Bhuvaneswari / Saxena Intelligent and Efficient Electrical Systems

Selected Proceedings of ICIEES'17
1. Auflage 2018
ISBN: 978-981-10-4852-4
Verlag: Springer Nature Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 446, 276 Seiten

Reihe: Lecture Notes in Electrical Engineering

This book presents selected papers from International Conference on Intelligent and Efficient Electrical Systems (ICIEES'17). The volume brings together content from both industry and academia. The book focuses on energy efficiency in electrical systems and covers en trende topics such as control of renewable energy systems. The collaborative industry-academia perspective of the conference ensures that equal emphasis is laid on novel topics and practical applications. The contents of this volume will prove useful to researchers and practicing engineers alike.

Dr. M C Bhuvaneswari received her B.E. degree in Electronics and Communication Engineering from the Government College of Technology, Coimbatore, in 1985; M.E. degree in Applied Electronics in 1989 and Ph.D. degree in the area of Very Large Scale Integration (VLSI) testing in 2002 from PSG College of Technology, Coimbatore. She is currently an associate professor at the Department of Electrical and Electronics Engineering at the same college. Her research interests include VLSI testing, VLSI CAD, embedded system design, evolutionary algorithms and multi-objective optimization. She has published 65 papers on these topics in journals and conferences. She has 26 years of teaching experience and was the recipient of the 2010 Dakshinamoorthy Award for Teaching Excellence, instituted by PSG College of Technology. Dr. Jayashree Saxena received her Ph.D in Electrical and Computer Engineering from the University of Massachusetts, Amherst in 1993. She is currently a business unit manager at Anora LLC in Plano, Texas where she is responsible for design-for-test (DFT) services. Prior to that, she worked at Texas Instruments, Dallas from 1993 to 2013, where she served in various roles ranging from management to technical leadership in the DFT area. She received the most Significant Paper award at the International Test Conference (ITC) 2013 for the ITC 2003 paper 'A Case-study of IR drop in Structured At-Speed Testing'. She also served on the Technical Program Committee of ITC from 2005 to 2013.

Autoren/Hrsg.

Bhuvaneswari, M. C.

Saxena, Jayashree

Inhaltsverzeichnis

1;Preface;6
2;Organizing Committee;9
2.1;Chief Patron;9
2.2;Patron;9
2.3;Convener;9
2.4;Organizing Secretary;9
2.5;Joint Organizing Secretaries;9
2.6;Coordinators;10
3;Contents;11
4;Editors and Contributors;14
5;1 Multi-Objective Optimization of Stand-alone Renewable Energy Hybrid System;18
5.1;Abstract;18
5.2;1 Introduction;18
5.3;2 Simulation and Optimization;19
5.4;3 Mathematical Model;21
5.4.1;3.1 Load Profile;21
5.4.2;3.2 Current from the PV Generator;21
5.4.3;3.3 Current from Wind Turbine;22
5.4.4;3.4 Batteries;22
5.4.5;3.5 Inverter;23
5.5;4 Solar and Wind Data;23
5.6;5 Objective Functions;24
5.6.1;5.1 Cost of Supplying Energy with Batteries;25
5.6.2;5.2 Cost of Supplying Energy with Diesel Generator;25
5.6.3;5.3 Unmet Load;25
5.7;6 Hybrid System Components;26
5.8;7 Results and Discussions;27
5.9;8 Conclusions;29
5.10;Acknowledgements;29
5.11;References;29
6;2 Wind Farm Power Prediction Based on Wind Speed and Power Curve Models;31
6.1;Abstract;31
6.2;1 Introduction;31
6.3;2 Wind Speed Prediction Model;32
6.3.1;2.1 NAR and NARX Models for Wind Speed;33
6.3.1.1;2.1.1 Model Incorporating Wind Direction;33
6.3.1.2;2.1.2 Model Incorporating Annual Trends;33
6.4;3 Wind Turbine Power Curve Model;34
6.4.1;3.1 Parametric Models;34
6.4.2;3.2 Nonparametric Models;34
6.5;4 Modeling Techniques;35
6.6;5 Results and Analysis;35
6.6.1;5.1 Experimental Data;35
6.6.2;5.2 Results of Wind Speed and Power Prediction Models;36
6.6.2.1;5.2.1 Performance of Wind Speed Prediction Models;36
6.6.2.2;5.2.2 Performance of Wind Power Prediction Models;37
6.6.3;5.3 Analysis of Results;37
6.7;6 Conclusion;39
6.8;References;39
7;3 Integration of Wind Power Generators for the Enhancement of Profit by Optimal Allocation of SVC;41
7.1;Abstract;41
7.2;1 Introduction;41
7.3;2 Modelling of SVC;42
7.3.1;2.1 Static VAR Compensator (SVC);42
7.4;3 Problem Formulation;43
7.4.1;3.1 Objective Function;43
7.4.2;3.2 Equality Constraints;44
7.4.3;3.3 Inequality Constraint;44
7.5;4 Grey Wolf Optimizer (GWO);45
7.5.1;4.1 Algorithm;45
7.6;5 Results and Discussions;46
7.6.1;5.1 Tools and Test Systems;46
7.6.2;5.2 IEEE 6 Bus System;47
7.6.3;5.3 Case 1: Without SVC and WPG in the System;47
7.6.4;5.4 Case 2: With SVC and WPG in the System;47
7.7;6 Conclusion;49
7.8;References;50
8;4 Block-Random Access Memory-Based Digital Pulse Modulator Architecture for DC–DC Converters;51
8.1;Abstract;51
8.2;1 Introduction;52
8.3;2 RAM-based DPWM/DPFM Architechture;53
8.3.1;2.1 Digital Pulse-Width Modulator;54
8.3.2;2.2 Digital Pulse-Frequency Modulator;54
8.3.3;2.3 Generation of PWM and PFM Signals;56
8.4;3 Results and Discussion;57
8.4.1;3.1 Simulation Results;57
8.5;4 Experimental Results;58
8.6;5 Conclusion and Future Work;60
8.7;References;60
9;5 Fractional-Order Controller Design and Analysis of SEPIC Converter;62
9.1;Abstract;62
9.2;1 Introduction;62
9.3;2 Principle of SEPIC;63
9.3.1;2.1 Modes of Operation;64
9.3.1.1;2.1.1 Mode I (0  lessthan  t  lessthan  D);64
9.3.1.2;2.1.2 Mode II (D  lessthan  t  lessthan  (1 ? D));65
9.4;3 State-Space Modeling of SEPIC;66
9.5;4 Design of PI Controller;69
9.5.1;4.1 Simulation Results of PI Controller;70
9.5.1.1;4.1.1 Reference Voltage Variation;70
9.5.1.2;4.1.2 Supply Voltage Variation;71
9.5.1.3;4.1.3 Load Resistance Variation;72
9.6;5 Design of Fractional-Order PI Controller;72
9.6.1;5.1 GA with FOPI Controller;73
9.6.2;5.2 Simulation Results Using FOPI Controller;74
9.6.2.1;5.2.1 Reference Voltage Variation of FOPI Controller;75
9.6.2.2;5.2.2 Supply Voltage Variation;75
9.6.2.3;5.2.3 Load Resistance Variation;76
9.7;6 Transient Analysis;76
9.8;7 Conclusion;77
9.9;References;77
10;6 Advanced Energy Management of a Micro-grid Using Arduino and Multi-agent System;79
10.1;Abstract;79
10.2;1 Introduction;80
10.3;2 Multi-agent System Approach for Autonomous Energy Management of Micro-grid;81
10.3.1;2.1 Multi-agent System;81
10.4;3 Problem Formulation;82
10.5;4 Implementation;82
10.5.1;4.1 Flow Chart;82
10.5.2;4.2 Arduino Serial Communication Using RXTX Library;84
10.6;5 Simulations and Results;85
10.7;6 Conclusion;89
10.8;References;89
11;7 Sustain the Critical Load in Blackout Using Virtual Instrumentation;91
11.1;Abstract;91
11.2;1 Introduction;91
11.3;2 Proposed System;92
11.4;3 Block Diagram;93
11.5;4 LabVIEW Front Panel;93
11.6;5 LabVIEW Block Diagram Panel;94
11.7;6 Simulation Result;95
11.7.1;6.1 Normal State;95
11.7.2;6.2 Restoration State;96
11.8;7 Result Discussion;97
11.9;8 Fuzzy Logic Control System for Sustaining the Critical Load in Blackout Restoration;98
11.9.1;8.1 Fuzzy Input and Output Variables;98
11.9.2;8.2 Fuzzy Rules;98
11.9.3;8.3 Simulation Result: Test System;100
11.10;9 Conclusion;101
11.11;References;101
12;8 Optimal Single and Multiple DG Installations in Radial Distribution Network Using SLPSO Algorithm;103
12.1;Abstract;103
12.2;1 Introduction;103
12.3;2 Problem Formulation;104
12.3.1;2.1 Objective Function;104
12.3.2;2.2 Technical Constraints;105
12.4;3 Implementation of SLPSO Algorithm;105
12.5;4 Results and Discussion;107
12.6;5 Conclusion;109
12.7;Acknowledgements;110
12.8;References;110
13;9 Dynamic Modeling and Control of Utility-Interactive Microgrid Using Fuzzy Logic Controller;111
13.1;Abstract;111
13.2;1 Introduction;111
13.3;2 System Configuration and Modeling;112
13.4;3 Control of DG Units and Power Converters;113
13.4.1;3.1 Control of PV System;113
13.4.2;3.2 Control of SOFC System;114
13.5;4 Control of VSC Using FLC;115
13.6;5 Results and Discussions;117
13.6.1;5.1 Case 1: Sudden Increase in Resistive Load;117
13.6.2;5.2 Case 2: During Sudden Change in Irradiance;118
13.7;6 Conclusion;119
13.8;References;119
14;10 A Novel Method of Power Quality Improvement in BLDC Motor Using Cascaded H-Bridge MLI Topology;121
14.1;Abstract;121
14.2;1 Introduction;121
14.3;2 Methodology;122
14.4;3 Modeling and Simulation;123
14.5;4 Results and Discussion;125
14.6;5 Conclusion;128
14.7;References;129
15;11 Tuning of Fractional Order Proportional Integral Derivative Controller for Speed Control of Sensorless BLDC Motor using Artificial Bee Colony Optimization Technique;130
15.1;Abstract;130
15.2;1 Introduction;130
15.3;2 Speed Control of Sensorless BLDC Motor Drives;131
15.3.1;2.1 Mathematical Model of BLDC Motor;132
15.3.2;2.2 Sensorless BLDC Motor;132
15.4;3 Optimum Tuning of Fractional-Order PID Controller;134
15.4.1;3.1 Fractional-Order PID Controller;134
15.4.2;3.2 Optimal Tuning of FOPID Controller;135
15.5;4 Proposed ABC-Based FOPID Controller;136
15.6;5 Results and Discussion;137
15.7;6 Conclusion;139
15.8;References;140
16;12 Torque Ripple Minimization of a FOC-Fed PMSM with MRAS Using Popov’s Hyper-Stability Criterion;141
16.1;Abstract;141
16.2;1 Introduction;141
16.3;2 Field-Oriented Control of Permanent Magnet Synchronous Motor;142
16.4;3 Implementation of FOC for PMSM Drive with MRAS;144
16.5;4 Results and Discussion;146
16.5.1;4.1 Analysis of Speed Response;146
16.5.2;4.2 Analysis of Torque Response;147
16.5.3;4.3 Analysis of Three-Phase Stator Currents;149
16.5.4;4.4 Analysis of Stator Flux Response;150
16.5.5;4.5 Dynamic Performance of PMSM;150
16.6;5 Conclusion;154
16.7;References;154
17;13 Effectual Particle Swarm Optimization Algorithm for the Solution of Non-convex Economic Load Dispatch Problem;155
17.1;Abstract;155
17.2;1 Introduction;155
17.3;2 Problem;156
17.3.1;2.1 Objective Function;156
17.3.2;2.2 Linear Constraints;157
17.3.2.1;2.2.1 Generation Capacity Constraint;157
17.3.2.2;2.2.2 Power Balance Constraint;157
17.3.3;2.3 Nonlinear Constraints;158
17.3.3.1;2.3.1 Generator Ramp Rate Limits;158
17.3.3.2;2.3.2 Prohibited Operating Zones;158
17.4;3 Proposed Method;159
17.4.1;3.1 Effectual Particle Swarm Optimization (EPSO) Algorithm;159
17.4.2;3.2 EPSO Algorithm Applied to ELD Problem;161
17.5;4 Result;162
17.6;5 Conclusion;163
17.7;References;163
18;14 Differential Evolution with Parameter Adaptation Strategy to Economic Dispatch Incorporating Wind;165
18.1;Abstract;165
18.2;1 Introduction;165
18.3;2 Economic Dispatch Incorporating Wind;167
18.3.1;2.1 Objective Function;167
18.3.2;2.2 Real Power Balance or Demand Constraint;167
18.3.3;2.3 Real Power Generating Limits;168
18.4;3 Proposed DE-PAS Algorithm;168
18.4.1;3.1 Initialization of Parameter Vectors;168
18.4.2;3.2 Mutation;169
18.4.3;3.3 Crossover;169
18.4.4;3.4 Selection;169
18.4.5;3.5 Terminating Condition;170
18.4.6;3.6 Adaption Techniques for Mutation Rate, F;170
18.4.7;3.7 Adaptation Technique for Crossover Rate, CR;171
18.5;4 Implementation of Proposed DE-PAS Algorithm to the Chosen Problem;171
18.6;5 Results and Discussion;172
18.7;6 Conclusion;175
18.8;References;176
19;15 Application of Cuckoo Search Algorithm in Deregulated Economic Load Dispatch;178
19.1;Abstract;178
19.2;1 Introduction;178
19.3;2 Problem Definition;179
19.4;3 Solution Methods Used;180
19.5;4 Results and Discussion;182
19.6;5 Conclusion;185
19.7;References;185
20;16 An Investigation of Small-Signal Stability of IEEE 14 Bus System with AVR, PSS and Performance Comparison with FACTS Devices;186
20.1;Abstract;186
20.2;1 Introduction;186
20.3;2 Methodology;187
20.3.1;2.1 Modeling and Analysis of SMIB System Connected with AVR;188
20.3.2;2.2 Modeling and Analysis of SMIB System Connected with AVR and PSS;189
20.3.3;2.3 Modeling and Analysis of SMIB System Connected with SVC;191
20.3.4;2.4 Modeling and Analysis of SMIB System Connected with UPFC;193
20.4;3 Conclusion;195
20.5;References;195
21;17 Investigation on the Properties of Natural Esters Blended with Mineral Oil and Pyrolysis Oil as Liquid Insulation for High Voltage Transformers;197
21.1;Abstract;197
21.2;1 Introduction;198
21.3;2 Samples Descriptions;199
21.4;3 Experimental Details;200
21.5;4 Results and Discussion;201
21.5.1;4.1 Breakdown Voltage;203
21.5.2;4.2 Flash Point;204
21.5.3;4.3 Viscosity;204
21.6;5 Conclusion;205
21.7;References;205
22;18 A Novel Approach to Using Energy-Efficient LED-Based Visible Light Communication in Hospitals;207
22.1;Abstract;207
22.2;1 Introduction;207
22.3;2 Li-Fi to Revolutionize Our Future;208
22.4;3 Network Capacity Evolution;208
22.5;4 Lighting Sources;209
22.6;5 Why VLC in Hospitals?;210
22.7;6 Proposed Architecture of VLCs in Hospital Applications;211
22.7.1;6.1 LED Lighting in MRI;211
22.7.2;6.2 LEDs in Operating Room/Examination Room;211
22.8;7 VLC Transmitter (Intensity Modulation);212
22.9;8 VLC Receiver (Direct Detection);212
22.10;9 Future Prospects;213
22.11;10 Conclusion;213
22.12;References;213
23;19 Implementation of Mesh Network Using Bluetooth Low Energy Devices;215
23.1;Abstract;215
23.2;1 Introduction;215
23.3;2 Prior Work;216
23.4;3 System Design;217
23.4.1;3.1 Block Diagram of Implementation;217
23.4.2;3.2 Schematic Diagram;217
23.4.3;3.3 Software Algorithm;218
23.4.4;3.4 Pseudocode;220
23.5;4 Experimental Results;220
23.5.1;4.1 Performance Metrics;220
23.5.1.1;4.1.1 Connection Interval;220
23.5.1.2;4.1.2 RSSI;221
23.5.1.3;4.1.3 Latency;221
23.6;5 Conclusion and Future Work;222
23.7;References;222
24;20 Online Static Security Assessment Module Using Radial Basis Neural Network Trained with Particle Swarm Optimization;224
24.1;Abstract;224
24.2;1 Introduction;224
24.3;2 Composite Security Index [1];225
24.4;3 Online Static Security Assessment Module Using PSO-Trained ANN;226
24.4.1;3.1 Radial Basis Neural Network Trained with PSO;226
24.5;4 Results and Discussion;228
24.6;5 Conclusion;232
24.7;References;233
25;21 Ocular Artifact Suppression in Single Trial EEG Using DWT-Combined ANC;234
25.1;Abstract;234
25.2;1 Introduction;234
25.3;2 Methodology;235
25.3.1;2.1 Experimental Setup;235
25.3.2;2.2 Denoising Process;235
25.3.3;2.3 Cancellation of Power-Line Interference;238
25.4;3 Discussion;239
25.5;References;239
26;22 Egomotion Estimation Using Background Feature Point Matching in OpenCV Environment;240
26.1;Abstract;240
26.2;1 Introduction;240
26.3;2 Algorithms;243
26.3.1;2.1 Gradient Projection Algorithm;243
26.3.2;2.2 Gabor Wavelet Transform;243
26.3.3;2.3 Feature Matching;244
26.3.4;2.4 RANSAC;244
26.4;3 Results and Discussion;245
26.5;4 Conclusions and Future Work;247
26.6;Acknowledgements;247
26.7;References;248
27;23 Performance Analysis of Wavelet Function Using Denoising for Clinical Database;250
27.1;Abstract;250
27.2;1 Introduction;250
27.3;2 Materials and Methods;251
27.4;3 Wavelet Transform;252
27.4.1;3.1 Haar Wavelets (haar);252
27.4.2;3.2 Daubechies Wavelets (db);253
27.4.3;3.3 Symlets Wavelets (sym);253
27.4.4;3.4 Coiflet Wavelet (coif);253
27.4.5;3.5 Bi-Orthogonal Wavelets (biro);253
27.4.6;3.6 Reverse Bi-Orthogonal (rbio);254
27.4.7;3.7 Discrete FIR Meyer Wavelet (dmey);254
27.5;4 Results and Discussion;254
27.5.1;4.1 Peak Signal-to-Noise Ratio (PSNR);254
27.5.2;4.2 Average Difference (AD);254
27.5.3;4.3 Structural Content (SC);255
27.5.4;4.4 Image Fidelity (IF);255
27.5.5;4.5 Normalized Correlation Coefficient (NK);255
27.5.6;4.6 Structural Similarity Index (SSIM);255
27.6;5 Conclusion;257
27.7;References;257
28;24 Influence of PWM Waveform on Breakdown in Twisted Pairs;259
28.1;Abstract;259
28.2;1 Introduction;259
28.3;2 Experimental Setup;260
28.4;3 Sample Preparation;261
28.5;4 Test Procedure;261
28.6;5 Results and Discussion;262
28.7;6 Conclusions;263
28.8;References;263
29;25 Diagnosis of Cardiovascular Diseases (CVD) Using Medical Images;265
29.1;Abstract;265
29.2;1 Introduction;265
29.3;2 Existing Methods;266
29.4;3 Methodology;267
29.4.1;3.1 Recording of Ultrasound Images;267
29.4.2;3.2 Proposed Methodology;267
29.4.2.1;3.2.1 Image Enhancement;267
29.4.3;3.3 De-speckling;267
29.4.3.1;3.3.1 Trimmed Average Filter;268
29.4.3.2;3.3.2 Median Filter;269
29.4.3.3;3.3.3 Wiener Filter;269
29.4.3.4;3.3.4 Lee Filter;269
29.4.3.5;3.3.5 Kuan Filter;269
29.4.3.6;3.3.6 SRAD Filter;269
29.4.3.7;3.3.7 Kalman Filter;270
29.4.4;3.4 Image Quality Assessment;270
29.4.5;3.5 Segmentation;271
29.4.5.1;3.5.1 Otsu’s Method;271
29.4.6;3.6 Morphological Operations;272
29.4.7;3.7 Classifier;272
29.4.7.1;3.7.1 SVM Classifier;272
29.4.7.2;3.7.2 RBF Classifier;272
29.5;4 Results;273
29.6;5 Conclusion and Future Work;275
29.7;References;276