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Study on the characteristics of low voltage ride through for DFIG based on vector control and direct torque control technology
Published in Rodolfo Dufo-López, Jaroslaw Krzywanski, Jai Singh, Emerging Developments in the Power and Energy Industry, 2019
Yi Wan, Zhe Zhao, Zhongxiang Li, Liu Yang, Chao He, Qinyue Tan*
Comparing Figure 2 and Figure 1, it can be seen that under the same simulation conditions, the dynamic response of the new control strategy is faster, the mechanical torque Tm and the electromagnetic torque Tem are less changed and the stabilization time is reduced. The fluctuations of active power and reactive power are small when the fault occurs as shown in Figure (c) and (d), which ensure that the grid is still connected when the voltage drops for a short period of time. The DC bus voltage changes less during the regulating process as shown in Figure (e) to ensure the safety of the rotor side converter; The rotor speed is quickly stabilized as shown in Figure (f), which ensure the stable operation of the generator. Analyzing Figure (a) to (f), it can be seen that the new strategy has a good effect on preventing DC side from getting over-voltage. When the grid voltage drops seriously, the ability of LVRT is strong. It combines the advantages of direct torque control and vector control, the dynamic response is faster and the fluctuation is smaller than VC and DTC.
Fuzzy Logic-Based IoT Technique for Direct Torque Control of Induction Motor Drive
Published in Ankan Bhattacharya, Bappadittya Roy, Samarendra Nath Sur, Saurav Mallik, Subhasis Dasgupta, Internet of Things and Data Mining for Modern Engineering and Healthcare Applications, 2023
Aurobinda Bag, Bibhuti Bhushan Pati
More than half of the total electricity consumed in industry is by electric motors. Among them, the three-phase induction motor is used about 80% for industrial control. It has replaced the DC motor for its simple construction, reliability, low cost and easy maintenance. Though it has several advantages, the dynamics of the induction machine are complex. However, the advanced control of torque and flux is necessary for an induction motor. Therefore, scalar control like the V/F control strategy for induction machines keeps the flux of the induction machine constant. But this control technique is suitable where the speed variation is not large. In the early days, Direct Torque Control (DTC) for induction machine drive has been considered to exhibit a very fast and superior dynamic response of torque. Furthermore, DTC for induction machine drive has been considered a surrogate to the field-oriented control (FOC) algorithm [1,2]. In the FOC technique, rotor flux has been taken as the reference frame. The DTC scheme abandons the stator current control philosophy. The DTC scheme directly controls the flux itself. DTC scheme consists of a hysteresis controller, voltage source inverter (VSI), flux and torque estimator [3]. It utilizes the band of hysteresis controller for controlling the flux and torque directly of the machine with taking into account the errors resulting among the calculated values and the actual values for the torque and the flux. Also, the inverter states are directly controlled for the reduction of the torque error and the flux errors under the predetermined band value [4,5]. In [6], rotor flux and back EMF-based speed estimators are used. The calculated speed is used as feedback for the vector control system.
Permanent Magnet Synchronous Motor Drives
Published in Maurizio Cirrincione, Marcello Pucci, Vitale Gianpaolo, Power Converters and AC Electrical Drives with Linear Neural Networks, 2017
Maurizio Cirrincione, Marcello Pucci, Vitale Gianpaolo, Angelo Accetta
The direct torque control (DTC) strategy is based on the selection of a proper stator voltage space-vector to directly control the stator flux space-vector and, therefore, the produced electromagnetic torque. DTC is also usually implemented in the control of IM, as shown in Chapter 5. In this section, DTC is devised to PMSM drives.
Maximum torque per ampere controlled induction motor drive with reduced DC link capacitor
Published in Australian Journal of Electrical and Electronics Engineering, 2023
Induction motor (IM) drives are widely used for many industrial drive applications due to attractive features: cheap, robust, efficient, and reliable compared with alternatives such as permanent magnet synchronous machines (Dorrell, Parsa, and Boldea 2014). However, IM gives the best efficiency under rated load conditions only and operating conditions other than rated load provide fairly degraded performances in terms of losses and efficiency. The scaler-controlled IM drive exhibits good steady-state performance and demonstrates poor performance during dynamic operations. On the other hand, indirect field-oriented vector control (IFOC) and direct torque control (DTC) are invariably used in drive applications for high and medium dynamic performance (Bose 2002; Buja and Kazmierkowski 2004). In the aforesaid speed control methods, the flux component is kept constant regardless of torque requirement. In view of above, IM drive achieves good dynamic performance, but its efficiency becomes lower at light-loaded conditions.
Optimized Convolutional Neural Network-Based Adaptive Controller for 6-Phase DFIG-Based Wind Energy Systems
Published in Electric Power Components and Systems, 2023
Arunkumar Azhakappan, Agees Kumar Chellappan, Murugan Sethuramalingam
To reduce the inconvenience of conventional direct torque control (DTC), an intelligent DTC was established by replacing hysteresis comparators, switching tables, and speed controllers with NNs. However, the fluctuations in the amount of power produced by the DFIG indicate that there is no guarantee of receiving the maximum amount of energy, as well as the ripple in the amount of electromechanical torque produced by the generator. Direct, reactive, and active power regulation based on artificial neural network has been presented by Chojaa et al. as a potential solution to this problem. The experimental test was carried out using a digital signal processor [42]. Mahfoud et al. trained the NN method using the feed-forward backpropagation technique to reduce MSE, which was selected as the cost function. The trial-and-error technique was used to estimate the number of neurons in the hidden layer until the required performance was achieved [43]. An artificial NN was developed for estimating uncertainty and rejecting external disturbances in adaptive control based on the Lyapunov function [44]. The proposed approach has been compared by the authors with sliding mode control and field-oriented vector control, and it has been demonstrated that the suggested control is effective and resilient to uncertainty.
Fuzzy-based estimation of reference flux, reference torque and sector rotation for performance improvement of DTC-IM drive
Published in Automatika, 2022
Sampath Kumar Subramaniam, Joseph Xavier Rayappan, Balamurugan Sukumar
In advanced motor drive applications, direct torque control of induction motor (DTC-IM) drives have been used widely owing to their instantaneous torque and flux control with simple structure. The DTC drive system is identified for its robustness and dependency on motor parameter variations [1,2]. However, DTC control scheme uses hysteresis comparators for torque and flux control which causes variable switching frequency and torque ripple. Hence, it requires high sampling rate for experimental validation. DTC scheme applies voltage vectors to IM based on the switching table which is developed based on hysteresis comparators output and stator flux angle. The instantaneous requirements of variation in stator flux and torque are accomplished by the switching table. The stator flux plot has been separated into six equal sectors with respect to stator flux angle, and DTC scheme is developed using the induction machine model [3,4].