Three-Phase Induction Motor Speed Estimation Using Recurrent Neural Network

In induction motor speed control method, the development of the field-oriented control (FOC) algorithm which can control torque and flux separately enables the motor to replace many roles of DC motors. Induction motor speed control can be done by using a close loop system which requires a speed sensor. Referring to the speed sensor weaknesses such as less accurate of the measurement, this is due to the placement of the sensor system that is too far from the control system. Therefore, a speed sensorless method was developed which has various advantages. In this study, the speed sensorless method using an artificial neural network with recurrent neural network (RNN) as speed observer on three-phase induction motor has been discussed. The RNN can maintain steady-state conditions against a well-defined set point speed, so that the observer is able and will be suitable if applied as input control for the motor drives. In this work, the RNN has successfully estimated the rotor flux of the induction motor in MATLAB R2019a simulation as about 0.0004Wb. As based on speed estimation error, the estimator used has produced at about 26.77%, 8.7% and 6.1% for 150rad/s, 200rad/s and 250rad/s respectively. The future work can be developed and improved by creating a prototype system of the induction motor to get more accurate results in real-time of the proposed RNN observer

A neuro-fuzzy approach for stator resistance estimation of induction motor = pendekatan neuro-fuzzy untuk meramal rintangan stator pada motor induksi

During the operation of induction motor, stator resistance changes incessantly with the temperature of the working machine. This situation may cause an error in rotor resistance estimation of the same magnitude and will produce an error between the actual and estimated motor torque which can leads to motor breakdown in worst cases. Therefore, this project will propose an approach to estimate the changes of induction motor stator resistance using neuro-fuzzy. Then, it will be compared with conventional method like P1 estimator to see the effectiveness. The behaviour of the induction machine will be analyzed when the stator resistance is changed. Based on the changes, a corrective procedure will be applied to ensure the stabilities of the induction motor. Generally, this project can be divided into three main parts which are design of induction motor, design of neuro-fuzzy and PT estimator, and corrective procedure for the induction machine. The Newcastle Drives Simulation Library will be used to design the induction motor model and MATLAB SIMULINK will be used to design the stator current observer. The neuro-fuzzy estimator will be designed based on Sugeno Method Fuzzy Inference System

Multiphase induction motor drives - a technology status review

The area of multiphase variable-speed motor drives in general and multiphase induction motor drives in particular has experienced a substantial growth since the beginning of this century. Research has been conducted worldwide and numerous interesting developments have been reported in the literature. An attempt is made to provide a detailed overview of the current state-of-the-art in this area. The elaborated aspects include advantages of multiphase induction machines, modelling of multiphase induction machines, basic vector control and direct torque control schemes and PWM control of multiphase voltage source inverters. The authors also provide a detailed survey of the control strategies for five-phase and asymmetrical six-phase induction motor drives, as well as an overview of the approaches to the design of fault tolerant strategies for post-fault drive operation, and a discussion of multiphase multi-motor drives with single inverter supply. Experimental results, collected from various multiphase induction motor drive laboratory rigs, are also included to facilitate the understanding of the drive operatio

Estimation of rotor flux of an induction machine

The objective of this dissertation is to estimate rotor flux of an IM. Some of the material is focused on the functional block of the IM i.e. Torque estimator, Speed estimator etc. while a subsequent part deals with estimation of rotor flux. The dissertation is organized as follows:Chapter 1 describes background information of the machines then it focuses on the methodology how on to approach the task on a particular time with the help of Gantt chart.Chapter 2 presents the basic principals of rotating magnetic field of the IM and asserts brief overview of the AC machines. Later it talks about different kinds of IM rotors suggesting which one is good. It is crucial to start with good and appropriate reviews which were verified by numerous journals. Literature review is presented by analysing the previous work. (Busawan et al., 2001) summarises that a nonlinear observers for the estimation of the rotor flux and the load torque in an induction motor. The observers are designed on the basis of the standard alpha - beta Park's model. Finally, fuzzy logic is mentioned in more detailed way and Membership functions were also discussedChapter 3 explains the dynamic model of induction machine plant and the model was presented. Then the model is analysed, developed in MATLAB-SIMULINK which was discussed in Chapter 4. By considering following assumptions, dynamic model is implemented i.e. it should be symmetrical two-pole, three phase windings. Slotting effects are neglected, Permeability of the iron part is infinite, and iron losses are neglected. Dynamic d-q model and Axes transformation is implemented on stationary reference frame (a-b-c). Lastly torque equation is derived.Chapter 4 is the heart of this project by scrutinizing the model thoroughly and by introducing fuzzy controller logic using MATLAB-SIMULINK; simulations are performed to estimate the functional block such as torque, speed, flux, resistance with and without fuzzy logic. Results were obtained for different blocks and the m-file, DTC, Flux table were obtained and presented in the Appendixes.Chapter 5 concludes the simulation results and concentrates mainly on the future direction what more can be done to improve the platform in a more efficient manner

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM

Machine Model Based Speed Estimation Schemes for Speed Encoderless Induction Motor Drives: a Survey

Speed Estimation without speed sensors is a complex phenomenon and is overly dependent on the machine parameters. It is all the more significant during low speed or near zero speed operation. There are several approaches to speed estimation of an induction motor. Eventually, they can be classified into two types, namely, estimation based on the machine model and estimation based on magnetic saliency and air gap space harmonics. This paper, through a brief literature survey, attempts to give an overview of the fundamentals and the current trends in various machine model based speed estimation techniques which have occupied and continue to occupy a great amount of research space

New Model Reference Adaptive System Speed Observer for Field-Oriented Control Induction Motor Drives Using Neural Networks

One of the primary advantages of field-oriented controlled induction motor for high performance application is the capability for easy field weakening and the full utilization of voltage and current rating of the inverter to obtain a wide dynamic speed rangeThis paper describes a Model Reference Adaptive System (MRAS) based scheme using Artificial Neural Network (ANN) for online speed estimation of sensorless vector controlled induction motor drive. The proposed MRAS speed observer uses the current model as an adaptive model. The neural network has been then designed and trained online by employing a back propagation network (BPN) algorithm. The estimator was designed and simulated in Matlab/Simulink. Simulation result shows a good performance of speed estimator. The simulation results show good performance in various operating conditions. Also Performance analysis of speed estimator with the change in resistances of stator is presented. Simulation results show this estimator robust to parameter variations especially resistances of stator

Machine model based Speed Estimation Schemes for Speed Encoderless Induction Motor Drives: A Survey

Speed Estimation without speed sensors is a complex phenomenon and is overly dependent on the machine parameters. It is all the more significant during low speed or near zero speed operation. There are several approaches to speed estimation of an induction motor. Eventually, they can be classified into two types, namely, estimation based on the machine model and estimation based on magnetic saliency and air gap space harmonics. This paper, through a brief literature survey, attempts to give an overview of the fundamentals and the current trends in various machine model based speed estimation techniques which have occupied and continue to occupy a great amount of research space

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 192

This bibliography lists 247 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1979

MODELING AND SIMULATION OF ROTOR FLUX OBSERVER BASED INDIRECT VECTOR CONTROL OF INDUCTION MOTOR DRIVE USING FUZZY LOGIC CONTROL

The indirect vector controlled inductor motor (IM) drive involves decoupling of the stator current into torque and flux producing components. This paper proposes the implementation of fuzzy logic control scheme applied to a two d-q current components model of an induction motor. A Fuzzy logic Controller is developed with the help of knowledge rule base for efficient and robust control. The performance of Fuzzy Logic Controller is compared with that of the PI controller with rotor flux observer in terms of the settling time and dynamic response to sudden load changes. The harmonic pattern of the output current is evaluated for both fixed gain proportional integral controller and the Fuzzy Logic based controller. The performance of the IM drive has been analyzed under steady state and transient conditions. Simulation results of both the controllers are presented for comparison