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.

Review of speed estimation algorithms for three- phase induction motor.

In the field of evolving industrial automation, there is a growing need for refined sensorless speed estimation techniques for induction drives to cater the demands of various applications. In this paper, the sensorless speed estimation algorithms for induction motor drives are investigated and reviewed detailly for real-time industrial usages. The main objective of this paper is to classify sensorless techniques by highlighting the characteristics, merits and drawbacks of each sensorless speed estimation techniques of induction motor drives. Different techniques like Rotor slot harmonics, Signal Injection, and Machine model based system have the benefits of sensorless motor drives involving lower costs, higher reliability, simpler hardware complication, improved noise immunity, and lesser maintenance requirement. As a result of the advancement of current industrial automation, more improved sensorless estimation techniques are required to meet application demand. The various speed estimation techniques are distinguished based on criteria of steady state error, dynamic behavior, low speed operation, parameter sensitivity, noise sensitivity, complexity and computation time. This comparison allows to opt the best sensorless speed estimation technique for induction motor drive to be implemented based on a specific application. The results of comparison highlight the characteristics of each technique.

A novel fault tolerant control approach based on backstepping controller for a five phase induction motor drive: Experimental investigation.

The five phase induction motor (FPIM) is a very suitable choice for different industrial applications which require high reliability. This is due to the ability of the motor to keep operating even with open stator phases. However, to ensure the right operation and for achieving the desired dynamic performance in terms of reduced torque fluctuations, a fault tolerant control (FTC) methodology must be applied. Because of this, the paper introduces a novel FTC approach for the FPIM drive based on a backstepping controller. The derivation and explanation of the proposed technique are presented and analyzed in a systematic manner. The validation of the proposed FTC strategy has been carried out experimentally using a dSPACE 1104 control board. The test results approve the validity of the designed controller in achieving the control targets which ensures the highest system reliability of the drive that is mostly required in different automotive and industrial applications.