this problem, a disturbance estimator (extended Kalman filter (EKF)) is employed in this work to enhance the robustness of the proposed DB-PC scheme by estimating the total disturbance due to parameter mismatches and adding it to the calculation of the actuation voltage. Furthermore, the same EKF observe the rotor speed and position of the PMSG, i.e., mechanical sensors are not required. Moreover, the EKF is able to reduce the harmonic distortion in the stator currents of the PMSG. The proposed DB-PC strategy is implemented in the laboratory. The experimental results proved the superiority of the proposed DB-PC strategy over the traditional DB-PC technique.

ACS Style

Mohamed Abdelrahem; Christoph Hackl; Ralph Kennel. Robust Predictive Control Scheme for Permanent-Magnet Synchronous Generators Based Modern Wind Turbines. Electronics 2021, 10, 1596 .

AMA Style

Mohamed Abdelrahem, Christoph Hackl, Ralph Kennel. Robust Predictive Control Scheme for Permanent-Magnet Synchronous Generators Based Modern Wind Turbines. Electronics. 2021; 10 (13):1596.

Chicago/Turabian Style

Mohamed Abdelrahem; Christoph Hackl; Ralph Kennel. 2021. "Robust Predictive Control Scheme for Permanent-Magnet Synchronous Generators Based Modern Wind Turbines." Electronics 10, no. 13: 1596.

Journal article
Published: 24 June 2021 in IEEE Transactions on Industrial Electronics
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Modular multilevel converters (MMCs) have been a promising topology in medium-voltage motor drive applications over the past decades. The main challenge that hinders the widespread use of the MMC in medium-voltage motor drives is the large voltage fluctuation under low-speed conditions. In this paper, we propose a model predictive control (MPC) for the MMC operating in a wide frequency range. Unlike conventional MPC methods, a novel cost function has been designed to realize the output current control and the voltage balancing control for both low- and high-frequency operations. Compared with the high-frequency circulating current injection method, the proposed method can easily achieve a trade-off between the modulation range and the amplitude of injected common-mode voltage under different operating conditions. In addition, due to the exclusion of the switching process between the low- and high-frequency operation, the control structure of this method is more straightforward. At last, the experimental results verify the effectiveness and superiority of the proposed method.

ACS Style

Xiaonan Gao; Wei Tian; Yuebin Pang; Ralph Kennel. Model Predictive Control for Modular Multilevel Converters Operating at Wide Frequency Range with a Novel Cost Function. IEEE Transactions on Industrial Electronics 2021, PP, 1 -1.

AMA Style

Xiaonan Gao, Wei Tian, Yuebin Pang, Ralph Kennel. Model Predictive Control for Modular Multilevel Converters Operating at Wide Frequency Range with a Novel Cost Function. IEEE Transactions on Industrial Electronics. 2021; PP (99):1-1.

Chicago/Turabian Style

Xiaonan Gao; Wei Tian; Yuebin Pang; Ralph Kennel. 2021. "Model Predictive Control for Modular Multilevel Converters Operating at Wide Frequency Range with a Novel Cost Function." IEEE Transactions on Industrial Electronics PP, no. 99: 1-1.

Journal article
Published: 23 June 2021 in Sustainability
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Model predictive control (MPC) is a flexible and multivariable control technique with better dynamic performance than linear control. However, MPC is sensitive to parametric mismatches that reduce its control capabilities. In this paper, we present a new method of improving the robustness of MPC to filter parameter variations/mismatches by easily implementable parameter estimation. Furthermore, we extend the proposed technique for wider operating conditions by novel neuro-fuzzy estimation. The results, which are demonstrated by both simulations and real-time hardware-in-the-loop tests, show a steady-state parameter estimation accuracy of 95%, and at least 20% improvement in total harmonic distortion (THD) than conventional non-adaptive MPC under parameter mismatches up to 50% of the nominal values.

ACS Style

Oluleke Babayomi; Zhenbin Zhang; Yu Li; Ralph Kennel. Adaptive Predictive Control with Neuro-Fuzzy Parameter Estimation for Microgrid Grid-Forming Converters. Sustainability 2021, 13, 7038 .

AMA Style

Oluleke Babayomi, Zhenbin Zhang, Yu Li, Ralph Kennel. Adaptive Predictive Control with Neuro-Fuzzy Parameter Estimation for Microgrid Grid-Forming Converters. Sustainability. 2021; 13 (13):7038.

Chicago/Turabian Style

Oluleke Babayomi; Zhenbin Zhang; Yu Li; Ralph Kennel. 2021. "Adaptive Predictive Control with Neuro-Fuzzy Parameter Estimation for Microgrid Grid-Forming Converters." Sustainability 13, no. 13: 7038.

Journal article
Published: 18 June 2021 in Machines
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Precise frequency measurement plays an essential role in many industrial and robotic systems. However, different effects in the application’s environment cause signal noises, which make frequency measurement more difficult. In small signals or rough environments, even negative Signal-to-Noise Ratios (SNRs) are possible. Thus, frequency measuring methods, which are suited for low SNR signals, are in great demand. While denoising methods such as autocorrelation do not suffice for small signal with low SNR, frequency measurement methods such as Fast-Fourier Transformation or Continuous Wavelet Transformation suffer from Heisenberg’s uncertainty principle, which makes simultaneous high frequency and time resolutions impossible. In this paper, the cross-correlation spectrum is presented as a new frequency measuring method. It can be used in any frequency domain, and provides greater denoising than autocorrelation. Furthermore, frequency and time resolutions are independent from one another, and can be set separately by the user. In simulations, it achieves an average deviation of less than 0.1% on sinusoidal signals with a SNR of −10 dB and a signal length of 1000 data points. When applied to “self-mixing”-interferometry signals, the method can reach a normalized root-mean square error of 0.2% with the aid of an estimation method and an averaging algorithm. Therefore, further research of the method is recommended.

ACS Style

Yang Liu; Jigou Liu; Ralph Kennel. Frequency Measurement Method of Signals with Low Signal-to-Noise-Ratio Using Cross-Correlation. Machines 2021, 9, 123 .

AMA Style

Yang Liu, Jigou Liu, Ralph Kennel. Frequency Measurement Method of Signals with Low Signal-to-Noise-Ratio Using Cross-Correlation. Machines. 2021; 9 (6):123.

Chicago/Turabian Style

Yang Liu; Jigou Liu; Ralph Kennel. 2021. "Frequency Measurement Method of Signals with Low Signal-to-Noise-Ratio Using Cross-Correlation." Machines 9, no. 6: 123.

Journal article
Published: 03 June 2021 in IEEE Transactions on Industrial Electronics
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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.

ACS Style

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 Style

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 (99):1-1.

Chicago/Turabian Style

Haotian 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.

Journal article
Published: 25 May 2021 in IEEE Transactions on Power Electronics
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This paper presents a flux linkage-based direct model predictive current control approach that achieves favorable performance both during steady-state and transient operation. The former is achieved by computing the optimal time instants at which a new switch position is applied to the converter. To this end, the future current behavior is not computed based on the machine inductances or inductance look-up tables; instead, flux linkage maps are utilized to predict the trajectory of the magnetic flux linkage, and subsequently of the current. This is advantageous for electric drives with noticeable magnetic nonlinearity in terms of saturation and/or cross-coupling effects. Hence, by using flux linkage maps in the prediction process, the evolution of the stator current can be calculated more accurately, enabling the controller to make better switching decisions. Moreover, the discussed predictive controller exhibits excellent dynamic performance owing to its direct control nature, i.e., the control and modulation tasks are performed in one computational stage rendering a dedicated modulation stage redundant. Three different drive systems based on permanent magnet synchronous motors (PMSMs) are examined to demonstrate the effectiveness of the presented control approach.

ACS Style

Sebastian Wendel; Petros Karamanakos; Philipp Gebhardt; Armin Dietz; Ralph Kennel. Flux Linkage-Based Direct Model Predictive Current Control for Synchronous Machines. IEEE Transactions on Power Electronics 2021, 36, 14237 -14256.

AMA Style

Sebastian Wendel, Petros Karamanakos, Philipp Gebhardt, Armin Dietz, Ralph Kennel. Flux Linkage-Based Direct Model Predictive Current Control for Synchronous Machines. IEEE Transactions on Power Electronics. 2021; 36 (12):14237-14256.

Chicago/Turabian Style

Sebastian Wendel; Petros Karamanakos; Philipp Gebhardt; Armin Dietz; Ralph Kennel. 2021. "Flux Linkage-Based Direct Model Predictive Current Control for Synchronous Machines." IEEE Transactions on Power Electronics 36, no. 12: 14237-14256.

Journal article
Published: 01 April 2021 in IEEE Transactions on Industrial Electronics
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ACS Style

Xinyue Li; Ralph Kennel. General Formulation of Kalman-Filter-Based Online Parameter Identification Methods for VSI-Fed PMSM. IEEE Transactions on Industrial Electronics 2021, 68, 2856 -2864.

AMA Style

Xinyue Li, Ralph Kennel. General Formulation of Kalman-Filter-Based Online Parameter Identification Methods for VSI-Fed PMSM. IEEE Transactions on Industrial Electronics. 2021; 68 (4):2856-2864.

Chicago/Turabian Style

Xinyue Li; Ralph Kennel. 2021. "General Formulation of Kalman-Filter-Based Online Parameter Identification Methods for VSI-Fed PMSM." IEEE Transactions on Industrial Electronics 68, no. 4: 2856-2864.

Journal article
Published: 20 March 2021 in Energies
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This article presents a multiple-vector finite-control-set model predictive control (MV-FCS-MPC) scheme with fuzzy logic for permanent-magnet synchronous motors (PMSMs) used in electric drive systems. The proposed technique is based on discrete space vector modulation (DSVM). The converter’s real voltage vectors are utilized along with new virtual voltage vectors to form switching sequences for each sampling period in order to improve the steady-state performance. Furthermore, to obtain the reference voltage vector (VV) directly from the reference current and to reduce the calculation load of the proposed MV-FCS-MPC technique, a deadbeat function (DB) is added. Subsequently, the best real or virtual voltage vector to be applied in the next sampling instant is selected based on a certain cost function. Moreover, a fuzzy logic controller is employed in the outer loop for controlling the speed of the rotor. Accordingly, the dynamic response of the speed is improved and the difficulty of the proportional-integral (PI) controller tuning is avoided. The response of the suggested technique is verified by simulation results and compared with that of the conventional FCS-MPC.

ACS Style

Ibrahim Bouguenna; Ahmed Tahour; Ralph Kennel; Mohamed Abdelrahem. Multiple-Vector Model Predictive Control with Fuzzy Logic for PMSM Electric Drive Systems. Energies 2021, 14, 1727 .

AMA Style

Ibrahim Bouguenna, Ahmed Tahour, Ralph Kennel, Mohamed Abdelrahem. Multiple-Vector Model Predictive Control with Fuzzy Logic for PMSM Electric Drive Systems. Energies. 2021; 14 (6):1727.

Chicago/Turabian Style

Ibrahim Bouguenna; Ahmed Tahour; Ralph Kennel; Mohamed Abdelrahem. 2021. "Multiple-Vector Model Predictive Control with Fuzzy Logic for PMSM Electric Drive Systems." Energies 14, no. 6: 1727.

Journal article
Published: 03 March 2021 in IEEE Transactions on Industrial Electronics
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A predictive current controller is established based on the system model and therefore suffers from the problems such as the parameter mismatches, the digital delay and the external disturbances. In order to tackle these problems, an observer-based robust predictive current control strategy is proposed in this paper, which employs an extended state observer to estimate the disturbances, currents and applies the augmented system model for the design of the controller. It is generally formulated and applicable to any AC motor drive system, as long as the prerequisites are satisfied. Furthermore, the proposed control scheme is proven to be input-to-state stable with a proper design of the controller and the observer. The effectiveness of the proposed control strategy is moreover verified with numerous experiments on a dSPACE system under various test scenarios. The comparative studies between the proposed method and the predictive current control approaches, i.e. the conventional deadbeat control and the continuous control set model predictive control, are conducted at different operating conditions.

ACS Style

Xinyue Li; Wei Tian; Xiaonan Gao; Qifan Yang; Ralph Kennel. A Generalized Observer-based Robust Predictive Current Control Strategy for PMSM Drive System. IEEE Transactions on Industrial Electronics 2021, PP, 1 -1.

AMA Style

Xinyue Li, Wei Tian, Xiaonan Gao, Qifan Yang, Ralph Kennel. A Generalized Observer-based Robust Predictive Current Control Strategy for PMSM Drive System. IEEE Transactions on Industrial Electronics. 2021; PP (99):1-1.

Chicago/Turabian Style

Xinyue Li; Wei Tian; Xiaonan Gao; Qifan Yang; Ralph Kennel. 2021. "A Generalized Observer-based Robust Predictive Current Control Strategy for PMSM Drive System." IEEE Transactions on Industrial Electronics PP, no. 99: 1-1.

Original paper
Published: 24 February 2021 in Electrical Engineering
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Sensorless strategies become very popular in modern control techniques because they increase the system reliability. Besides, they can be used as back-up control in case of sensor failure. In this paper, a DC-link sensorless control approach is developed, which is suited for grid-connected PV systems. The studied system is a two-stage PV scheme, where the DC–DC stage (boost converter) is controlled using an adaptive step-size perturb and observe (P&O) method. Further, the inverter control is accomplished by voltage oriented control (VOC). Generally, the VOC is implemented with two cascaded control loops, namely an outer voltage loop and an inner current loop. However, in this work, the outer loop is avoided and the reference current is generated using a losses model for the system. The losses model accounts for the most significant losses in the PV system. Moreover, particle swarm optimization (PSO) is utilized to compensate for the unmodeled losses. The PSO is executed offline for the purpose of calculation burden reduction. The proposed approach simplifies the cascaded VOC strategy and eliminates the DC-link voltage sensor, which in turn decreases the cost of the system. Finally, the proposed technique is compared with the conventional one at different atmospheric conditions and validated using MATLAB simulation results.

ACS Style

Mostafa Ahmed; Mohamed Abdelrahem; Ahmed Farhan; Ibrahim Harbi; Ralph Kennel. DC-link sensorless control strategy for grid-connected PV systems. Electrical Engineering 2021, 1 -11.

AMA Style

Mostafa Ahmed, Mohamed Abdelrahem, Ahmed Farhan, Ibrahim Harbi, Ralph Kennel. DC-link sensorless control strategy for grid-connected PV systems. Electrical Engineering. 2021; ():1-11.

Chicago/Turabian Style

Mostafa Ahmed; Mohamed Abdelrahem; Ahmed Farhan; Ibrahim Harbi; Ralph Kennel. 2021. "DC-link sensorless control strategy for grid-connected PV systems." Electrical Engineering , no. : 1-11.

Journal article
Published: 20 January 2021 in IEEE Transactions on Energy Conversion
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Conventionally, the converter of switched reluctance motor (SRM) is fed by the uncontrollable diode bridge rectifier (DBR), which leads to a low grid-side power factor (PF) and high current total harmonic distortion (THD). In this paper, an alternative solution for the grid-connected high-speed SRM drive system with improved PF is proposed. In the proposed drive system, the three-level active front end (AFE) is connected in cascade with the midpoint converter for SRM operation. A centralized strategy, which controls the AFE and SRM together, is proposed to govern the motor speed and grid-side PF by regulating the real power and reactive power of the system, respectively. Specifically, the real power, reactive power, and the voltage balancing of split capacitors are controlled by the model predictive directed power control (MP-DPC) algorithm, which significantly reduces the control complexity and guarantees the fast dynamic response. Consequently, satisfying speed regulation, high PF, low current THD, and bi-directional power-transfer capability are achieved. An idea-proofed testbench is constructed in laboratory, and the applicability of the proposed drive system is verified by a series of experimental results.

ACS Style

Ying Tang; Yingjie He; Fengxiang Wang; Guiying Lin; Jose Rodriguez; Ralph Kennel. A Centralized Control Strategy for Grid-Connected High-Speed Switched Reluctance Motor Drive System With Power Factor Correction. IEEE Transactions on Energy Conversion 2021, 36, 2163 -2172.

AMA Style

Ying Tang, Yingjie He, Fengxiang Wang, Guiying Lin, Jose Rodriguez, Ralph Kennel. A Centralized Control Strategy for Grid-Connected High-Speed Switched Reluctance Motor Drive System With Power Factor Correction. IEEE Transactions on Energy Conversion. 2021; 36 (3):2163-2172.

Chicago/Turabian Style

Ying Tang; Yingjie He; Fengxiang Wang; Guiying Lin; Jose Rodriguez; Ralph Kennel. 2021. "A Centralized Control Strategy for Grid-Connected High-Speed Switched Reluctance Motor Drive System With Power Factor Correction." IEEE Transactions on Energy Conversion 36, no. 3: 2163-2172.

Journal article
Published: 17 December 2020 in Energies
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In this article, a modified control structure for a single-stage three phase grid-connected photovoltaic (PV) system is presented. In the proposed system, the maximum power point tracking (MPPT) function is developed using a new adaptive model-based technique, in which the maximum power point (MPP) voltage can be precisely located based on the characteristics of the PV source. By doing so, the drift problem associated with the traditional perturb and observe (P&O) technique can be easily solved. Moreover, the inverter control is accomplished using a predictive dead-beat function, which directly estimates the required reference voltages from the commanded reference currents. Then, the reference voltages are applied to a space vector pulse width modulator (SVPWM) for switching state generation. Furthermore, the proposed inverter control avoids the conventional and known cascaded loop structure of the voltage oriented control (VOC) method by elimination of the outer PI controller, and hence the overall control strategy is simplified. The proposed system is compared with different MPPT techniques, including the conventional P&O method and other techniques intended for drift avoidance. The evaluation of the suggested control methodology depends on various radiation profiles created in MATLAB. The proposed technique succeeds at capturing the maximum available power from the PV source with no drift in comparison with other methods.

ACS Style

Mostafa Ahmed; Mohamed Abdelrahem; Ibrahim Harbi; Ralph Kennel. An Adaptive Model-Based MPPT Technique with Drift-Avoidance for Grid-Connected PV Systems. Energies 2020, 13, 6656 .

AMA Style

Mostafa Ahmed, Mohamed Abdelrahem, Ibrahim Harbi, Ralph Kennel. An Adaptive Model-Based MPPT Technique with Drift-Avoidance for Grid-Connected PV Systems. Energies. 2020; 13 (24):6656.

Chicago/Turabian Style

Mostafa Ahmed; Mohamed Abdelrahem; Ibrahim Harbi; Ralph Kennel. 2020. "An Adaptive Model-Based MPPT Technique with Drift-Avoidance for Grid-Connected PV Systems." Energies 13, no. 24: 6656.

Letter
Published: 12 November 2020 in Machines
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Operations of the doubly-fed induction generators (DFIGs) without mechanical sensors are highly desirable in order to enhance the reliability of the wind generation systems. This article proposes a limited-position set model-reference adaptive observer (LPS-MRAO) for control of DFIGs in wind turbine systems (WTSs) without mechanical sensors, i.e., without incremental encoders or speed transducers. The concept of of the developed LPS-MRAO is obtained from the finite-set model predictive control (FS-MPC). In the proposed LPS-MRAO, an algorithm is presented in order to give a constant number of angles for the rotor position of the DFIG. By using these angles, a certain number of rotor currents can be predicted. Then, a new quality function is defined to find the best angle of the rotor. In the proposed LPS-MRAO, there are not any gains to tune like the classical MRAO, where a proportional-integral is used and must be tuned. Finally, the proposed LPS-MRAO and classical one are experimentally implemented in the laboratory and compared at various operation scenarios and under mismatches in the parameters of the DFIG. The experimental results illustrated that the estimation performance and robustness of the proposed LPS-MRAO are better than those of the classical one.

ACS Style

Mohamed Abdelrahem; Christoph M. Hackl; Ralph Kennel. Limited-Position Set Model-Reference Adaptive Observer for Control of DFIGs without Mechanical Sensors. Machines 2020, 8, 72 .

AMA Style

Mohamed Abdelrahem, Christoph M. Hackl, Ralph Kennel. Limited-Position Set Model-Reference Adaptive Observer for Control of DFIGs without Mechanical Sensors. Machines. 2020; 8 (4):72.

Chicago/Turabian Style

Mohamed Abdelrahem; Christoph M. Hackl; Ralph Kennel. 2020. "Limited-Position Set Model-Reference Adaptive Observer for Control of DFIGs without Mechanical Sensors." Machines 8, no. 4: 72.

Journal article
Published: 06 November 2020 in Applied Sciences
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Voltage models of lithium-ion batteries (LIB) are used to estimate their future voltages, based on the assumption of a specific current profile, in order to ensure that the LIB remains in a safe operation mode. Data of measurable physical features—current, voltage and temperature—are processed using both over- and undersampling methods, in order to obtain evenly distributed and, therefore, appropriate data to train the model. The trained recurrent neural network (RNN) consists of two long short-term memory (LSTM) layers and one dense layer. Validation measurements over a wide power and temperature range are carried out on a test bench, resulting in a mean absolute error (MAE) of 0.43 V and a mean squared error (MSE) of 0.40 V2. The raw data and modeling process can be carried out without any prior knowledge of LIBs or the tested battery. Due to the challenges involved in modeling the state-of-charge (SOC), measurements are used directly to model the behavior without taking the SOC estimation as an input feature or calculating it in an intermediate step.

ACS Style

Daniel Jerouschek; Ömer Tan; Ralph Kennel; Ahmet Taskiran. Data Preparation and Training Methodology for Modeling Lithium-Ion Batteries Using a Long Short-Term Memory Neural Network for Mild-Hybrid Vehicle Applications. Applied Sciences 2020, 10, 7880 .

AMA Style

Daniel Jerouschek, Ömer Tan, Ralph Kennel, Ahmet Taskiran. Data Preparation and Training Methodology for Modeling Lithium-Ion Batteries Using a Long Short-Term Memory Neural Network for Mild-Hybrid Vehicle Applications. Applied Sciences. 2020; 10 (21):7880.

Chicago/Turabian Style

Daniel Jerouschek; Ömer Tan; Ralph Kennel; Ahmet Taskiran. 2020. "Data Preparation and Training Methodology for Modeling Lithium-Ion Batteries Using a Long Short-Term Memory Neural Network for Mild-Hybrid Vehicle Applications." Applied Sciences 10, no. 21: 7880.

Journal article
Published: 30 September 2020 in IEEE Transactions on Power Electronics
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This paper presents a compensation network with multiple resonating frequencies for multiple-pickup wireless power transfer (WPT) systems, which aims to establish multiple channels to deliver the power to multiple loads simultaneously. In previous studies, a number of efforts have been made to realize the multichannel transmission by utilizing multiple transmitting coils or transformer to deliver power at different frequencies, which inevitably results in the extra power loss. Although loads can be energized simultaneously, the transmission performance and the maintainability of WPT systems are both deteriorated significantly. In order to address the issue, this paper proposes and implements the multiple-frequency resonating compensation (MFRC) network, which can offer WPT systems with various resonating frequencies. By adopting the power supply with multiple-frequency components, the power can be simultaneously transmitted to loads at corresponding frequencies while avoiding the utilization of transformer or extra transmitting coils. As a result, the proposed MFRC-based multichannel transmission scheme can effectively reduce the power loss and the complexity, as well as increase the transmission efficiency of multiple-pickup WPT systems. In this paper, simulated and experimental results are both given to verify the feasibility of the proposed MFRC network for the multichannel transmission of multiple-pickup WPT systems.

ACS Style

Zhen Zhang; Xingyu Li; Hongliang Pang; Hasan Komurcugil; Zhenyan Liang; Ralph Kennel. Multiple-Frequency Resonating Compensation for Multichannel Transmission of Wireless Power Transfer. IEEE Transactions on Power Electronics 2020, 36, 5169 -5180.

AMA Style

Zhen Zhang, Xingyu Li, Hongliang Pang, Hasan Komurcugil, Zhenyan Liang, Ralph Kennel. Multiple-Frequency Resonating Compensation for Multichannel Transmission of Wireless Power Transfer. IEEE Transactions on Power Electronics. 2020; 36 (5):5169-5180.

Chicago/Turabian Style

Zhen Zhang; Xingyu Li; Hongliang Pang; Hasan Komurcugil; Zhenyan Liang; Ralph Kennel. 2020. "Multiple-Frequency Resonating Compensation for Multichannel Transmission of Wireless Power Transfer." IEEE Transactions on Power Electronics 36, no. 5: 5169-5180.

Journal article
Published: 27 September 2020 in Sustainability
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This paper proposes a finite control set model predictive control (FCS-MPC) with a reduced computational burden for a single-phase grid-connected modified packed U-cell multilevel inverter (MPUC-MLI) with two control objectives: reference current tracking and switching frequency minimization. The considered competitive topology consists of two units with six active switches and two DC sources in each unit, allowing the generation of 49 levels in the output voltage, which is considered a significant reduction in the active and passive components compared to the conventional and recently developed topologies of multilevel inverters (MLIs). This topology has 49 different switching states, which means that 49 predictions of the future current and 49 calculations of the cost function are required for each evaluation of the conventional FCS-MPC. Accordingly, the computational load is heavy. Thus, this paper presents two reduced-complexity FCS-MPC methods to reduce the calculation burden. The first technique reduces the computational load almost to half by computing the reference voltage and dividing the states of the MLI into two sets. Based on the reference voltage polarity, one set is defined and evaluated to specify the optimal state, which has a minimal cost function. However, in the second proposed method, only three states of the 49 states are evaluated each iteration, achieving a significant reduction in the execution time and superior control performance compared to the conventional FCS-MPC. A mathematical analysis is conducted based on the reference voltage value to locate the three vectors under evaluation. In the second part of the paper, the sensitivity to parameter variations for the proposed simplified FCS-MPC is investigated and tackled by employing an extended Kalman filter (EKF). In addition, noise related to variable measurement is filtered in the proposed system with the EKF. The simulation investigation was performed using MATLAB/Simulink to validate the system under different operating conditions.

ACS Style

Ibrahim Harbi; Mohamed Abdelrahem; Mostafa Ahmed; Ralph Kennel. Reduced-Complexity Model Predictive Control with Online Parameter Assessment for a Grid-Connected Single-Phase Multilevel Inverter. Sustainability 2020, 12, 7997 .

AMA Style

Ibrahim Harbi, Mohamed Abdelrahem, Mostafa Ahmed, Ralph Kennel. Reduced-Complexity Model Predictive Control with Online Parameter Assessment for a Grid-Connected Single-Phase Multilevel Inverter. Sustainability. 2020; 12 (19):7997.

Chicago/Turabian Style

Ibrahim Harbi; Mohamed Abdelrahem; Mostafa Ahmed; Ralph Kennel. 2020. "Reduced-Complexity Model Predictive Control with Online Parameter Assessment for a Grid-Connected Single-Phase Multilevel Inverter." Sustainability 12, no. 19: 7997.

Journal article
Published: 16 September 2020 in Energies
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In micro-grid systems, wind turbines are essential power generation sources. The direct-driven surface-mounted permanent-magnet synchronous generators (SMPMSGs) in variable-speed wind generation systems (VS-WGSs) are promising due to their high efficiency/power density and the avoidance of using a gearbox, i.e., regular maintenance and noise are averted. Usually, the main goal of the control system for SMPMSGs is to extract the maximum available power from the wind turbine. To do so, the rotor position/speed of the SMPMSG must be known. Those signals are obtained by the help of an incremental encoder or speed transducer. However, the system reliability is remarkably reduced due to the high failure rate of these mechanical sensors. To avoid this problem, this paper presents a model reference adaptive system with finite-set (MRAS-FS) observer for encoderless control of SMPMSGs in VS-WGSs. The motif of the presented MRAS-FS observer is taken from the direct-model predictive control (DMPC) principle, where a certain number of rotor position angles are utilized to estimate the stator flux of the SMPMSG. Subsequently, a new optimization criterion (also called quality or cost function) is formulated to select the best rotor position angle based on minimizing the error between the estimated and reference value of the stator flux. Accordingly, the traditional fixed-gain proportional-integral regulator generally employed in the classical MRAS observers is not needed. The proposed MRAS-FS observer is validated experimentally, and its estimation response has been compared with the conventional MRAS observer under different conditions. In addition to that, the robustness of the MRAS-FS observer is tested at mismatches in the parameters of the SMPMSG.

ACS Style

Mohamed Abdelrahem; Christoph M. Hackl; José Rodríguez; Ralph Kennel. Model Reference Adaptive System with Finite-Set for Encoderless Control of PMSGs in Micro-Grid Systems. Energies 2020, 13, 4844 .

AMA Style

Mohamed Abdelrahem, Christoph M. Hackl, José Rodríguez, Ralph Kennel. Model Reference Adaptive System with Finite-Set for Encoderless Control of PMSGs in Micro-Grid Systems. Energies. 2020; 13 (18):4844.

Chicago/Turabian Style

Mohamed Abdelrahem; Christoph M. Hackl; José Rodríguez; Ralph Kennel. 2020. "Model Reference Adaptive System with Finite-Set for Encoderless Control of PMSGs in Micro-Grid Systems." Energies 13, no. 18: 4844.