APPLICATION OF MACHINE LEARNING TECHNIQUE IN WIND TURBINE FAULT DIAGNOSIS Afrooz Purarjomandlangrudi B.Sc (Electrical Engineering) Principal supervisor: Dr Ghavameddin Nourbakhsh Submitted in fulfilment of the requirements for the degree of Master of Engineering (Research) Science and Engineering Faculty Queensland University of Technology 2014 KEYWORDS Wind turbine, renewable energy, fault detection, condition monitoring, fault diagnosis, rotating components, gearbox, bearing, machine learning, support vector machine, anomaly detection, acoustic emission technique, and data mining ABSTRACT With the increasing demand for electric power, environmental regulations are putting restrictions on the use of thermal power plants and renewable energy sources; in particular, wind farm energy turbines are becoming very popular around the world As a result, wind turbine availability and the ability to accurately predict faults in advance have become very critical in this industry Unpredicted failures of an element in a wind turbine, particularly in low speed rotating components such as gearboxes and bearings, can lead to major financial drawbacks One of the most efficient approaches to prevent catastrophic failures and unplanned outages is by using Condition Monitoring (CM) Although a variety of CM techniques have been used recently, their applications in the power industry are still relatively new In addition, most CMs require a large number of fault indicators to accurately diagnose the component faults Learning techniques can be employed to overcome such problems in CM, as the definition of machine learning is the ability of a program or system to learn, improve and develop its efficiency over time Machine learning techniques focus on creating a system that improves its performance based on previous results and historical data instead of understanding the process that generated the data In fact, the machine learning paradigm provides the ability I of changing execution strategy based on newly acquired information from a system Learning algorithms can be useful in different applications such as prediction of the future value, clustering and detection of anomaly behaviour in the data In this study, two learning algorithms called anomaly detection and Support Vector Machine (SVM) are employed to bearing fault diagnosis and CM Basically the anomaly detection algorithm is used to recognize the presence of unusual and potentially faulty data in a dataset, which contains two phases: a training phase and a testing phase In the former, the algorithm is trained with a training dataset and in the latter; the learned algorithm is applied to a set of new data Two bearing datasets were used to validate the proposed technique, fault-seeded bearing from a test rig located at Case Western Reserve University to validate the accuracy of the anomaly detection method Detecting faults and defects in their early stages is one of the most important aspects of machine CM The second dataset was a test to failure data of bearings from the NSF I/UCR Centre for Intelligent Maintenance Systems (IMS) which was used to compare anomaly detection with a previously applied method (SVM) for finding the time incipient faults II List of Publications Journal papers:  A Purarjomandlangrudi, G Nourbakhsh, A Tan, M Esmalifalak, “Fault Detection in Wind Turbine: A Systematic Literature Review” ” Wind Engineering Vol 37, NO 5, 2013, PP 535-546 ERA ranking C  A Purarjomandlangrudi, G Nourbakhsh, A Tan, H Ghaemaghami, “Wind Turbine Condition Monitoring Using Machine Learning Techniques” Expert systems and applications, submitted ERA ranking B Conference papers:  A Purarjomandlangrudi, G Nourbakhsh, A Tan, H Ghaemaghami, Y Mishra, “Application of Anomaly Technique in Wind Turbine Bearing Fault Detection” 2014 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Submitted III STATEMENT OF ORIGINAL AUTHORSHIP The work contained in this thesis has not been previously submitted to meet requirements for an award at this or any other higher education institution To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made Signature: Date: QUT Verified Signature 10/03/2014 IV ACKNOWLEDGEMENTS First and foremost, I would like to thank my father and mother, Mehdi Purarjomand and Azam Bodaghi, for their love and unwavering support throughout my education, from the 23rd of September 1992, when my mum walked me to the school where I started my primary education, to the present day in 2014, when I am finishing my master’s degree I attribute whatever achievement I have achieved or will achieve in my life to them I am also very thankful to my younger sister, Ema Purarjomand, for her love and kindness, and for being with my parents while I have been away I would like to gratefully and sincerely thank my supervisor, Dr Ghavameddin Nourbakhsh for believing in my work and for providing insightful advice and support during all stages of my master’s journey No words can justice to my appreciation of his nurturing support and attention His time, guidance and encouragement have made all the difference My sincere thanks also go to Professor Andy Tan, whose expertise, understanding, and patience have added considerably to my graduate experience I appreciate the vast knowledge and skill in many areas that he has shared with me V I would like to thank the editors and anonymous reviewers of the various journals in which I have published articles associated with this thesis for their precious time in reviewing my works and for their valuable comments and suggestions As well, professional editor, Ms Diane Kolomeitz, has provided copyediting and proofreading services, according to the guidelines laid out in the University-endorsed national policy guidelines, ‘The editing of research theseszby professional editors’ (available at http://iped- editors.org/About_editing/Editing_theses.aspx ) I would also like to thank my other good colleagues and co- authors for their support and insightful suggestions throughout this journey: Dr Amir Hossein Ghapanchi, Dr Mohhamad Esmalifalak and Dr Houman Ghaemmaghami VI TABLE OF CONTENTS CHAPTER 1: INTRODUCTION 1.1 1.2 1.3 1.4 1.5 1.6 1.7 GENERAL INTRODUCTION WIND TURBINE COMPONENTS AND FAILURES WIND TURBINE CONDITION MONITORING AND RESEARCH QUESTIONS RESEARCH PROBLEM OBJECTIVE OF RESEARCH OVERVIEW OF RESEARCH METHODOLOGY 11 THESIS PRESENTATION AND STRUCTURE 13 CHAPTER 2: PAPER 1- FAULT DETECTION IN WIND TURBINE: A SYSTEMATIC LITERATURE REVIEW 15 2.1 INTRODUCTION 17 2.2 LITERATURE REVIEW 21 2.2.1 Gearbox and Bearing 23 2.2.2 Power Electronics and Electrical Control Failures 24 2.2.3 Generators 25 2.3 RESEARCH METHODOLOGY 26 2.3.1 Resources Searched 27 2.3.2 Search terms 27 2.3.3 Inclusion/Exclusion Criteria 28 2.3.4 Data Analysis 29 2.4 LITERATURE REVIEW FINDINGS AND RESULTS 30 2.5 CONCLUSION 36 CHAPTER 3: PAPER 2- WIND TURBINE CONDITION MONITORING USING MACHINE LEARNING TECHNIQUES 38 3.1 INTRODUCTION 41 3.2 FEATURE EXTRACTION 43 3.2.1 Kurtosis 44 3.2.2 Non-Gaussianity Score (NGS) feature 45 3.3 MACHINE LEARNING APPROACHES 45 3.3.1 Support Vector Machine (SVM) 46 3.3.2 Anomaly detection 47 3.4 EXPERIMENTAL RESULTS 50 3.4.1 Model description 51 3.5 CONCLUSION 58 CHAPTER 4: PAPER 3- APPLICATION OF ANOMALY TECHNIQUE IN WIND TURBINE BEARING FAULT DETECTION 60 4.1 INDTRODUCTION 62 4.2 MACHINE LEARNING APPROACHES 66 4.2.1 One-class Support Vector Machine 66 4.2.2 Anomaly Detection (AD) 67 4.3 EXPERIMENTAL RESULTS 69 VII 4.3.1 Model description 70 4.4 CONCLUSIONS 74 CHAPTER 5: CONCLUSIONS 76 5.1 OVERVIEW 76 5.2 SUMMARY OF FINDINGS 76 5.3 ADDRESSING RESEARCH QUESTIONS AND CONCLUSION 79 5.4 IMPLICATIONS AND FUTURE WORKS 81 5.4.1 Implications for Industry Practitioners 82 5.4.2 Implications for Researchers 83 VIII 87 CONCLUSIONS 88 CONCLUSIONS 89 CONCLUSIONS 90 CONCLUSIONS 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