DTC-IM drive using adaptive neuro fuzzy inference strategy with multilevel inverter

Banu, J. Barsana; Jeyashanthi, J.; Ansari, Anzum

doi:10.1007/s12652-021-03244-3

Cited by 16 publications

(3 citation statements)

References 32 publications

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“…To lessen the Harmonics, [12][13][14][15] studies the usage of many forms of Selective Harmonics Elimination procedures. Normally Multilevel inverter step angles are frequently determined using optimization techniques [16]. In [17], the author used a Genetic Algorithm (GA) to train the neural network to reduce the harmonics.…”

Section: Introductionmentioning

confidence: 99%

Selective Harmonics Elimination Technique for Artificial Bee Colony Implementation

DeepikaVinothini¹,

Karthigaivel²,

Banu³

2023

Computer Systems Science and Engineering

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In this research, an Artificial Bee Colony (ABC) algorithm based Selective Harmonics Elimination (SHE) technique is used as a pulse generator in a reduced switch fifteen level inverter that receives input from a PV system. Pulse width modulation based on Selective Harmonics Elimination is mostly used to suppress lower-order harmonics. A high gain DC-DC-SEPIC converter keeps the photovoltaic (PV) panel's output voltage constant. The Grey Wolf Optimization (GWO) filter removes far more Photovoltaic panel energy from the sunlight frame. To eliminate voltage harmonics, this unique inverter architecture employs a multi-carrier duty cycle, a high-frequency modulation approach. The proposed ABC harmonics elimination approach is compared to SHE strategies based on Particle Swarm Optimization (PSO) and Flower Pollination Algorithm (FPA). The suggested system's performance is simulated and measured using the MATLAB simulation tool. The proposed ABC approach has a THD level of 4.86%, which is better than the PSO and FPA methods.

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Section: Introductionmentioning

confidence: 99%

Selective Harmonics Elimination Technique for Artificial Bee Colony Implementation

DeepikaVinothini¹,

Karthigaivel²,

Banu³

2023

Computer Systems Science and Engineering

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“…Compared to conventional DTC, the inverter duty ratio control method gives good steady-state torque output and minimum torque ripple. A scalar control technique utilizing a fuzzy logic algorithm for the induction motor speed control has developed in [6] and [20].…”

Section: Introductionmentioning

confidence: 99%

“…To overcome this problem the sensor less induction motor control scheme has been projected by [17] because of the versatile rotor speed observer. PI-based shunt active filter has been introduced in an artificial neural network to diminish the harmonics produced by the non-linear loads in the source current [16].Improved efficiency of DTC-IM by the golden section method [18], fuzzy logic [19] and ANFIS strategy [20] are discussed.…”

Section: Introductionmentioning

confidence: 99%

ANN-based direct torque control scheme of an IM drive for a wide range of speed operation

Jeyashanthi¹,

Banu²

2021

NNW

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Induction motor (IM) drives with direct torque control (DTC) enable fast torque response without the need for complex orientation conversions or inner loop current loop. In the speed estimation responses, however, there is a significant level of torque ripple. The voltage source inverter adds acoustic noise and needs a high sampling frequency since it operates at a high and variable switching frequency. This work describes an ANN-based DTC technique for controlling the speed of an IM drive over a large speed range. To achieve good dynamic performance of induction motor drive, the ANN-based speed controller will replace the speed controller, switching tables, and hysteresis comparators. The neural network was trained using the back-propagation algorithm. The goal of a neural speed controller is to improve the system ability to respond quickly to changes in process variables while also mitigating the impacts of external perturbations. The projected ANN based DTC considerably and simply tracks the reference speed thus improves the efficiency of speed-torque of induction motors with quicker responses for rapid varying of speed reference and torque as that of Electric Vehicles in any uneven roads circumstances. MATLAB/Simulink software is used to evaluate the drive performance for both transient and dynamic operations. The proposed control performance is simulated and compared to a DTC-based traditional PI speed controller. In comparison to PI, the results show that ANN has better and faster effects. The torque ripple gets reduced by 1.5 % in ANN (artificial neural network) controller compared to PI controller. The THD (total harmonic distortion) is reduced by 6.38 % from PI controller to ANN controller.

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Artificial neural network-based DTC of an induction machine with experimental implementation on FPGA

Gdaim

Mtibaa

Mimouni

2023

Engineering Applications of Artificial Intelligence

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DTC-IM drive using adaptive neuro fuzzy inference strategy with multilevel inverter

Cited by 16 publications

References 32 publications

Selective Harmonics Elimination Technique for Artificial Bee Colony Implementation

Selective Harmonics Elimination Technique for Artificial Bee Colony Implementation

ANN-based direct torque control scheme of an IM drive for a wide range of speed operation

Artificial neural network-based DTC of an induction machine with experimental implementation on FPGA

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