Page 8 - Decision Making Applications in Modern Power Systems

viii Contents

6.2.2 Adaptive filtering algorithms for power quality
estimation 158
6.2.3 Sparse model based adaptive filters 160
6.2.4 FPGA implementation of adaptive filters used in
power quality estimation 161
6.2.5 Simulation results and discussion 162
6.3 Methodologies for feature extraction and classification
of power quality disturbances 163
6.3.1 Empirical mode decomposition 164
6.3.2 Hilbert transform 164
6.3.3 Artificial neural network 165
6.3.4 Probabilistic neural network classifier 165
6.3.5 Support vector machine 167
6.3.6 Power quality event classification 168
6.3.7 Results and discussion 170
6.3.8 Conclusion 176
Appendix 177
Parameters of ANN 177
Parameters of probabilistic neural network 177
Parameters of particle swarm optimization 178
References 178

7. Role of microphasor measurement unit for decision
making based on enhanced situational awareness of a
modern distribution system 181
Soham Dutta, Pradip Kumar Sadhu, Maddikara Jaya
Bharata Reddy and Dusmanta Kumar Mohanta
7.1 Introduction 181
7.2 Need of microphasor measurement unit in modern
distribution system 182
7.3 Synchrophasor technology 185
7.4 Principal components of a basic microphasor
measurement unit 188
7.5 Decision application of microphasor measurement unit
in modern distribution system 190
7.6 Open microphasor measurement unit data for
research study 195
7.7 Conclusion 196
References 197

8. Effects of electrical infrastructures in grid with high
penetration of renewable sources 201
Yuri R. Rodrigues, Antonio Carlos Zambroni de Souza
and Paulo Fernando Ribeiro
Nomenclature 201
8.1 Introduction 202