Finite Control Set Model Predictive Current Control Research Articles

Techno-enviro-socio-economic design and finite set model predictive current control of a grid-connected large-scale hybrid solar/wind energy system: A case study of Sokhna Industrial Zone, Egypt

This article offers a cohesive design optimization and control framework of a large-scale grid-connected battery and battery-less hybrid solar/wind system. Primarily, a techno-enviro-socio-economic design optimization and feasibility analysis were performed for eight distinct energy alternatives. Secondly, a finite-set model predictive current control (FS-MPCC) was proposed to improve both stability and dynamic behavior of the optimal energy alternative under various climatic circumstances. Moreover, finite-set model predictive control is furtherly employed to exploit the maximum permissible power from both solar and wind energies. The proposed framework is verified over a new case-study in Sokhna Industrial Zone in Suez, Egypt. The studied load demand combines both industrial and residential load sectors with a daily consumption of 40,500 kWh and of 4-MW peak. The design optimization results revealed that the grid-connected photovoltaic/wind turbines/converter system has superior sustainable and economic performances with the least net present cost ($17,371,980) and energy cost (0.0782 $/kWh). These values were less than those of the base-case system (grid-only) by 17 % and 22 %, respectively. Moreover, the reliability analysis found a tiny and tolerable daily capacity shortage of 5835-kWh despite the five annual grid outages. Besides, around 52 % of the total energy production was acquired from both solar and wind generation systems, which interpreted in a maximum saving in carbon dioxide emissions by 45.8 % compared to the base-case system and offered substantial social benefits in terms of creating nearly 10 jobs. The FS-MPCC results proved a reinforced dynamic behavior for the maximum power tracking profiles of solar and wind systems as well as in the DC-bus voltage profile regarding the over- and undershoot and the steady-state response. Compared to PI controller, the proposed FS-MPCC creates dq-axis voltages and currents with less disturbances and ripples which inject current/voltage to the grid with fewer harmonics and thus increases the generator's lifetime. To sum up, the proposed method features an integrative and compelling case to be used as an efficient tool to implement large-scale renewable energy projects. It could act as a blueprint for energy developers, researchers, investors, and authorities to achieve affordable and sustainable energy access.

FPGA-Based Extended Control Set Model Predictive Current Control with a Simplified Search Strategy for Permanent Magnet Synchronous Motor

The conventional finite control set model predictive current control (FCS-MPCC) suffers from suboptimal steady-state performance, primarily due to the limited selection of only eight basic voltage vectors in each control cycle. To overcome this limitation, the proposed extended control set MPCC (ECS-MPCC) utilizes an control set consisting of 818 selectable vectors, enabling a more refined voltage output and achieving a deadbeat response for current control by minimizing the cost function. To mitigate the computational burden resulting from the substantial increase in voltage vectors, a simplified search strategy is devised, which can be extended to other multi-objective cost functions. Remarkably, based on the inherent parallelism of the algorithm, the ECS-MPCC is implemented on an FPGA, further reducing the overall control time of the current loop to an impressive 0.61 μs. Through simulation and experimental tests on a laboratory PMSM driver, the effectiveness of the proposed ECS-MPCC strategy is validated. The experimental results demonstrate a significant reduction of 79% in the total harmonic distortion of phase currents compared to the conventional FCS-MPCC approach. This improvement underscores the superiority of the ECS-MPCC in enhancing the performance of PMSM drives, thereby illustrating its potential for practical implementation in real-world applications.

Optimized FCS-MPCC based on disturbance feedback rejection for IPMSMs under demagnetization fault in high-speed trains

This paper proposes an optimized finite control set model predictive current control (FCS-MPCC) strategy based on disturbance feedback rejection control (DFRC) method for interior permanent magnet synchronous motors (IPMSM) employed in high-speed trains. The strategy aims to mitigate the impact of parameter mismatch caused by permanent magnet demagnetization faults. Firstly, a discrete predicting model is established for IPMSMs, considering the mismatch in flux linkage, resistance, and inductance. The analysis is conducted to evaluate the impact of disturbances caused by demagnetization faults on the traditional FCS-MPCC. Secondly, second-order sliding mode disturbances observers (SMDOs) are utilized to detect real-time disturbances under demagnetization faults. The stability analysis of the second-order SMDOs is conducted using a Lyapunov function. Furthermore, disturbance controllers are developed to regulate the disturbances and generate compensating values for the FCS-MPCC. Lastly, experimental validation is performed on two IPMSMs to demonstrate the effectiveness of the proposed DFRC-based FCS-MPCC strategy against parameter mismatch issues caused by demagnetization faults.

Adaptive Integral Extended State Observer-Based Improved Multistep FCS-MPCC for PMSM

In this article, an improved multi-step finite control set model predictive current control (FCS-MPCC) based on adaptive integral extended state observer (AIESO) is proposed for permanent magnet synchronous motor (PMSM). The improved multi-step FCS-MPCC based on sector is introduced in the current loop. The elements of the voltage vector set are reduced with different sector division method, so it could reduce the computational burden to some extent. Meanwhile, considering that the high gain extended state observer (ESO) will obtain faster convergence, better tracking accuracy in theory and the system disturbance rejection can be enhanced, but the noise suppression performance will become poor. Thus, the adaptive extended state observer (AESO) is proposed to balance the disturbance rejection and noise suppression. When system subject to disturbance, the adaptive gain will increase to enhance the system disturbance rejection and become small to improve the noise suppression in steady-state. However, disturbed by time-varying disturbance, the small gain in the steady state will lead to poor steady-state tracking accuracy. To improve the steady-state tracking accuracy, AIESO is proposed by adding the integral term into AESO. Finally, in order to avoid the sector misjudgment caused by parameter mismatch, the strategy based on extended state observer is used for disturbance compensation. However the selection of inductance is closed related to the estimated burden of the observer. Thus, the inductance parameter estimation method is proposed to update the initial inductance value to reduce the estimated burden, which can help to suppress the parameter mismatches with a smaller estimated burden of the observer. The simulation and experimental results verify the effectiveness of the proposed method.

Improved ROGI-FLL-Based Sensorless Model Predictive Current Control With MRAS Parameter Identification for SPMSM Drives

This paper presents a sensorless finite control set model predictive current control (FCS-MPCC) for surface-mounted permanent magnet synchronous motor drives based on an improved reduced-order generalized integrator-frequency-locked loop (IROGI-FLL). To simplify the structure of the sensorless FCS-MPCC and avoid control performance degradation caused by the rotor position estimation error, an instantaneous power theory-based given current calculation method is designed. Accordingly, the whole algorithm can be realized without using rotor position. IROGI-FLL is adopted for filtering the estimated back electromotive force and estimating motor speed. To enhance its dynamic speed tracking performance, a speed tracking deviation compensation term is designed for FLL. With the feedforward compensation term, speed static tracking deviation in acceleration and deceleration states can be eliminated. Moreover, to avoid the adverse effect of parameter mismatch on control performance, an incremental adjustable model-based model reference adaptive system (MRAS) is introduced to identify the resistance and inductance parameters. The incremental adjustable model can avoid identification error caused by the rank deficiency issue. Finally, the effectiveness of the proposed method is investigated by experiments.

Flux Observation Transfer-Based Inductance Identification Technique for Precise FCS-MPCC Used in Surface-Mounted PMSMs

To eliminate the side impacts of inductance mismatch on the accuracy of finite control set model predictive current control (FCS-MPCC) scheme used for surface-mounted permanent magnet synchronous motors (SPMSM), this paper proposes a new flux observation transfer-based inductance identification strategy. First, a flux observer based on sliding mode theory is developed, with its stability analyzed. Second, the flux estimation errors are discussed, obtaining the relationship between the estimated flux and the inductance variations. On this basis, the inductance is detected using the flux information. Then, the estimated inductance is used to achieve the precise FCS-MPCC method. Finally, experiment is conducted on a three-phase SPMSM prototype to validate the proposed techniques.

Full prediction cascade control of permanent magnet toroidal motor with time-varying parameters

This paper presents the modeling and predictive control of permanent magnet toroidal motor (PMTM). Based on the spatial structure characteristics and working principle of toroidal motor, the time-varying inductance parameters are derived. The nonlinear mathematical model of toroidal motor is established in rotating coordinate system. The output fluctuation of the toroidal motor is analyzed. To improve the output performance of toroidal motor, the full predictive cascade speed and current control (F-MPC) strategy for toroidal motor is proposed. For F-MPC of toroidal motor, the finite control set model predictive current control (FCS-MPCC) strategy with delay compensation is adopted in current inner loop, and the deadbeat model predictive speed control (DB-MPSC) strategy is adopted in speed outer loop. Meanwhile, to achieve DB-MPSC, the reduced-order Luenberger observer is designed to obtain load torque in real time. F-MPC is the combination of current-speed prediction and Luenberger observer; it can overcome the limitation of excessive parameter adjustment of traditional cascade PI (Proportional Integral) controller. The simulation results show that compared with FOC (Field Oriented Control) and FCS-MPCC, F-MPC can improve the dynamic response and anti-interference performance of toroidal motor, and it can also reduce the fluctuation of output speed and torque significantly.

Low-Cost Multistep FCS-MPCC for PMSM Drives Using a DC Link Single Current Sensor

In this article, a low-cost multistep finite control set model predictive current control (FCS-MPCC) using a dc link single current sensor is proposed for permanent magnet synchronous motor (PMSM). In order to reduce the system cost, the single dc link current sensor is used to reconstruct the three-phase current. Different from the current reconstruction technology of field-orientated control strategy, there are some problems of the existed current reconstruction technology applied for finite control set model predictive control (FCS-MPC). First, the optimal switching state of FCS-MPC in each cycle is not fixed, and only contains at most one phase current information for reconstruction, which will cause the reconstruction failure. Therefore, a multiperiod sampling method is proposed to obtain the different two-phase current information to reconstruction. Second, the uncertainty of the two-phase reconstruction current interval will lead to current error, so this article proposes a variable period compensation method to solve this problem. Moreover, in order to improve the system closed-loop performance without significantly increasing the computational cost and enhance the system reliability, a low-cost multistep FCS-MPCC strategy based on the reconstructed three-phase current is proposed for PMSM. Finally, simulation and experimental results validate the effectiveness of the low-cost multistep FCS-MPCC method using a dc link single current sensor reconstruction for PMSM.

Voltage Vector Redundancy Exploitation for Battery Balancing in Three-Phase CHB-Based Modular Energy Storage Systems

This article presents a flexible controller algorithm for bidirectional modular cascaded H-bridge (CHB)-based battery storage systems. The primary objectives are to maintain high-quality power on the ac side while balancing dc-side battery voltages under charging or discharging operations. The proposed algorithm is based on a finite-set model-predictive current control (FSMPC) technique, which performs in-phase and phase voltage equalization in two complementary balancing terms by taking advantage of intrinsic voltage vector redundancy characteristic of three-phase CHB converters. Additionally, the proposed modifications to the FSMPC provide lower demand on computational resources of the controller through offline reordering of vectors and adjacent vector introduction, which also brings the least differential-mode <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$dv/dt$</tex-math></inline-formula> as an added value. Effects of battery voltage variation in charge/discharge cycles and voltage drop caused by nonideal semiconductors are considered to ensure the most accurate performance during steady-state, transient, and common-mode voltage reduction operation. The proposed approach is an efficient easy-to-implement multiparameter controller, which handles all the tasks needless to any additional control loops. Simulations and experimental prototyping results validate the feasibility of the battery voltage balancer terms to be utilized in battery storage applications with no downside impact on key power exchange functions.

Integral Sliding Mode Observer-Based Ultralocal Model for Finite-Set Model Predictive Current Control of Induction Motor

The ultralocal model of a plant expresses the behavior of the output variable in relation to the input without requiring any specific information about the plant. Replacing the conventional model of the system with an ultralocal model can effectively improve the robustness of the model predictive control (MPC) because all types of uncertainties that correlate with the system modeling are removed in the ultralocal model. This article proposes an integral sliding mode observer (ISMO)-based ultralocal model for the finite-set model predictive current control (FS-MPCC) of an induction motor (IM). The ISMO is constructed based on the Lyapunov theory to guarantee the stability of the proposed control method. Moreover, an analytical comparison is made between the proposed ISMO and the conventional extended state observer (ESO) in the application of the ultralocal model. This comparison shows that the stability of ESO depends on the unknown term of the ultralocal model. In contrast, the stability of the proposed ISMO does not rely on any terms of the ultralocal model. Experimental tests confirm that the proposed FS-MPCC method is practically applicable to the IM drive system. Also, it improves the robustness of the predictive method against model uncertainty.

Improved super-twisting-observer-based finite-control-set model-predictive fault-tolerant current control of PMSM considering demagnetization fault

Temperature and other factors will cause the permanent-magnet flux to demagnetize. The demagnetization fault inevitably results in the prediction error since the finite-control-set model-predictive current control (FCS-MPCC) method is sensitive to the parameter variation of permanent magnet synchronous motor (PMSM). This paper presents a improved super-twisting-observer-based FCS model-predictive fault-tolerant current control (ISTO-based FCS-MPFTCC) method to compensate for the prediction error caused by the demagnetization fault. The unified PMSM model of healthy and demagnetized is first derived. Next, a improved super-twisting-observer (ISTO) is designed to detect permanent-magnet flux. The estimated permanent-magnet flux updated the prediction model. Thus, the presented method performs fault-tolerant current-control of PMSM on the demagnetization fault. It eliminates the influence caused by the demagnetization fault. The experiment results validate the effectiveness of the presented ISTO-based FCS-FCS-MPFTCC method.

An Improved Finite Control Set Model Predictive Current Control for a Two-Phase Hybrid Stepper Motor Fed by a Three-Phase VSI

In this paper, an improved finite control set model predictive current control (FCS-MPCC) is proposed for a two-phase hybrid stepper motor fed by a three-phase voltage source inverter (VSI). The conventional FCS-MPCC selects an optimal voltage vector (VV) from six active and one null VVs by evaluating a simple cost function and then applies the optimal VV directly to the VSI. Though the implementation is simple, it features a large current ripple and total harmonic distortion (THD). The proposed improved FCS-MPCC builds an extended control set consisting of 37 VVs to replace the original control set with only seven VVs. The increase in the amount of VVs helps to regulate the current more accurately. In each control period, the improved FCS-MPCC takes advantage of deadbeat control to calculate a reference VV, and only the three VVs adjacent to the reference VV are predicted and evaluated, which decrease the computational workload significantly. Build waveform patterns for all VVs in the unbalanced circuit structure to modulate the optimal VV using discrete space vector modulation, which improves the current quality in reducing current ripple and THD. The comparative simulations and experimental results validate the effectiveness of the proposed method.

Finite Control Set Model-Free Predictive Current Control of a Permanent Magnet Synchronous Motor

In this paper, a finite control set model-free predictive current control (FCS-MFPCC) of a permanent magnet synchronous motor is presented. The control scheme addresses the problems of large current fluctuation and decline of the motor system performance during parameter perturbation for the traditional finite control set model predictive current control (FCS-MPCC). Firstly, the mathematical model of the motor is analyzed and derived during parameter perturbation, and a new hyperlocal model of the motor is established based on this mathematical model. Secondly, a finite control set model-free predictive current controller is designed based on the new hyperlocal model, and a current error correction factor is introduced to correct the prediction error. Meanwhile, the stability of the observer is demonstrated via the Lyapunov theory. The simulation results show that the proposed control strategy reduces current fluctuation compared with the FCS-MPCC strategy, and the system is robust during parameter perturbation.

Robust Deadbeat Finite-Set Predictive Current Control With Torque Oscillation and Noise Reduction for PMSM Drives

This article proposes a unique control strategy for a deadbeat multiple vector finite-set model predictive current control with an embedded integral action (MV-FMPC) for permanent magnet synchronous motor drives. Torque ripple and phase current distortions in permanent magnet synchronous motor (PMSM) drives are minimized with the proposed controller by adopting the multiple vector approach to the finite-set model predictive control. The controller uses a hexagonal co-ordinate system to simplify the location and identification of the virtual vectors created, thereby eliminating the use of large look-up tables and reducing computational burden. When used with the proposed deadbeat prediction model, the overall steady-state performance, system robustness, and quality of disturbance rejection are improved compared to the state-of-the-art finite-set model predictive current control (FS-MPC) methods with pulsewidth modulation. The improvements are due to the modified deadbeat prediction model with integral action, the algorithm used for multiple virtual voltage identification and the retention of the cost function in the proposed method. The proposed deadbeat MV-FMPC method and its improvements over the conventional FS-MPC have been verified through simulation and experiments with an interior-type permanent magnet synchronous machine.

Finite‐control‐set model prediction current control of five‐phase permanent magnet synchronous motor based on eight‐switch fault‐tolerant inverter

The existence of unbalanced voltage and current harmonics is one of the most pressing issues when the failure happens on the inverter, which will render the inverter less reliable or even failed. Therefore, an improved finite-control-set model predictive current control (FCS-MPCC) is proposed for eight-switch fault-tolerant inverter (ESFTI)-driven five-phase permanent magnet synchronous motor (FPPMSM). First, the topological structure of the whole system and the mathematical model of FPPMSM are presented. Second, the unbalance voltage and the predictable current vector of the controlled system are analysed in terms of FCS-MPCC. After that, the information of the corresponding control voltage vector is obtained by utilizing the deadbeat current control, which realizes the partial vector enumeration in the ideal voltage vector plane. Furthermore, the concept of two-step forward prediction is integrated to induce the manipulated variables at next sampling time such that the time delay may be alleviated, whose corresponding minimum cost function is constructed by extra considering the fluctuation of the capacitor voltages. Finally, the comparative simulation studies between the traditional FCS-MPCC and the proposed one are carried out to demonstrate the validity and effectiveness of the proposed method.

Model predictive control of grid-connected PV power generation system considering optimal MPPT control of PV modules

Because of system constraints caused by the external environment and grid faults, the conventional maximum power point tracking (MPPT) and inverter control methods of a PV power generation system cannot achieve optimal power output. They can also lead to misjudgments and poor dynamic performance. To address these issues, this paper proposes a new MPPT method of PV modules based on model predictive control (MPC) and a finite control set model predictive current control (FCS-MPCC) of an inverter. Using the identification model of PV arrays, the module-based MPC controller is designed, and maximum output power is achieved by coordinating the optimal combination of spectral wavelength and module temperature. An FCS-MPCC algorithm is then designed to predict the inverter current under different voltage vectors, the optimal voltage vector is selected according to the optimal value function, and the corresponding optimal switching state is applied to power semiconductor devices of the inverter. The MPPT performance of the MPC controller and the responses of the inverter under different constraints are verified, and the steady-state and dynamic control effects of the inverter using FCS-MPCC are compared with the traditional feedforward decoupling PI control in Matlab/Simulink. The results show that MPC has better tracking performance under constraints, and the system has faster and more accurate dynamic response and flexibility than conventional PI control.

An Improved Finite-Control-Set Model Predictive Current Control for IPMSM under Model Parameter Mismatches

The control performance of the finite control set model predictive current control (FCS-MPCC) for the interior permanent magnet synchronous machine (IPMSM) depends on the accuracy of the mathematical model. A novel robust model predictive current control method based on error compensation is proposed in order to reduce the parameter sensitivity and improve the current control robustness. In this method, the equivalent parameters are obtained from the known voltage and current information at the past time and the error between the predicted current and the actual current at the present time, which is utilized in the two-step prediction process to compensate the parameter mismatch error. Finally, the optimal voltage vector is selected by the cost function. The proposed method is compared with the traditional model predictive current control method through experiments. The experimental results show the effectiveness of the proposed method.

Model Predictive Current Control with Asymmetric Stacked Multilevel Inverter and LCL-Filter Based STATCOM

The work presented in this paper deals with a proposal of a new topology of a multilevel inverter to act as a Static synchronous Compensator (STATCOM). The proposed inverter is the five-level Asymmetric Stacked Multi-Level Inverter (ASMLI). One of the essential features of this inverter that distinguishes it from the conventional types is that it achieves the required voltage levels with fewer switching devices, leading to simplifying the control process. Moreover, the work includes using a Finite Control Set Model Predictive Current Control (FCS-MPCC) to control the proposed structure. The FCS-MPCC control strategy performs the finite optimization process at the current sampling instant to provide the optimum switching states to the inverter at the next sampling instant. Therefore, this control strategy allows injecting harmonic current and reactive power compensation to reduce source current distortion and improve the voltage profile and power factor. The optimization mechanism reduces the cost function, which is a function of measuring the network current's deviation from the reference value and how the capacitor voltage deviates from the required values. LCL-filter was used to connect this setup to the grid, and its resonance was actively damped using the multivariable capabilities of the FCS-MPCC. The proposed control framework was simulated using MATLAB/Simulink 9.1 environment and tested in a distorted and healthy network compared to a conventional two-level converter with RL-filter. The STATCOM was used to inject reactive power to raise the source power factor to unity and reduce source current harmonics by injecting harmonic current. The proposed prototype could absorb 70% of source current harmonics, which is nearly 25% better than a conventional inverter, inject an appropriate amount of reactive power, and raise the source power factor to unity in two case scenarios. The performance achieved was promising at steady-state operation and speedy response during transients with balanced capacitors voltages.

Virtual-Vector-Based FCS Model Predictive Current Control with Duty Cycle Optimization for Dual Three-Phase Motors

This paper presents an improved finite control set model predictive current control (FCS-MPCC) based on virtual vectors with duty cycle control for dual three-phase permanent magnet synchronous motor (DTP PMSM) drives. Virtual vectors synthesized by two basic voltage vectors of the inverter are introduced to suppress the harmonic current by the vector space decomposition (VSD) model. And the virtual vectors with the optimal duty cycle can be obtained by duty cycle control using zero vectors to generate a fixed switching frequency. Moreover, a simple scheme of centrosymmetric switching patterns is presented for the realtime implementation. In this way, the performance of current tracking is improved while the computational burden is reduced. Finally, results of simulation analysis and DSP test for the three different strategies are given to verify the validity of the proposed strategy.

Robust adaptive observer-based finite control set model predictive current control for sensorless speed control of surface permanent magnet synchronous motor

The objective of the paper is to present the efficient and dynamic sensorless speed control of a surface permanent magnet synchronous motor (SPMSM) drive at a wide speed range. For high-performance speed sensorless control, a finite control set model predictive current control (FCS-MPCC) algorithm based on a model reference adaptive system (MRAS) is proposed. With the FCS-MPCC algorithm, the inner current control loop is eliminated, and the limitations of the cascaded linear controller are overcome. The proposed speed sensorless control algorithm provides an efficient speed control technique for the SPMSM drive owing to its fast dynamic response and simple principle. The adaptive mechanism is adopted to estimate the rotor shaft speed and position used in FCS-MPCC for dynamic sensorless control. FCS-MPCC uses a square cost function to determine the optimal output voltage vector (VV) from the switching states that give the low cost index. A discrete-time model of a system is used to predict future currents across all the feasible VVs produced by the voltage source inverter. The VV that reduced the cost function is adopted and utilized. Simulation results showed the efficacy of the presented scheme and the viability of the proposed sensorless speed control design under various load conditions at a wide speed operation range.