POWER QUALITY MEASUREMENT AND ANALYSIS USING HIGHER-ORDER STATISTICSHelp protect your network with this important reference work on cyber securityPower quality (PQ) in electrotechnical systems refers to a set of characteristics related to the movement of energy and the delivery of voltage to consumers in the highest standard. As electricity networks change and adapt to new technologies and concepts of energy within a future Smart Grid, it has become clear that standardized methods by which stability and accuracy of electrical service along a network are currently measured are no longer enough to solve inherent issues in service and ensure established requirements are met.Power Quality Measurement and Analysis using Higher-Order Statistics reflects the latest information related to PQ (Power Quality) analysis solutions, particularly that related to the implementation of new quality indices in the domain of higher-order statistics (HOS). The authors--noted experts on the topic--carefully address the detection of PQ problems from two perspectives: the detection of specific events that occur on networks in isolation and continuous monitoring detection. In doing so, the authors demonstrate the use of HOS in current waveform models, enabling the characterization of different power circuit topologies and loads. This book thereby expertly explores the benefits of using HOS, bridging the gap between signal processing and power, and building a better understanding for readers.Power Quality Measurement and Analysis using Higher-Order Statistics readers will also find:* A unique methodology for PQ analysis through its combination of HOS and PQ monitoring* A proposal for new measurement solutions that can be easily implemented into modern instrumentation* The detection of PQ problems from multiple perspectives* The use of HOS in current waveform models, which enables the characterization of different power circuit topologies and loadsPitched at a specialized level, Power Quality Measurement and Analysis is an essential reference for researchers, academics, and industry insiders, as well as advanced students in this field.

POWER QUALITY MEASUREMENT AND ANALYSIS USING HIGHER-ORDER STATISTICS 1Understanding HOS contribution on the Smart(er) Grid 1POWER QUALITY MEASUREMENT AND ANALYSIS USING HIGHER-ORDER STATISTICS 3Understanding HOS contribution on the Smart(er) Grid 3LOGO 3Contents 11Contributors 14Foreword 17Acronyms 21Acknowledgments 24Chapter 1. Power quality monitoring and higher-order statistics. State of the Art 261.1 Introduction 271.2 Background on power quality 271.3 PQ Practices at the Industrial Level 331.4 A new PQ monitoring Framework 331.4.1 The Smart Grid 351.4.2 The Smart Grid and the Power Quality 351.4.3 Performance Indicators 361.4.4 Existing measurement and instrumentation solutions 371.4.5 New approach in Measurement and Instrumentation solutions in the SG 381.4.6 Economic Issues for PQ 391.4.7 Power Quality and Big Data 391.4.8 Signal Processing for PQ 401.4.9 HOS for PQ analysis 43Chapter 2. HOS Measurements in the time domain 47HOS Measurements in the time domain 482.1 Introduction 482.2 Background on power quality 482.3 Traditional theories of electrical time domain 492.4 HOS contribution in the PQ field 512.4.1 HOS indices definitions 512.4.2 HOS performance in signal processing 522.4.3 HOS versus electrical time domain indices 532.5 Regulations 552.6 The Sliding Window Method for HOS feature extraction 562.6.1 Amplitude Changes 572.6.2 Phase Angle Jumps 582.6.3 Fundamental Frequency 602.6.4 Waveform shape deviation 622.7 PQ index based on HOS 642.8 Representations used by the time-domain 67Chapter 3. Event Detection Strategies based on HOS feature extraction 723.1 Introduction 733.2 Detection methods based in HOS 733.3 Experiment description 733.3.1 Computational Strategy 733.3.2 HOS for Sag Detection under Symmetrical and Sinusoidal Conditions 743.3.2 HOS for Sag Detection including Phase-Angle Jump based on Non-Symmetrical & Non-Sinusoidal conditions 753.3.2.1 HOS range for Transient detection including Phase-Angle Jump based on Non-Symmetrical & Non- Sinusoidal conditions 873.3 Flow Diagram of HOS monitoring strategy focus on detecting short duration events: detecting amplitude, symmetry, and sinusoidal states 873.4 Continuous event's characterization fundamental frequency 903.4.1 Frequency deviation regions in the HOS planes 923.4.2 Frequency deviation regions in the HOS planes 943.5 Detection of Harmonics with HOS in the time domain 953.6 Conclusions 97Chapter 4. Measurements in the Frequency domain 1004.1 Introduction 1014.2 Frequency-domain 1014.3 HOS in Frequency-domain 1024.3.1 Spectral Kurtosis in Power Quality 1034.4 Harmonic distortion 1034.4.1 Types of Harmonic distortion 1044.4.2 Sources of Harmonic distortion 1054.4.3 Impact of harmonic distortion over power system 1054.5 Traditional theories of electrical frequency-domain indicators 1054.5.1 Harmonic measure 1054.5.2 DFT derived measures 1074.6 HOS contribution in PQ in the frequency-domain 1074.6.1 Spectral Kurtosis 1084.6.2 Spectral Kurtosis basic usage 1154.6.3 Spectral Kurtosis and Power quality 118Chapter 5 Measurement Campaigns and Virtual Instruments 1245.1 Introduction 1255.2 Virtual Instrument 1265.2.1 Measurement Analysis Framework 1265.2.2 Experimental Strategy for PQM through a Virtual Instrument 1285.2.3 Configuration of the Virtual Instrument 1285.2.4 Results 1315.3 PQ continuous monitoring based on HOS for consumers characterization, public networks and household 1325.3.1 Measurement and Analysis Framework 1325.3.2 Evolution of the individual statistics histograms during several weeks 133References 149Annex A. Voltage Waveform 1Theoretical power system waveform 1Annex. B. Time-domain cumulants 1Annex. C. HOS Range for Sag Detection, one cycle 3Annex. D. HOS Range for Sag Detection, 10 cycles 7