State Of The Art Of In Induction Motor Speed Control

Electrical Electronics Engineering

The contemporary landscape of induction motor speed control encapsulates a dynamic amalgamation of technological advancements and innovative methodologies aimed at enhancing efficiency and performance across diverse applications. With a focus on regulating the rotational speed of induction motors, this field navigates through a spectrum of sophisticated techniques, including sensorless control, vector control, and pulse-width modulation (PWM). Sensorless control methods leverage algorithms to estimate motor parameters and rotor position, circumventing the need for physical sensors, thereby reducing cost and complexity while ensuring precise speed regulation. Vector control, on the other hand, orchestrates intricate control algorithms to manipulate both the amplitude and phase of stator currents, enabling superior dynamic response and torque control. Complementing these methodologies, PWM techniques modulate the width of pulses in the voltage supplied to the motor, offering adjustable speed control with minimal energy losses. The contemporary advancements in induction motor speed control underscore a relentless pursuit of efficiency, reliability, and adaptability across industrial, commercial, and residential domains, driving transformative innovations poised to redefine the landscape of electric motor systems.

ABSTRACT

Higher percentage of electrical energy is consumed by electric motors in loads like variable torque fan, pump and compressor. Induction motors are the most important workhorses in industry and they are manufactured in large numbers. About half of the electrical energy generated in a developed country is ultimately consumed by electric motors, of which over 90 % are induction motors. For a relatively long period, induction motors have mainly been deployed in constant-speed motor drives for general purpose applications. The rapid development of power electronic devices and converter technologies in the past few decades, however, has made possible efficient speed control by varying the supply frequency, giving rise to various forms of adjustable-speed induction motor drives. In about the same period, there were also advances in control methods and artificial intelligence (AI) techniques, including expert system, fuzzy logic, neural networks and genetic algorithm. Researchers soon realized that the performance of induction motor drives can be enhanced by adopting artificial-intelligence-based methods. Since the 1990s, AI-based induction motor drives have received greater attention. The purpose of this paper is to review major milestones that set the stage for the development of artificial intelligent based induction motor drives, including sufficient details to acquaint readers with their basic principles, strengths and limitations.

TABLE OF CONTENTS

COVER PAGE

TITLE PAGE

APPROVAL PAGE

DEDICATION

ACKNOWELDGEMENT

ABSTRACT

CHAPTER ONE

INTRODUCTION
BACKGROUND OF THE PROJECT
PROBLEM STATEMENT
AIM AND OBJECTIVE OF THE PROJECT
SCOPE OF THE PROJECT
PURPOSE OF THE PROJECT

CHAPTER TWO

LITERATURE REVIEW

OVERVIEW OF THE STUDY
HISTORICAL BACKGROUND OF INDUCTION MOTOR
PRINCIPLE OF OPERATION
TYPES OF THREE PHASE INDUCTION MOTOR
DESCRITION OF INDUCTION MOTOR
REVIEW OF DIFFERENT SPEEED CONTROL OF INDUCTION MOTOR.
REVIEW PF RELATED STUDIES

CHAPTER THREE

METHODOLOGY

CONTROL APPROACHES OF INDUCTION MOTOR
INTELLIGENT INDUCTION MOTOR CONTROL SCHEMES

CHAPTER FOUR

4.1 FUTURE TRENDS AND NEEDS

CHAPTER FIVE

CONCLUSION
REFERENCES

CHAPTER ONE

1.0 INTRODUCTION

1.1 BACKGROUND OF THE PROJECT

Today, three-phase induction motor drives are employed in different industrial areas with a wide power range starting from few 100W to several MW. Drives industry is very thankful to the present generation of powerful microprocessors, which is responsible for the realization of control functions within short cost margins. However, even today, cost of controller hardware is a limiting constraint, particularly at low-power and low performance drives. Main market share of about 80-90% are simple drives with low dynamic requirements like pumps and fans. All these drives are working without speed sensors. The control principle is still based on v/f control. In this segment there is no need to introduce more powerful controls. Consequently, most industrial drive applications employ induction motors. Unfortunately, the speed of an induction motor cannot be continuously varied without additional expensive equipment. High-performance control of an induction motor is more difficult than D.C. motors, because the induction motor is inherently a dynamic, recurrent, and nonlinear system. Induction motor control problems have attracted the attention of researchers for many years. Most of the earlier researches are based on classical control theory and electric machine theory, using precise mathematical models of the induction motor. As shown in Fig.1, an induction motor control system consists of the controller, sensors, inverter and the induction motor. It can be seen that a study of induction motor control involves three main electrical engineering areas: control, power electronics, and electrical machines (Bose, 1981). The induction motor can be described by a fifth order nonlinear differential equation with two inputs and three state variables. (Marino and Tomei, 1995).The control task is further complicated by the fact that the induction motor is subject to unpredictable disturbances (such as noise and load changes) and there are uncertainties in machine parameters. Induction motor control has constituted a theoretically interesting and practically important class of nonlinear systems, and is evolving into a benchmark example for nonlinear control. The ARTIFICIAL INTELLIGENCE (AI) techniques, such as Expert System (ES), Fuzzy Logic (FL), Artificial Neural Network (ANN or NNW), and Genetic Algorithm (GA) have recently been applied widely in power electronics and motor drives. The history of NNW goes back to 1940s, but its advancement was camouflaged by the glamorous evolution of modern-day digital computers. From the beginning of 1990s, the NNW technology captivated the attention of a large segment of scientific community. Since then, the technology has been advancing rapidly and its applications are expanding in different areas. The GA theory (also known as evolutionary computation) was proposed in 1970s and it is based on principles of genetics (or Darwin’s survival of the fittest theory of evolution). Basically, it solves optimization problem by an evolutionary process resulting in a best (fittest) solution (survivor). Lofty Zadeh, the inventor of FL, defined ES as hard or precise computing and FL, NNW and GA as soft or approximate computing [1-2]

Fig. 1 An Induction Motor Control System

1.2 PROBLEM STATEMENT

Traditionally, motor operation which includes motor speed and direction control is always initiated by human being, which involves much labour, consumes time. In order to solve this problem, artificial intelligence is used. artificial intelligence helps in solving our problems that are difficult to solve by traditional methods.

1.3 AIM AND OBJECTIVE OF THE PROJECT

The main aim of this work is to discuss the stage for the development of artificial intelligent based induction motor drives. At the end of this work, this work shall be able to give sufficient details to acquaint readers with their basic principles, strengths and limitations of using artificial intelligence in motor speed control.

1.4 PURPOSE OF THE PROJECT

The main purpose of this work is to make motor operation fast and easy for home and industrial purposes.

1.5 SCOPE OF THE PROJECT

The goal of AI is to plant human or natural intelligence in a computer so that a computer can think intelligently like a human being. A system with embedded computational intelligence is often defined as an “intelligent system” that has “learning”, “self-organizing”, or “self-adapting” capability. Computational intelligence has been debated for a long time, and will possibly be debated forever. However, there is no deny in the fact that computers can have adequate intelligence to help solving our problems that are difficult to solve by traditional methods. Therefore, it is true that AI techniques are now being extensively used in industrial process control, image processing, diagnostics, medicine, space technology and information management system and just to name a few. While Expert Systems (ES) and FL are rule-based, and tend to emulate the behavioral nature of human brain, the NNW is more generic in nature that tends to emulate the biological neural network directly.