Electronic Instrumentation for Distributed Generation and Power Processes

The goal of the book is to provide basic and advanced knowledge of design, analysis, and circuit implementation for electronic instrumentation and clarify how to get the best out of the analog, digital, and computer circuitry design steps. The reader will learn the physical fundamentals guiding the electrical and mechanical devices that allow for a modern automation and control system, which are widely comprised of computers, electronic instrumentation, communication loops, smart grids, and digital circuitry. It includes practical and technical data on electronic instrumentation with respect to efficiency, maximum power, and applications. Additionally, the text discusses fuzzy logic and neural networks and how they can be used in practice for electronic instrumentation of distributed generation, smart grids, and power systems.

Autorentext

Felix Alberto Farret was born in Santa Maria, RS, Brazil. He received his Bachelor and Master degrees in Electrical Engineering from the Federal University of Santa Maria, in 1972 and 1976, respectively. He was a specialist in electronic instrumentation at Osaka Prefectural Industrial Research Institute, Japan, in 1975. Later, he received his MSc from the University of Manchester, UMIST, England in 1981, and then his PhD in Electrical Engineering from the University of London, Imperial College, England in 1984. He followed a Postdoctoral program in alternative energy sources in the Colorado School of Mines, USA, 2003. He worked as an operation and maintenance engineer at the State Electric Power Company (CEEE), RGS, Brazil in 1973-1974. He was a visiting professor at the Colorado School of Mines in the Division of Engineering, USA in 2002-2003. Dr. Farret has published several books on electrical power sources of energy. Currently, he is a Full Professor of Electronic Instrumentation and Small Renewable Sources of Energy in the Department of Electrical Energy Processing, Federal University of Santa Maria, Brazil. His interests are mostly related to industrial applications in integration, sizing and location of alternative power sources for distribution and industrial systems including stacks of fuel cells, hydro generators, wind, photovoltaic, geothermal energy storage applications in batteries and other power converter systems. Marcelo Godoy Simões received a B.Sc. degree from University of São Paulo, Brazil, a M.Sc. degree from University of São Paulo, Brazil, and a Ph.D. degree from The University of Tennessee, USA in 1985, 1990 and 1995 respectively. He received his D.Sc. degree (Livre-Docência) from the University of São Paulo in 1998. Dr. Simões was a US Fulbright Fellow for AY 2014-15, working for Aalborg University, Institute of Energy Technology (Denmark). He has been elevated to the grade of IEEE Fellow, Class of 2016, with the citation: "for applications of artificial intelligence in control of power electronics systems." He is with Colorado School of Mines since 2000, and is currently a Visiting-Professor with Petroleum Institute, in Abu Dhabi (UAE), from January to December of 2016. Danilo Iglesias Brandao was born in São Gonçalo do Sapucai, Brazil. He received his M.Sc. degree from Universidade EstadualPaulista, Brazil in 2013, and a Ph.D. degree from University of Campinas, Brazil in 2015-both in electrical engineering. He was a FAPESP scholarship student from 2008 to 2015, a visiting researcher at Colorado School of Mines, USA, in 2009 and in 2013, and at University of Padova, Italy, in 2014. He is currently an Associate Professor of Electronics with the Department of Electrical Energy (DEE), Federal University of Minas Gerais. His main interests are power filter, power quality, photovoltaic power system, distributed energy resource and microgrid. Dr. Brandao is a member of IEEE and SOBRAEP

Inhalt

Chapter 1 Computer Interface and Instrumentation Electronics

1.1 Introduction

1.2 Processing of Analog Signals

1.3 The Error Equation

1.4 Large-Signal Amplifiers

Exercises

References

Chapter 2 Analog-Based Instrumentation Systems

2.1 Introduction

2.2 Instrumentation Amplifier for Customized Purposes

2.3 Voltage-to-Current Conversion (V-to-I)

2.4 Electronic Signal Processing

2.5 Signal Conditioning

2.6 Hints to Implement Circuit Boards for Signal Conditioning

Exercises

References

Chapter 3 Sensors and Transducers

3.1 Introduction

3.2 Passive Electric Sensors

3.3 Active Electric Sensors

3.4 Mechanical Sensors

Exercises

References

Chapter 4 Electronic Instruments for Electrical Engineering

4.1 Introduction

4.2 Instrument in Direct Current with Amplifiers

4.3 Common Circuits Used in Instrumentation

4.4 Electronic Multimeters

4.5 General Classification of DVMs

4.6 Ohmmeters and Megometers

4.7 Digital Network Analyzers

4.8 Power Sources and Adapters

Exercises

References

Chapter 5 Signal Simulators and Emulators

5.1 Introduction

5.2 Waveforms for Electronic Instruments

5.3 Signal Generators and Simulators with Frequency Synthesizer

5.4 Signal Generators by Frequency Division

5.5 Signal Generator with Modulation

5.6 Frequency Sweeping Generator

5.7 Pulse Generators and Rectangular Waves

5.8 Function Generator, Simulator, and Audio Generator

Exercises

References

Chapter 6 Advanced Harmonic Analysis for Power Systems

6.1 Introduction

6.2 Harmonic Analysis

6.3 Harmonic Distortion Analyzers

6.4 Spectrum Analyzers

Exercises

References

Chapter 7 Instrumentation and Monitoring for Distributed Generation Systems

7.1 Introduction

7.2 General Control Scheme for Distributed Generation

7.3 Signal Reference Generator

7.4 Power Quality Standards Applied to DG

7.5 Distributed Generator Based on Instrumentation: Study Cases

Exercises

References

Chapter 8 Fuzzy Logic and Neural Networks for Distributed Generation Instrumentation

8.1 Introduction

8.2 Applications of Artificial Neural Network in Industrial Systems, Energy Conversion, and Power Systems

8.3 Fuzzy Logic and Neural Network Controller Design

8.4 Fuzzy Logic and Neural Network Function Optimization

8.5 Fuzzy Logic and Neural Network Function Approximation

References

Chapter 9 Instruments for Data Acquisition

9.1 Data Acquisition by Computers

9.2 Signal Processors for Instrumentation

9.3 Computer-Based Systems Instrumentation

9.4 Digital Control

Exercises

References

Chapter 10 Software for Electric Power Instrumentation

10.1 Introduction

10.2 LabVIEW Development System

10.3 Arduino Development System

10.4 Mathworks MATLAB®/Simulink® Development System

Exercises

References

Chapter 11 Introduction to Smart Grid Systems

11.1 Introduction

11.2 Distribution System Automation

11.3 Advanced Metering Infrastructure

11.4 Smart Home

11.5 Information and Communication Technologies

11.6 Electric Vehicles (EV) in Smart Grid Systems

11.7 A Smart Operation Example

References