E-raamat: Electronic Instrumentation for Distributed Generation and Power Processes

(Federal University of Minas Gerais, Brazil), (Federal University of Santa Maria, Brazil), (University of Vaasa, Finland.)
  • Formaat: 303 pages
  • Ilmumisaeg: 16-Aug-2017
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781351651479
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Electronic Instrumentation for Distributed Generation and Power ProcessesSuurem pilt
  • Formaat: 303 pages
  • Ilmumisaeg: 16-Aug-2017
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781351651479
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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.
Foreword xiii
Preface xv
Acknowledgments xvii
Authors xix
Chapter 1 Computer Interface and Instrumentation Electronics 1(24)
1.1 Introduction
1(8)
1.1.1 Operational Amplifiers
1(1)
1.1.2 Gains of Differential Amplifiers
2(2)
1.1.3 Calculation of Ad
4(1)
1.1.4 Calculation of Ac
5(1)
1.1.5 Calculation of Common-Mode Rejection Ratio (CMRR)
6(1)
1.1.6 Current Mirror Circuit
6(1)
1.1.7 Parameters of Differential Amplifiers
7(2)
1.2 Processing of Analog Signals
9(8)
1.2.1 Inverting Amplifier
9(1)
1.2.2 Summing Amplifier for Inverting Variables
10(1)
1.2.3 Integration Amplifier
11(1)
1.2.4 Derivative Amplifier
12(1)
1.2.5 Noninverting Amplifier
13(1)
1.2.6 Buffer Amplifier
14(1)
1.2.7 Level Detector or Comparator
14(1)
1.2.8 Precision Diode, Precision Rectifier, and Absolute-Value Amplifier
15(1)
1.2.9 High-Gain Amplifier with Low-Value Resistors
16(9)
1.2.9.1 Example of a High-Gain Amplifier with Low-Value Resistors
17(1)
1.3 The Error Equation
17(3)
1.4 Large-Signal Amplifiers
20(1)
Exercises
21(2)
References
23(2)
Chapter 2 Analog-Based Instrumentation Systems 25(36)
2.1 Introduction
25(5)
2.1.1 Voltage Feedback for Differential Amplifiers
25(2)
2.1.2 Standard Differential Amplifier for Instrumentation
27(2)
2.1.3 Programmable Gain Amplifier
29(1)
2.2 Customized Instrumentation Amplifier
30(4)
2.2.1 Isolated Instrumentation Amplifier
30(1)
2.2.2 Magnetic Isolation
30(1)
2.2.3 Optical Isolation
31(1)
2.2.4 Capacitive Isolation
32(2)
2.3 Voltage-to-Current Conversion (V-to-I)
34(4)
2.3.1 Single-Input V-to-I Converter with Ungrounded Load
34(1)
2.3.2 Single-Input V-to-I Converter Load with Grounded Load
34(2)
2.3.3 V-to-I Converter with Differential Input and Ungrounded Load
36(1)
2.3.4 V-to-I Converter with Differential Input and Grounded Load
37(1)
2.4 Analog Signal Processing
38(11)
2.4.1 Signal Linearization
38(1)
2.4.2 Phase-Shift Amplifier
39(2)
2.4.3 Schmitt Trigger Comparator
41(1)
2.4.4 Oscillators for Signal Generators
42(7)
2.4.4.1 Wien Oscillator
43(2)
2.4.4.2 VCO Oscillator
45(1)
2.4.4.3 Relaxation Oscillator
45(2)
2.4.4.4 Crystal Oscillators
47(1)
2.4.4.5 Types of Crystal Oscillators
48(1)
2.4.5 Automatic Gain Control (AGC)
49(1)
2.5 Signal Conditioning
49(8)
2.5.1 Digital-to-Analog Conversion
51(2)
2.5.2 Analog-to-Digital Conversion
53(4)
2.6 Working With Circuit Boards for Signal Conditioning
57(1)
Exercises
58(1)
References
59(2)
Chapter 3 Sensors and Transducers 61(36)
3.1 Introduction
61(1)
3.2 Passive Electric Sensors
62(13)
3.2.1 Resistive Sensors
63(1)
3.2.2 Resistive Sensors and Effects of Temperature on Measurements
63(3)
3.2.3 Compensation of External Influences on Resistive Sensors
66(3)
3.2.4 Capacitive Sensors
69(3)
3.2.5 Inductive Sensors
72(3)
3.3 Active Electric Sensors
75(12)
3.3.1 Thermocouples
75(3)
3.3.2 Piezoelectric Sensors
78(1)
3.3.3 Photovoltaic Sensors
78(1)
3.3.4 Hall Effect Device
79(2)
3.3.5 Photomultipliers and Image Intensifiers
81(2)
3.3.6 Geiger-Muller Tube and Proportional Counter
83(1)
3.3.7 Probes for Biological and Chemical Voltages
84(2)
3.3.8 Alcohol Sensors Used in Blood Measurements
86(1)
3.3.9 Other Magnetic Sensors
87(1)
3.4 Mechanical Sensors
87(8)
3.4.1 Elastic Sensors
87(1)
3.4.2 Pneumatic Sensors
87(1)
3.4.3 Differential Pressure Sensors
88(1)
3.4.4 Turbine Sensors
89(1)
3.4.5 Rotation Sensors
89(1)
3.4.6 Shaft Encoders (Rotation or Position)
90(5)
Exercises
95(1)
References
96(1)
Chapter 4 Electronic Instruments for Electrical Engineering 97(22)
4.1 Introduction
97(1)
4.2 Instrument in Direct Current with Amplifiers
97(2)
4.2.1 Standard DC Electronic Voltmeter
97(1)
4.2.2 Electronic Voltmeter-Ammeter
98(1)
4.3 Common Circuits Used in Instrumentation
99(4)
4.3.1 Operating Principle of the Chopper Amplifier
99(1)
4.3.2 Alternating Current Voltmeters with Rectifier
100(2)
4.3.3 Voltmeters of True Effective Value (Vtrue-rms)
102(1)
4.4 Electronic Multimeters
103(2)
4.4.1 General Characteristics of the Analog Voltmeters
103(2)
4.5 General Classification of DVMs
105(5)
4.5.1 Standard Single-Slope or Ramp Voltmeter
105(1)
4.5.2 Dual-Slope Voltmeter
106(2)
4.5.3 Continuous Balance Voltmeter
108(1)
4.5.4 Voltmeter of Successive Approximations
109(1)
4.5.5 General Characteristics of a DVM
110(1)
4.6 Ohmmeters and Megometers
110(3)
4.7 Digital Network Analyzers
113(2)
4.7.1 Analog-to-Digital Network Analyzers
113(2)
4.8 Power Sources and Adapters
115(1)
Exercises
116(1)
References
117(1)
Bibliography
117(2)
Chapter 5 Signal Simulators and Emulators 119(16)
5.1 Introduction
119(3)
5.1.1 The Attenuator
119(1)
5.1.2 Types of Attenuators
120(2)
5.2 Waveforms for Electronic Instruments
122(1)
5.3 Signal Generators and Simulators with Frequency Synthesizer
123(5)
5.3.1 Indirect Method of Frequency Synthesis
123(1)
5.3.2 Phase-Locked Loop
124(3)
5.3.3 Direct Method of Frequency Synthesis
127(1)
5.4 Signal Generators by Frequency Division
128(1)
5.5 Signal Generator with Modulation
129(1)
5.6 Frequency Sweeping Generator
129(2)
5.7 Pulse Generators and Rectangular Waves
131(1)
5.8 Function Generators, Simulators, and Audio Generators
131(1)
Exercises
131(1)
References
132(3)
Chapter 6 Advanced Harmonic Analysis for Power Systems 135(22)
6.1 Introduction
135(1)
6.2 Harmonic Analysis
136(11)
6.2.1 Effects of Harmonic Distortions on Network Parameters
139(5)
6.2.1.1 Instantaneous Active Power, pa
142(1)
6.2.1.2 Instantaneous Nonactive Power, pna
143(1)
6.2.1.3 Active or Average Power, P
143(1)
6.2.2 Effects of Harmonic Distortion on the Power Factor
144(3)
6.3 Harmonic Distortion Analyzers
147(2)
6.3.1 Harmonic Analyzer with a Tuned Filter
147(1)
6.3.2 Heterodyne Harmonic Analyzer or Wave Distortion Meter
148(1)
6.3.3 Determination of THD
149(1)
6.4 Spectrum Analyzers
149(6)
6.4.1 Spectrum Analyzers by Frequency Scanning
150(3)
6.4.2 Mathematical Analysis of Power Signals
153(2)
Exercises
155(1)
References
156(1)
Bibliography
156(1)
Chapter 7 Instrumentation and Monitoring for Distributed Generation Systems 157(22)
7.1 Introduction
157(2)
7.2 General Control Scheme for Distributed Generation
159(2)
7.2.1 Current-Based DG
160(1)
7.2.2 Voltage-Based DG
160(1)
7.3 Signal Reference Generator
161(5)
7.3.1 Fundamental Frequency Terms
161(3)
7.3.1.1 Generator of the In-Phase Reference Term
163(1)
7.3.1.2 Quadrature Reference Term Generator
163(1)
7.3.2 Nonfundamental Frequency Terms
164(2)
7.4 Power Quality Standards Applied to DG
166(3)
7.4.1 Harmonic Circulation
166(1)
7.4.2 Power Factor and THD Concerns in DG
167(2)
7.5 Distributed Generator Based on Instrumentation: Case Studies
169(5)
7.5.1 Instrumentation of DO-Based PV
169(1)
7.5.2 Instrumentation of DO-Based Smart Inverter: Voltage-Based DG
169(1)
7.5.3 Instrumentation of DG-Based Automonitoring Inverter: Current-Based DG
170(4)
Exercises
174(3)
References
177(2)
Chapter 8 Fuzzy Logic and Neural Networks for Distributed Generation Instrumentation 179(24)
8.1 Introduction
179(2)
8.1.1 Fuzzy Logic Systems
180(1)
8.1.2 Neural Network Systems
181(1)
8.2 Applications of Artificial Neural Network in Industrial Systems, Energy Conversion, and Power Systems
181(2)
8.2.1 Applications of Fuzzy Logic and Neural Networks in Distributed Generation Systems
182(1)
8.3 Fuzzy Logic and Neural Network Controller Design
183(6)
8.4 Fuzzy Logic and Neural Network Function Optimization
189(8)
8.5 Fuzzy Logic and Neural Network Function Approximation
197(3)
References
200(1)
Bibliography
200(3)
Chapter 9 Instruments for Data Acquisition 203(32)
9.1 Data Acquisition by Computers
203(7)
9.1.1 Fundamentals of Digital Signal Processing
203(3)
9.1.2 The Data Acquisition Board
206(1)
9.1.3 Microcontrollers
207(1)
9.1.4 Example of Data Acquisition
208(2)
9.2 Signal Processors for Instrumentation
210(4)
9.2.1 Digital Signal Processors
210(1)
9.2.2 Parallel Processing
211(1)
9.2.3 Transputers
212(1)
9.2.4 Design of Instruments with ASIC
213(1)
9.2.5 FPGA (Field-Programmable Gate Arrays) in Instrumentation
213(1)
9.2.6 PLDs (Programmable Logic Devices) in Instrumentation
214(1)
9.3 Computer-Based Systems Instrumentation
214(13)
9.3.1 Essential Components
214(1)
9.3.2 Tests Operated by Computer
214(1)
9.3.3 The Parallel Interface IEEE488
215(2)
9.3.4 Example
217(1)
9.3.4.1 Frequency Counter (Hardware)
217(1)
9.3.5 Input and Output Drivers as Open Collectors (Transceiver)
217(1)
9.3.6 Output Drivers
218(1)
9.3.7 Serial Interface IEEE485
219(2)
9.3.8 USB Connectors
221(4)
9.3.8.1 USB Hub
222(1)
9.3.8.2 USB Process
223(1)
9.3.8.3 USB Features
224(1)
9.3.9 Signals of Data Transmission
225(1)
9.3.10 RS485 Network Biasing
226(1)
9.3.11 Calculation of Biasing Resistors
227(1)
9.4 Digital Control
227(6)
9.4.1 Instrumentation Controlled by Computer
228(1)
9.4.2 Programmable Logic Controller
228(5)
Exercises
233(1)
References
234(1)
Chapter 10 Software for Electric Power Instrumentation 235(22)
10.1 Introduction
235(1)
10.2 LabVIEW Development System
235(12)
10.2.1 Programming with LabVIEW
238(1)
10.2.2 Virtual Instrument for Power Quality Analysis
239(3)
10.2.3 Electric Power Instrumentation for Distributed Generation
242(5)
10.3 Arduino Development System
247(1)
10.4 Mathworks MATLAB®/Simulink® Development System
248(7)
10.4.1 Interface with LabVIEW
249(1)
10.4.2 Interface with Arduino
250(2)
10.4.2.1 MATLAB® Support Package for Arduino
251(1)
10.4.2.2 Simulink® Support Package for Arduino
251(1)
10.4.3 Interface with PSIM
252(1)
10.4.4 Programming DSP through MATLAB®/Simulink®
253(2)
Exercises
255(1)
References
256(1)
Chapter 11 Introduction to Smart Grid Systems 257(20)
Luciane N. Canha
Alzenira R. Abaide
Daniel P. Bernardon
11.1 Introduction
257(2)
11.2 Distribution System Automation
259(1)
11.3 Advanced Metering Infrastructure
260(2)
11.4 Smart Home
262(1)
11.5 Information and Communication Technologies
262(3)
11.5.1 Standard 61850
263(1)
11.5.2 Standard 62351
263(1)
11.5.3 Attacks on SCADA Systems
264(1)
11.6 Electric Vehicles (EV) in Smart Grid Systems
265(1)
11.7 A Smart Operation Example
266(7)
11.7.1 Methodology for Automatic Restoration of a Power Supply
266(2)
11.7.2 Objective Functions and Constraints
268(1)
11.7.2.1 Objective Functions
268(1)
11.7.2.2 Constraints
268(1)
11.7.3 Multicriteria Decision-Making Method
269(2)
11.7.4 Application of AHP Method
271(2)
References
273(4)
Index 277
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 2015both 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
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