Artificial Neuron-Based Model for a Hybrid Real-Time System: Induction Motor Case Study

Automatic Machine Learning (AML) methods are currently considered of great interest for use in the development of cyber-physical systems. However, in practice, they present serious application problems with respect to fitness computation, overfitting, lack of scalability, and the need for an enormous amount of time for the computation of neural network hyperparameters. In this work, we have experimentally investigated the impact of continuous updating and validation of the hyperparameters, on the performance of a cyber-physical model, with four estimators based on feedforward and narx ANNs, all with the gradient descent-based optimization technique. The main objective is to demonstrate that the optimized values of the hyperparameters can be validated by simulation with MATLAB/Simulink following a mixed approach based on interleaving the updates of their values with a classical training of the ANNs without affecting their efficiency and automaticity of the proposed method. For the two relevant variables of an Induction Motor (IM), two sets of estimators have been trained from the input current and voltage data. In contrast, the training data for the speed and output electromagnetic torque of the IM have been established with the help of a new Simulink model developed entirely. The results have demonstrated the effectiveness of ANN estimators obtained with the Deep Learning Toolbox (DLT) that we used to transform the trained ANNs into blocks that can be directly used in cyber-physical models designed with Simulink.Junta de Andalucia B-TIC-42-UGR20European CommissionSpanish Science Ministry (Ministerio de Ciencia e Innovacion) PID2020-112495RB-C2

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

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

FPGA design methodology for industrial control systems—a review

This paper reviews the state of the art of fieldprogrammable gate array (FPGA) design methodologies with a focus on industrial control system applications. This paper starts with an overview of FPGA technology development, followed by a presentation of design methodologies, development tools and relevant CAD environments, including the use of portable hardware description languages and system level programming/design tools. They enable a holistic functional approach with the major advantage of setting up a unique modeling and evaluation environment for complete industrial electronics systems. Three main design rules are then presented. These are algorithm refinement, modularity, and systematic search for the best compromise between the control performance and the architectural constraints. An overview of contributions and limits of FPGAs is also given, followed by a short survey of FPGA-based intelligent controllers for modern industrial systems. Finally, two complete and timely case studies are presented to illustrate the benefits of an FPGA implementation when using the proposed system modeling and design methodology. These consist of the direct torque control for induction motor drives and the control of a diesel-driven synchronous stand-alone generator with the help of fuzzy logic

Modelling, simulation and analysis of DSIM using artificial intelligent controller

This paper presents an elaboration for speed control of dual stator induction motor (DSIM) using artificial intelligent controller. The main problem with the conventional fuzzy controllers is that the parameters associated with the membership functions and the rules depend broadly on the intuition of the experts. To overcome this problem, adaptive neuro-fuzzy inference system (ANFIS) controller is proposed in this paper. The elaboration allows to compare simulation results between classical and artificial intelligent controllers. The fuzzy logic controller (FLC) and ANFIS controllers are also introduced to the system for keeping the motor speed to be constant when the load varies. Comparison between PI, fuzzy and Adaptive neuro-fuzzy controller-based dynamic performance of DSIM drive has been presented. ANFIS-based control of DSIM will prove to be more reliable than other control methods. The performance of the DSIM drive has been analyzed for constant load and change in speed conditions.&nbsp

A New Induction Motor Adaptive Robust Vector Control based on Backstepping

In this paper, a novel approach to nonlinear control of induction machine, recursive on-line estimation of rotor time constant and load torque are developed. The proposed strategy combines Integrated Backstepping and Indirect Field Oriented Controls. The proposed approach is used to design controllers for the rotor flux and speed, estimate the values of rotor time constant and load torque and track their changes on-line. An open loop estimator is used to estimate the rotor flux. Simulation results are presented which demonstrate the effectiveness of the control technique and on-line estimation

Machine Learning based Early Fault Diagnosis of Induction Motor for Electric Vehicle Application

Electrified vehicular industry is growing at a rapid pace with a global increase in production of electric vehicles (EVs) along with several new automotive cars companies coming to compete with the big car industries. The technology of EV has evolved rapidly in the last decade. But still the looming fear of low driving range, inability to charge rapidly like filling up gasoline for a conventional gas car, and lack of enough EV charging stations are just a few of the concerns. With the onset of self-driving cars, and its popularity in integrating them into electric vehicles leads to increase in safety both for the passengers inside the vehicle as well as the people outside. Since electric vehicles have not been widely used over an extended period of time to evaluate the failure rate of the powertrain of the EV, a general but definite understanding of motor failures can be developed from the usage of motors in industrial application. Since traction motors are more power dense as compared to industrial motors, the possibilities of a small failure aggravating to catastrophic issue is high. Understanding the challenges faced in EV due to stator fault in motor, with major focus on induction motor stator winding fault, this dissertation presents the following: 1. Different Motor Failures, Causes and Diagnostic Methods Used, With More Importance to Artificial Intelligence Based Motor Fault Diagnosis. 2. Understanding of Incipient Stator Winding Fault of IM and Feature Selection for Fault Diagnosis 3. Model Based Temperature Feature Prediction under Incipient Fault Condition 4. Design of Harmonics Analysis Block for Flux Feature Prediction 5. Flux Feature based On-line Harmonic Compensation for Fault-tolerant Control 6. Intelligent Flux Feature Predictive Control for Fault-Tolerant Control 7. Introduction to Machine Learning and its Application for Flux Reference Prediction 8. Dual Memorization and Generalization Machine Learning based Stator Fault Diagnosi

Power quality disturbances assessment during unintentional islanding scenarios. A contribution to voltage sag studies

This paper presents a novel voltage sag topology that occurs during an unintentional islanding operation (IO) within a distribution network (DN) due to large induction motors (IMs). When a fault occurs, following the circuit breaker (CB) fault clearing, transiently, the IMs act as generators due to their remanent kinetic energy until the CB reclosing takes place. This paper primarily contributes to voltage sag characterization. Therefore, this novel topology is presented, analytically modelled and further validated. It is worth mentioning that this voltage sag has been identified in a real DN in which events have been recorded for two years. The model validation of the proposed voltage sag is done via digital simulations with a model of the real DN implemented in Matlab considering a wide range of scenarios. Both simulations and field measurements confirm the voltage sag analytical expression presented in this paper as well as exhibiting the high accuracy achieved in the three-phase model adopted.Postprint (published version