Switching Table Research Articles

Improved Active Vector Optimization Strategy‐based Model Predictive Torque Control for Matrix Converter‐Fed PMSM With Reduced Torque and Flux Ripples

ABSTRACTThe Matrix Converter‐fed Finite Control Set‐Model Predictive Torque Control (FCS‐MPTC) technique has superior potentiality in the control of electrical drives due to its availability of increased number of voltage vectors with compact topology. On the other side, the FCS‐MPTC technique suffers a major drawback of high computational complexity and processing time. This major drawback has degraded the attention of the FCS‐MPTC technique in high‐power industrial applications because it needs expensive and high‐rating processors for the functioning of the control algorithm. In this regard, this paper proposes an efficient active vector optimization concept by formulating a switching table for the optimal voltage vector selection. This approach reduces the available 18 active vectors to three, thereby decreasing the execution time of the cost function (CF). In addition, to mitigate the high torque and flux ripple problems associated with the existing FCS‐MPTC method, a modified switching table is proposed. The effectiveness of the proposed approach is evaluated by conducting various test loadings using the MATLAB Simulink, and the results are further validated in real time using the OPAL‐RT laboratory setup.

Model predictive control of a brushless cascadedoubly-fed induction generator with torque regulation for stand-alone applications

In this paper, a novel control method for Brushless Cascade Doubly-Fed Induction Generator (BCDFIG) control is proposed by integrating the Model Predictive Control (MPC) and the Direct Torque Control (DTC) methods. In previous studies, the DFIG's torque control has been performed, using different methods to improve power production. Here, direct torque control, assisted by model predictive control, is used to maintain output voltage during a change in wind speed and load. To reduce switching loss in the inverter, the model predictive unit selects the optimized switching vector and sends it to the DTC unit. According to a predefined switching table in each phase interval, the proper switching sequence for controlling the flux and torque for producing a constant output voltage will be selected. This method can be useful for wind farms and everywhere we need constant voltage, especially in stand-alone applications. Also, this proposed method can be used in grid-connected networks for injecting power into the grid or controlling reactive power. For evaluating this control method, in the first step, the BCDFIG setup is simulated in MATLAB software. Then an experimental setup has been realized in the lab to perform some tests. At the end, the obtained experimental results were investigated.

Traffic evolution in Software Defined Networks

Software Defined Networking (SDN) offers unprecedented traffic engineering possibilities due to optimal centralized decision making. However, network traffic evolves over time and changes the underlying optimization problem. Frequent application of the model to reflect traffic evolution causes flooding of control messages, traffic re-routing and synchronization problems. This paper addresses the problem of graceful traffic evolution in SDNs (Software Defined Networks) minimizing rule installations and modifications, optimizing the global objectives of minimization of Maximum Link Utilization (MLU) and minimization of the Maximum Switch Table Space Utilization (MSTU). The problem is formulated as multi-objective optimization using Mixed Integer Linear Programming (MILP). Proof of NP-Hardness is provided. Then, we re-formulate the problem as a single-objective problem and propose two greedy algorithms to solve the single-objective problem, namely MIRA-Im and MIRA-Im with Conflict Detection, and experiments are performed to show the effectiveness of the algorithms in comparison to previous state of the art proposals. Simulation results show significant improvements of MIRA-Im with Conflict Detection, especially in terms of number of installed rules (with a gain till 80% with the highest number of flows) and flow table space utilization (with a gain till 55% with the highest number of flows), compared to MIRA-Im and other algorithms available in the literature, while the other metrics are essentially stable.

An improved finite set model predictive control of SRM drive based on a voltage vectors strategy for low torque ripple

The robust rotor structure and fault-tolerance characteristics of the Switched Reluctance Motors (SRMs) are the best choice for next-generation Electric Vehicle (EV) applications. This machine has few restraints like high torque and flux ripples. However, the existing Model Predictive Control (MPC) using multiple control objectives and maximum sectors in the switching table results in high torque ripples due to the improper sector partition, Voltage Vectors (VVs) and weight factor (K1) selection. This paper proposes a Finite Set-Model Predictive Control (FS-MPC) for an analytical model of a non-linearity SRM machine to analyze the torque ripple performance. The proposed VVs are derived using sector partition based on the rotor position. The control is designed as a single cost function with the weighting factor contributing to smooth torque by selecting optimal control signals. Simulation studies and experiments with a four-phase 8/6 SRM drive verifies the enhanced FS-MPC for real-time implementation. The dynamic speed and ripple values of SRM Drives are measured using a mixed signal oscilloscope and the sensor probes. The laboratory outcomes calculate the analytical equations to validate the findings. The calculated value of torque ripple is 9 % through this FS-MPC. The study reveals that the proposed method is well suited for torque ripple reduction than flux ripples.

Weightless Model Predictive Control for Permanent Magnet Synchronous Motors with Extended State Observer

Traditional model predictive torque control (MPTC) predicts the torque and flux values for the next time step and selects the voltage vector that minimizes the cost function as the optimal vector to apply to the inverter. This control approach is straightforward and allows for multi-objective control, but it has some issues in terms of the dynamic steady-state performance and parameter robustness. Therefore, this paper proposes a weightless model predictive control method based on an extended state observer (ESO). By designing an improved ESO to observe and compensate for motor parameter disturbances in real time, and employing a novel 2-D switching table and voltage vector sector selection diagram, the method evaluates three out of eight voltage vectors based on the torque and stator flux error signals. This reduces the computational load while increasing the number of candidate voltage vectors. Finally, a cost function without weighting factors is designed to lower the computational complexity. The simulation results show that the proposed new control method effectively reduces the torque and flux ripple and improves the current waveform compared to traditional MPTC.

Enhancing the efficiency of a dual three-phase permanent magnet synchronous motor with modified switching table for direct torque control

Enhancing the efficiency of a dual three-phase permanent magnet synchronous motor with modified switching table for direct torque control

ENHANCEMENT RIPPLES OF A DIRECT TORQUE CONTROL APPLIED TO A PERMANENT MAGNET SYNCHRONOUS MOTOR BY USING A FOUR-LEVEL MULTICELLULAR INVERTER AND A NEW REDUCED SWITCHING TABLE

The improvement of the direct control of flux and torque (DTC) essentially consists of reducing the ripples encountered at the levels of the torque and the stator magnetic flux. This control technique is widely used in various applications that recently employ the permanent magnet synchronous motor (PMSM). The improvement techniques are grouped into two large families: those that use multilevel inverters and require an additional cost to the driven system, which is only encouraging if the application covers this cost, and those that offer selection tables. This last solution is advantageous because it does not need any added device. In this paper, we propose to combine the two solutions, using a reduced switching table (RST) compared to the conventional switching table (CST) and using a four-level multicellular inverter (4LMI). This solution brought very satisfactory results by significantly reducing the ripples.

Optimizing electric vehicle powertrains peak performance with robust predictive direct torque control of induction motors: a practical approach and experimental validation

Enhancing the efficiency of the electric vehicle’s powertrain becomes a crucial focus, wherein the control system for the traction motor plays a significant role. This paper presents a novel electric vehicle traction motor control system based on a robust predictive direct torque control approach, an improved version of the conventional DTC, where the traditional switching table and the hysteresis regulators are substituted with a predictive block based on an optimization algorithm. Additionally, a robust predictive speed loop regulator is employed instead of the proportional-integral regulator, which integrates a new cost function with a finite horizon, incorporating integral action into the control law based on a Taylor series expansion. This technique’s primary benefit is its independence from the necessity to measure and observe external disturbances, as well as uncertainties related to parameters. The effectiveness of the suggested system was confirmed through simulation and experimental results under the OPAL-RT platform. The findings indicate that the proposed approach outperforms the conventional method in terms of rejecting disturbances, exhibiting robustness to variations in parameters, and minimizing torque ripple.

Intelligent control of the power generation system using DSPACE

AbstractWind power systems (WPs) are complex non‐linear systems with varying parameters affected by environmental changes, including wind speed fluctuations. Extracting maximum power from WPs poses a significant challenge due to these factors. Direct power control (DPC) is a highly effective technique known for its simplicity and ease of implementation. However, it suffers from power ripples caused by the use of hysteresis comparators and switching tables that operate at variable frequencies. To address this issue, this paper presents the robust neural controller (NC) based on DPC, which replaces the switching tables. The Double‐Fed Induction Generator (DFIG) is the chosen generator for the studied WP system due to its advantageous features. The NC‐DPC effectively regulates the exchange of active and reactive powers between the DFIG and the system, maximizing power extraction from the WP system while reducing Total Harmonic Distortion and enhancing overall system quality. The effectiveness of the NC‐DPC is evaluated through MATLAB simulations and further supported by experimental data obtained using the Real‐Time Interface of the dSPACE‐DS1104 Controller card.

Artificial neural network-based direct power control to enhance the performance of a PMSG-wind energy conversion system under real wind speed and parameter uncertainties: An experimental validation

With the increasing emphasis on embedding advanced technology into system controls, the Direct Power Control (DPC) approach has garnered considerable attention due to its simple and highly adaptable algorithm. This approach has been increasingly recognized in numerous applications. However, the variable frequency, harmonic distortion of the currents, and power ripples caused by Hysteresis controllers and switching tables decrease its effectiveness and robustness, affecting the system’s performance. For this reason, this paper proposed a new DPC based on Artificial Neural Network (ANN) approaches. In this approach, the hysteresis comparator and the switching table are substituted with ANN controllers and then applied on both sides: machine-side converter (MSC) and grid-side converter (GSC) of a Permanent Magnet Synchronous Generator based Wind Energy Conversion System (PMSG-WECS). Moreover, to make the system more efficient in varying wind conditions, this study expands the utilization of the artificial neural networks (ANN) to encompass the maximum power point tracking (MPPT) control strategy. To demonstrate the effectiveness of the proposed approach on the system behaviors, a simulation test was carried out in the Matlab/Simulink environment, using a real wind profile of a Moroccan city (Essaouira). In comparison to the classical DPC control, the simulation results showed the superior performance of the proposed ANN-DPC control in terms of reference tracking, response time, overshoot, precision, and its capacity to reduce the rate of power ripples and total harmonic distortion (THD) in the injected currents. Furthermore, a robustness test was also included in this work to check the robustness of the proposed control against parameters variation. In conclusion, the feasibility and effectiveness of the ANN-DPC control approach were confirmed through experimental validation using the dSPACE DS1104 board.

Direct power control of dual three-phase permanent magnet synchronous motor based on multiple vectors

In response to the traditional mode of dual three-phase permanent magnet synchronous generator switch form vector direct power control (DPC), the current harmonics are large, the active (P) and reactive (Q) harmonic oscillations are large, and the unstable losses of each phase current are large. A virtual voltage vector direct power control method based on duty cycle modulation is proposed. This method compares and synthesizes the two vectors of the outermost and second layers of the PWM rectifier, as well as three large vector synthesis methods, to obtain 12 new virtual voltage vectors. Then, the combined voltage vector is directly modulated into the deadbeat switching table, and the optimal predicted vector value is calculated based on the principle of the deadbeat formula, further suppressing the problem of large fluctuations in active and reactive power. Results show that the proposed multi-vector composite odd even quadrant DPC can effectively reduce current harmonics and power ripple, make the phase current more stable, reduce motor operating losses, and significantly improve the motor.

Advanced direct torque control based on neural tree controllers for induction motor drives

This paper introduces a novel direct torque control approach based on the decision tree (T-DTC), employing artificial neural networks that are effectively trained to enhance accuracy and robustness. The main objective of T-DTC is the substantial reduction of flux and torque ripples inherent in the conventional DTC, ensuring effective control of the induction motor. The conventional hysteresis controllers for stator flux and electromagnetic torque are replaced by two advanced controllers named M5 Prime model trees. Additionally, the traditional switching table is substituted with a novel decision tree table utilizing the classifier algorithm 4.5. The effectiveness of the proposed T-DTC strategy is demonstrated through simulation in MATLAB/Simulink and validated in real-time using an HIL platform based on OPAL-RT OP 5600 and Virtex 6 FPGA ML605. The results obtained demonstrate a notable improvement compared to existing techniques in the literature.

Two different controllers-based DPC of the doubly-fed induction generator with real-time implementation on dSPACE 1104 controller board

The direct power command (DPC) of the doubly-fed induction generators (DFIG) has garnered increasing attention due to its simplicity, ease of implementation, and fast dynamic response that distinguished it from other controls. Recently, nonlinear controllers have been suggested to improve the DPC robustness, in particular the minimization of the DFIG power fluctuations. In the present research work, two different controls are used to regulate the DFIG power, where the first is a traditional DPC and the second is the DPC based on neural super-twisting algorithm (NSTA) controllers. The NSTA controllers were used instead of hysteresis comparators in the DPC technique, and the pulse width modulation (PWM) technique was used as a suitable solution instead of the switching table to better manage the state of switches and simplify the control system. The proposed controls were investigated and implemented using Matlab software and Dspace 1104 card with different tests to determine the best control. Experimental and simulation results show the good efficiency of the NSTA controller in improving energy and current quality. Also, the comparison is made between both techniques and the other controls in terms of reducing current harmonic distortions and ratios of the DFIG power fluctuations.

Optimization of Direct Power Control of Three-Phase Active Rectifiers by using Multiple Switching Tables

One of the best known control methods for three-phase active rectifiers is the so-called Direct Power Control (DPC). Control algorithm in DPC is based on the regulation of instantaneous active and reactive powers and incorporates a switching table where the successive optimal configurations of the rectifier are stored. Traditional DPC models use only one switching table within their control systems. In this study, a new DPC method is presented, based on the combination of two different switching tables. By using Simulink simulations, it is demonstrated that better dynamics and lower switching stress are achieved in the rectifier thanks to the use of the proposed method, in comparison to the simple switching table models.

Experimental DTC of an Induction Motor Applied to Optimize a Tracking System

In recent years, the areas of industrial application of high performance AC drives, especially the induction motor (IM), based on Direct Torque Controller (DTC) technique has gradually increased due to its advantages over the other techniques of control. Among these applications are cited, propulsion and tracking systems. This work presents the experimental part of a research project conducted by the authors where they have implemented an algorithm for a DTC in order to achieve a tracking system designed for a series of solar panels to maximize its overall efficiency. In this paper, three different switching tables are presented for choosing the best between them for a less fluctuation in torque. This study is justified by reduced cost of implementation compared to other methods that require the use of expensive hardware and complicated control techniques. The tests were carried out under the same conditions with the same equipment of the testbed, to get a good comparative study. Three variants were tested: simple switching table, table of sectors shifted by 30 ° and a table with 12 sectors. The results show an improvement, but a dilemma between the torque ripple and those of the stator flux.

Hysteresis controller based on a circular error area table applied to a variable speed renewable energy system.

Nowadays the proliferation of renewable energies has the problem of the limited capacity of the grid to absorb these types of energies due to their low quality. In order to increase their penetration in the electrical system it is mandatory to improve the control of the power stages used to inject the generated power and, in many cases, to achieve variable speed operation. The paper proposes a new current control method based on a switching table that keeps the current error in a circular area and compares it with other table hysteresis control [1],[2]. As it is shown, both the THD and the frequency is better in the proposed method.

A new flux and multilevel hysteresis torque band DTC with lookup table‐based switched reluctance motor drive to suppress torque ripple

SummaryThe switched reluctance motor (SRM) is a prominent electrical machine drive with low cost and good performance in different drive applications. This machine's main drawbacks are torque ripple and flux ripple. However, the existing SRM‐operated direct torque control (DTC) Voltage vectors (VVs) give high torque ripples due to improper sector division and minimum selection of VVs switching states. On the other hand, a two‐level hysteresis torque band can result in torque ripples. Therefore, this paper proposed DTC using 16 partitions, active small and large VVs, and a multilevel hysteresis torque band (MHTB) strategy to mitigate the torque ripple further. More active VVs (i.e., 16) are employed in the modified sector‐based switching tables, suppressing the torque ripples. The proposed strategy is verified and validated using MATLAB/Simulink. The detailed result discusses the response of torque, flux, and speed of SRM. The DTC‐operated SRM drive experimental results prove the effective minimization of torque ripples in the proposed DTC compared to the existing DTC.

Enhancing the control of doubly fed induction generators using artificial neural networks in the presence of real wind profiles.

This study tackles the complex task of integrating wind energy systems into the electric grid, facing challenges such as power oscillations and unreliable energy generation due to fluctuating wind speeds. Focused on wind energy conversion systems, particularly those utilizing double-fed induction generators (DFIGs), the research introduces a novel approach to enhance Direct Power Control (DPC) effectiveness. Traditional DPC, while simple, encounters issues like torque ripples and reduced power quality due to a hysteresis controller. In response, the study proposes an innovative DPC method for DFIGs using artificial neural networks (ANNs). Experimental verification shows ANNs effectively addressing issues with the hysteresis controller and switching table. Additionally, the study addresses wind speed variability by employing an artificial neural network to directly control reactive and active power of DFIG, aiming to minimize challenges with varying wind speeds. Results highlight the effectiveness and reliability of the developed intelligent strategy, outperforming traditional methods by reducing current harmonics and improving dynamic response. This research contributes valuable insights into enhancing the performance and reliability of renewable energy systems, advancing solutions for wind energy integration complexities.

Contribution to the Improvement of Sensorless DTC-SVM for Three-Level NPC Inverter-fed Induction Motor Drive

This paper deals with the high performance of multi-level direct torque control (DTC) for induction machines without speed and stator flux sensors. The estimation is performed using a sliding mode observer, which is known for its robustness in high and low-speed operations, the control is based on a backstopping speed controller. This control technique was introduced years ago to circumvent the problems of sensitivity to parametric variations, it presents a high dynamic but their major problems are the variable switching frequency, the size and complexity of the switching tables, and undulations of the torque. The proposed approach is to replace switching tables with constant switching frequency control using three-level spatial vector modulation (SVM). Theoretical elements and simulation results are presented and discussed. As a result, the flux and torque ripple of three-level DTC-SVM control is greatly reduced compared to the flux and torque ripple of DTC-classic control. The advantages of the training system have been validated by the simulation results.

Modelling, Simulation and Implementation on dSpace 1103 of the Direct Power Control in a Three-Phase Shunt Active Power Filter System

This article describes a control method called "direct power control" designed specifically for parallel active power filters. The purpose of this method is to attenuate harmonics in the supply current and offset reactive power problems. The main goal is to bring the active power and reactive power back to the reference values through hysteresis control. The output of the hysteresis controller is combined with the switching table to regulate active and reactive power in real time by determining the optimal switching configuration of the inverter. This study proposes a novel switching table design based on analyzing the impact of the inverter switching vector on the instantaneous reactive and active power derivatives. The goal is to reduce the number of commutations by eliminating zero vectors while maintaining the required DC bus voltage using anti-windup techniques based on PI controllers.