Phasor Measurement Unit (PMU) – based system for event detection on synchronous generators Nicholas Mark Randall Thesis of 60 ECTS credits Master of Science (M.Sc.) in Sustainable Energy Engineering (Electrical) September 2017 ii Phasor Measurement Unit (PMU) – based system for event detection on synchronous generators by Nicholas Mark Randall Thesis of 60 ECTS credits submitted to the School of Science and Engineering at Reykjavík University in partial fulfillment of the requirements for the degree of Master of Science (M.Sc.) in Sustainable Energy Engineering (Electrical) September 2017 Supervisor: Joseph Timothy, Foley, Supervisor Assistant Professor, Reykjavík University, Iceland Ragnar Kristjánsson, ISE Advisor Assistant Professor, Reykjavík University, Iceland Guðjón Hugberg Björnsson, Co-advisor System Operations, Landsnet, Iceland Examiner: Dr. Magni Þór Pálsson, Examiner Program Manager for R&D, Landsnet, Iceland Copyright Nicholas Mark Randall September 2017 iv Phasor Measurement Unit (PMU) – based system for event detection on synchronous generators Nicholas Mark Randall September 2017 Abstract Unusual generator events are sometimes seen in the Phasor Measurement Unit (PMU) data in the Icelandic power system, probably due to the small size. The use of PMU data with event detection algorithms, along with modeling, could be a method of capturing these events for analysis. The goals of this thesis are to develop an event detection algorithm and methods of modeling the power system, using events found in the PMU data, to simu- late generator behavior under the relevant operating conditions. There are two approaches used: Event Detection Analysis and Parameter Estimation. Event Detection Analysis em- ployed three methods. Method One used a digital filter, and fast Fourier transforms for analysis. Method Two used a matched filter algorithm. Method Three worked on the prin- cipals of machine learning, using the Gaussian Process Classifier (GPC) for identification of events in the PMU data. The Parameter Estimation approach used the method of time- series analysis for finding the necessary parameters of the generators and modeling them. These parameters were: the inertia of the generators, the speed regulation constant, and the time constant of the turbine-governor system. Axiomatic Design was used for developing the design protocol for Event Detection Analysis software. The results from the Event De- tection Analysis showed that out of the three methods tried the machine learning gave the best results for the Event Detection approach. Results from Parameter Estimation revealed that this method works but requires a lot of fine-tuning for better parameter estimates. The results from the two approaches showed that when tested they would meet the requirements of processing PMU data in real time. Therefore, these approaches have relevance for the Icelandic power system and will need further research. Index Terms–Axiomatic Design, Hydro Power, Gaussian process classifier (GPC), Generator Modeling, Machine Learning, Parameter Estimation, Phasor Measurement Unit (PMU) Fasamælingar (Phasor Measurement Units) - aðferð til greiningar á óvenjulegum atburðum í rekstri rafals Nicholas Mark Randall september 2017 Útdráttur Smæð íslenska raforkukerfisins er talin valda því að stundum sjást óvenjulegir atburðir í rekstri rafala í fasamælitækjum (PMU mælingum). Notkun þessara mæligagna ásamt líkana- gerð gæti verið leiðin til að vakta þessa atburði og skýra. Markmið þessarar ritgerðar er að þróa aðferð til þess að vakta þessa atburði og aðferðafræði við líkanagerð raforkukerfis, með notkun atburða sem fundnir eru í PMU mæligögnum, til að herma hegðun rafala undir þess- um óvenjulegu rekstrarskilyrðum. Tvær aðferðir eru notaðar: Greining á vöktun atburða og mat á breytum í líkanagerð. Greining á vöktun atburða styðst við þrjár aðferðir. Aðferð eitt notast við stafræna síu, og hraða Fouriervörpun við greiningu. Aðferð tvö notast við reiknirit mátsíu. Aðferð þrjú vinnur með meginreglur vélræns náms (machine learning), sem notast við Gauss ferlaflokkun (GPC) fyrir auðkenningu atburða innan PMU gagnanna. Nálgun við mat á breytum styðst við aðferð sem byggir á greiningu tímaraða til að finna nauðsynlegar breytur rafala við líkanagerð þeirra. Skoðaðar voru eftirfarandi breytur; tregða rafala, stuðull hraðareglunar, tímastuðull rafala og gangráðar. “Forsendu hönnun” (Axiomatic Design) var beitt við þróun hönnunar á hugbúnaði sem var notaður við greiningu á vöktun atburða. Nið- urstöður úr greiningu á vöktun atburða leiddi í ljós að af þeim þrem aðferðum sem skoðaðar voru, reyndist aðferð vélræns náms (machine learning) gefa bestar niðurstöður við vökt- un atburða. Niðurstöður úr mati á breytum í líkanagerð leiddu í ljós að aðferðin virkar en krefst mikillar fínstillingar fyrir ítarlegra mat. Niðurstöður úr prófunum á báðum aðferðum sýndu að þær myndu uppfylla kröfur um rauntíma greiningu á PMU gögnum. Af þessu má leiða að báðar þessar aðferðir eru áhugaverðar fyrir vöktun á þessum atburðum í íslenska raforkukerfinu og lagt er til að þær verði rannsakaðar frekar. Viðeigandi hugtök– Axiomatic Design, Hydro Power (Vatnsafl), Gaussian process classifier (GPC), Generator Modeling (líkan af rafala) Machine Learning (Vélrænt nám), Parameter Estimation (breytur í líkanagerð), Phasor Measurmement Unit (PMU) vi Phasor Measurement Unit (PMU) – based system for event detection on synchronous generators Nicholas Mark Randall Thesis of 60 ECTS credits submitted to the School of Science and Engineering at Reykjavík University in partial fulfillment of the requirements for the degree of Master of Science (M.Sc.) in Sustainable Energy Engineering (Electrical) September 2017 Student: Nicholas Mark Randall Supervisor: Joseph Timothy, Foley Ragnar Kristjánsson Guðjón Hugberg Björnsson Examiner: Dr. Magni Þór Pálsson viii The undersigned hereby grants permission to the Reykjavík University Library to reproduce single copies of this Thesis entitled Phasor Measurement Unit (PMU) – based system for event detection on synchronous generators and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the Thesis, and except as herein before provided, neither the Thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author’s prior written permission. date Nicholas Mark Randall Master of Science x I would like to dedicate this thesis work to my friends and family. xii Acknowledgements This thesis would not have been possible without help from the following people: Alexander Moses for all the help with the python programming and machine learning on this thesis, Lee Colwill with the help on writing and editing my thesis, Dr. Ananya Sen Gupta for the help on signals and systems along with the matched filter, Keith Smithson for editing help, the Foley family for letting me say with them while I finished up my thesis work, and finally my boss Lucien Marini at INIRAM Precision Machinery working with me to say in Iceland while I finished up my thesis. I also had help from Landsvirkjun for financial support from energy research (Orku- rannsóknasjóður) scholarship and grant for 2017, this help pay for my living expenses in Iceland. Finally, Landsnet for providing me with the data needed for this thesis. xiv xv Contents Acknowledgements xiii Contents xv List of Figures xix List of Tables xxiii List of Abbreviations xxv List of Symbols xxvii 1 Introduction 1 1.1 The Icelandic Power System . 1 1.1.1 The Challenge of Low Inertia Power Systems . 2 1.2 Thesis Overview . 4 1.2.1 The Thesis Objective . 4 1.2.2 Initial Thesis Plan . 5 1.2.3 Development of the Initial Thesis Proposal . 5 1.3 Background . 6 1.3.1 Overview of Power Systems . 6 1.3.2 Three-Phase Power . 10 1.3.3 Per-Unit System . 11 1.3.4 Power System Stability and Control . 12 Voltage Stability . 13 Frequency Control . 13 Rotor Or Power Angle Stability . 14 1.4 The Phasor Measurement Unit (PMU) . 16 The Importance of PMU Data . 19 1.5 Axiomatic Design . 22 1.6 Axiomatic Design Implementation . 22 1.6.1 Customer Needs . 22 1.6.2 Design Constraints . 23 1.6.3 Design Decomposition “Zig-zagging Approach” . 23 1.6.4 Design matrix . 25 1.7 Executive Summary . 25 2 Literature Review 27 2.0.1 Methods of Generator Parameter Identification and Validation . 27 The hybrid dynamic simulation method . 28 xvi The LSE method with discrete input ARX model . 30 The Unscented Kalman Filter (UKF) method . 32 Using a digital filter for power quality parameter detection . 33 3 Methods 37 3.1 Key Assumptions . 37 3.1.1 General Assumptions Made . 37 3.1.2 Event Detection Assumptions . 38 The Definitions Events and Non-Events . 38 Software Feasibility Assumptions . 38 3.1.3 Modeling Assumptions . 39 3.2 Tools Used for the Thesis . 39 3.2.1 Principal Component Analysis (PCA) . 40 3.2.2 Gaussian Process Classification (GPC) . 42 Kernel Methods Overview . 42 Gaussian Processes for Classification . 44 3.2.2.0.1 Derivation Using Bayesian Statistics . 44 3.2.2.0.2 Laplace approximation . 45 3.2.3 Programming Environment . 46 Python Programing Language . 46 OpenModelica . 47 3.2.3.0.1 OpenModelica Software advantages over Simulink . 47 3.2.4 PMU Data from Landsnet . 48 Data formats . 48 3.3 Section 1: PMU Data and Event Detection Analysis . 49 3.3.1 Method 1: Using a Digital Filter and FFT for Analysis . 50 PMU Data Frequency for Event Detection . 52 3.3.2 Method 2: Matched Filter Algorithm . 52 3.3.3 Method 3: Supervised Machine Learning . 53 Loading the event data into Python and preprocessing. 54 The process of the supervised learning with a Gaussian process clas- sifier (GPC). 55 The Event Detection Algorithm Test . 55 3.3.4 Testing the Feasibility of These Methods . 57 Time Trail . 57 3.4 Section 2: PMU Data and Parameter Estimation . 57 3.4.1 Parameters Estimation . 57 3.4.2 Modelica Model . 58 3.4.3 Parameter Identification and Estimation From the Landsnet PMU Data 59 3.4.4 The Feasibility Testing of the Modelica Model .