Condition monitoring and fault diagnosis of tidal stream turbines subjected to rotor imbalance faults

The main focus of the work presented within this thesis was the testing and development of condition monitoring procedures for detection and diagnosis of HATT rotor imbalance faults. The condition monitoring processes were developed via Matlab with the goal of exploiting generator measurements for rotor fault monitoring. Suitable methods of turbine simulation and testing were developed in order to test the proposed CM processes. The algorithms were applied to both simulation based and experimental data sets which related to both steady-state and non-steady-state turbine operation. The work showed that development of condition monitoring practices based on analysis of data sets generated via CFD modelling was feasible. This could serve as a useful process for turbine developers. The work specifically showed that consideration of the torsional spectra observed in CFD datasets was useful in developing a, ‘rotor imbalance criteria’ which was sensitive to rotor imbalance conditions. Furthermore, based on the CFD datasets acquired it was possible to develop a parametric rotor model which was used to develop rotor torque time series under more general flow conditions. To further test condition monitoring processes and to develop the parametric rotor model developed based on CFD data a scale model turbine was developed. All aspects of data capture and test rig control was developed by the researcher. The test rig utilised data capture within the turbine nose cone which was synchronised with the global data capture clock source. Within the nose cone thrust and moment about one of the turbine blades was measured as well as acceleration at the turbine nose cone. The results of the flume testing showed that rotor imbalance criteria was suitable for rotor imbalance faults as applied to 4 generator quadrature axis current measurements as an analogue for drive train torque measurements. It was further found that feature fusion of the rotor imbalance criterion calculated with power coefficient monitoring was successful for imbalance fault diagnosis. The final part of the work presented was to develop drive train simulation processes which could be calculated in real-time and could be utilised to generate representative datasets under non-steady-state conditions. The parametric rotor model was developed, based on the data captured during flume testing, to allow for non-steady state operation. A number of simulations were then undertaken with various rotor faults simulated. The condition monitoring processes were then applied to the data sets generated. Condition monitoring based on operational surfaces was successful and normalised calculation of the surfaces was outlined. The rotor imbalance criterion was found to be less sensitive to the fault cases under non-steady state condition but could well be suitable for imbalance fault detection rather than diagnosis

Maintenance Management of Wind Turbines

“Maintenance Management of Wind Turbines” considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements

회전기계 내 저해상도 및 고해상도 신호를 활용한 딥러닝 기반 거시적 및 미시적 고장 진단 방법론

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2023. 2. 윤병동.Rotating machinery is widely used in many industrial sites, including manufacturing and power generation. Unpredicted failures in these systems can result in huge economic and human losses. To prevent this situation, fault diagnosis studies have gathered much attention, with the goal of operating rotating machines without the occurrence of any unpredicted problems. Fault diagnosis methods aim to accurately detect any abnormality prior to failure and classify the health conditions of the target system. Recently, fault diagnosis studies using deep learning have achieved excellent performance thanks to the ability of new methods to autonomously extract meaningful features. For this purpose, two types of signals of different resolutions are measured from rotating machinery, specifically: operation signals and vibration signals. Operation signals, which are measured with a low sampling rate, are obtained in real-time and contain various types of condition parameters that enable global monitoring of the system. Vibration signals with a high sampling rate are obtained when an event occurs, not in real-time. Using these signals of different resolutions, two sub-tasks of fault diagnosis – anomaly detection and fault identification – are performed. Anomaly detection, which is conducted with operation signals, is a task to detect abnormalities in a system before those abnormalities develop into a hard failure. This is considered macro-level fault diagnosis. When performing anomaly detection, the normal data is modeled by unsupervised learning, a residual is calculated, and a threshold is determined. If the residual becomes larger than the threshold, the system is regarded as an anomaly condition. Fault identification is performed to classify the health conditions of the system using vibration signals; this is viewed as micro-level fault diagnosis. For fault identification, supervised learning is used to train a deep-learning-based classifier; thus, a large amount of labeled data is required for the training. Since fault data is insufficient in real industrial fields, data augmentation is necessary to augment the fault data. Currently, a variational auto-encoder or a generative adversarial network are the approaches most widely used for data augmentation. Anomaly detection and fault identification have been studied separately. If both tasks are integrated, macro- and micro-level fault diagnosis can be implemented. However, there are three issues that must be handled to develop a deep-learning-based methodology for macro- and micro-level fault diagnosis. First, conventional anomaly detection methods produce frequent false alarms; in other words, they may indicate a problem even if there is no anomaly in the system. This problem occurs because conventional approaches may model the normal data inadequately or set a wrong threshold; for example, one that does not consider the fluctuations in the normal data. Second, the prior generative-network-based augmentation approach has inborn limitations due to its structural properties. With this method, signals of various lengths cannot be generated because the architecture is fixed. Also, incorrect samples can be generated if the latent vectors are sampled wrongly. The final issue with health classification is that the performance of a classifier can be affected by noise in the input data. Since noise can distort the data distribution, it is difficult for a classifier to correctly classify the noisy data. Based on the current state of the field, this doctoral dissertation proposes a deep-learning-based methodology for macro- and micro-level fault diagnosis using operation and vibration signals from rotating machinery. The first research thrust proposes new methods for modeling and threshold setting to reduce false alarms related to anomaly detection. The proposed modeling method is developed by applying ensemble and denoising techniques to auto-encoders. Further, a threshold is newly proposed using the joint distribution of the output and the residual. Consequently, the proposed method considers the fluctuations in the normal data, which can significantly reduce false alarms. The second research thrust proposes a new generative network to generate signals of variable lengths. The proposed network, whose input and output are the time and amplitude, respectively, is designed to learn the frequency information of the training data. The proposed method is implemented to reflect the signal processing knowledge, including the use of the Nyquist theorem. After the training is finished, the proposed model can produce signals of various lengths in the desired time range. The proposed approach can also focus on the characteristic frequency components, thanks to attention blocks. The third research thrust proposes a novel training method that simultaneously learns the classification and denoising tasks. In the proposed scheme, multi-task learning is used to allow a classifier to solve the classification and denoising tasks concurrently. The proposed method can be applied to any deep-learning algorithm, regardless of the network type. The classifier that is trained by the proposed method can classify the health conditions, as well as remove noise in the input signals.회전기계는 제조 및 발전과 같이 다양한 산업 현장에서 널리 사용되고 있다. 회전기계의 예기치 못한 고장은 막대한 경제적, 인적 손실을 야기할 수 있다. 이러한 상황을 예방하기 위해서, 회전기계의 건전성 상태를 정확히 관리하는 것을 목표로 하는 고장 진단 연구가 주목을 받고 있다. 고장 진단 기법들은 목표 시스템의 이상을 정확히 감지하고 건전성 상태를 식별하는 것을 목표로 한다. 최근에는 딥러닝 기반 연구들이 자동적으로 유의미한 특성인자를 추출하는 능력 덕분에 뛰어난 진단 성능을 보이고 있다. 회전기계에서는 해상도가 서로 다른 운전 신호 및 진동 신호가 취득된다. 저샘플링 주파수로 취득되는 운전 신호는 실시간으로 얻어지고, 시스템을 전반적으로 관리할 수 있는 다양한 종류의 상태 변수를 포함하고 있다. 진동 신호는 고샘플링 주파수로 측정되고 실시간이 아니라, 고장이 발생하면 취득된다. 해상도가 다른 두 신호를 활용해서 고장 진단의 두 가지 하위 테스크인 이상 감지 및 고장 식별이 수행된다. 운전 신호를 가지고 수행되는 이상 감지는 시스템의 이상을 가능하면 빨리 감지하는 것을 목표로 한다. 이것은 거시적 수준의 고장 진단으로 여겨진다. 이상 감지 수행 시, 정상 데이터는 비지도 학습 방식으로 모델링 되고, 잔차 신호가 계산된 후에 기준치가 결정된다. 잔차 신호가 기준치를 초과하면, 해당 시스템은 이상이 있다고 판단된다. 고장 식별은 진동 신호를 사용해서 시스템의 건전성 상태를 분류하는 것을 목표로 한다. 이것은 미시적 수준의 고장 진단으로 여겨진다. 지도학습 방식을 활용해 딥러닝 기반 진단기를 학습시킨다. 그러므로 많은 양의 라벨 데이터가 학습에 필요하다. 실제 산업 현장에서는 고장 데이터가 부족하기 때문에, 부족한 고장 데이터를 증량하기 위한 데이터 증량 기법이 필수적이다. 최근에는 변분적 오토인코더나 적대적 생성 신경망을 활용한 증량 기법이 널리 연구되고 있다. 이상 감지와 고장 식별은 각자 따로 연구되었다. 만약 두 테스크가 통합된다면, 거시적 및 미시적 고장 진단이 수행될 수 있다. 하지만, 딥러닝 기반 거시적 및 미시적 고장 진단 기법을 개발하는 데 해결해야 할 세 가지 문제점이 있다. 첫째, 기존 이상 감지 기법들은 시스템에 아무 이상이 없어도 오감지를 빈번하게 발생시켰다. 기존 방법들은 정상 데이터를 부정확하게 모델링하거나 기준치를 잘못 설정해서 정상 데이터에 존재하는 변동을 고려하지 못한다. 둘째, 기존 생성 신경망 기반 모델들은 구조적 특징에 기인한 한계점을 갖고 있다. 다양한 길이의 신호가 만들어질 수 없고, 잠재 벡터가 잘못 샘플링되면 잘못된 샘플이 생성될 수 있다. 건전성 분류와 관련된 마지막 이슈는 분류기의 성능이 입력 데이터의 노이즈에 영향을 받을 수 있다는 점이다. 노이즈는 데이터 분포를 왜곡할 수 있기 때문에, 분류기가 노이즈가 있는 데이터를 올바르게 분류하는 것은 어렵다. 이러한 현황을 바탕으로, 본 박사학위 논문에서는 회전기계 내 운전 및 진동 신호를 활용한 딥러닝 기반 거시적 및 미시적 고장 진단 기법을 제안한다. 첫 번째 연구는 오감지를 줄이는 이상 감지를 위해서, 새로운 모델링 및 기준치 설정 기법들을 제안한다. 제안하는 모델링 방법은 오토인코더에 앙상블 및 디노이징 기법을 적용하여 개발됐다. 또한, 결과값과 잔차 신호 사이의 결합분포를 사용해서 동적 기준치를 설정하는 기법도 개발됐다. 이를 통해, 제안하는 방법은 정상 데이터의 변동을 고려하여 오감지를 상당히 줄일 수 있다. 두 번째 연구에서는 다양한 길이의 신호를 만들기 위한 새로운 생성 모델을 제안한다. 제안하는 네트워크는 입력과 출력이 시간 및 진폭이고, 학습 데이터의 주파수 정보를 학습하도록 설계됐다. 제안하는 모델은 나이키스트 이론과 같은 신호 처리 지식을 반영하기 위해서 신중히 설계됐다. 학습 후에, 제안하는 방법은 원하는 시간대의 다양한 길이의 신호를 만들 수 있다. 또한, 제안하는 네트워크는 어텐션 블록 덕분에 특성 주파수 성분에 집중할 수 있다. 세 번째 연구는 분류와 디노이징 테스크를 동시에 배우는 학습 기법을 제안한다. 제안하는 기법에서는 두 가지 테스크를 동시에 학습하기 위해서 다중 테스크 학습 기법이 사용된다. 제안하는 기법은 네트워크 종류에 상관없이 어떠한 딥러닝 알고리즘에 적용될 수 있다. 제안하는 방법으로 학습된 분류기는 건전성 상태를 잘 분류할 뿐만 아니라, 입력 신호의 노이즈도 제거할 수 있다.Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Research Scope and Overview 5 1.3 Dissertation Layout 9 Chapter 2 Technical Background and Literature Review 10 2.1 Fault Diagnosis Methods of Rotating Machinery 10 2.2 Low- and High-resolution Signals from Rotating Machinery 13 2.3 Review of Deep Learning Algorithms 15 2.3.1 One-dimensional Convolutional Neural Network (1D CNN) 16 2.3.2 Long Short-term Memory (LSTM) 17 2.4 Deep-learning-based Macro- and Micro-level Fault Diagnosis Methods 19 2.4.1 Anomaly Detection 23 2.4.2 Data Augmentation 28 2.4.3 Health Classification 32 2.5 Summary and Discussion 35 Chapter 3 Ensemble Denoising Auto-encoder-based Dynamic Threshold (EDAE-DT) for Anomaly Detection 37 3.1 Background: Deep-learning-based Anomaly Detection 39 3.1.1 Conventional Methods to Model the Normal Data 39 3.1.2 Conventional Methods to Set a Threshold 41 3.2 Ensemble Denoising Auto-encoder-based Dynamic Threshold (EDAE-DT) 42 3.3 Performance Evaluation Metrics 47 3.4 Description of the Validation Datasets 50 3.5 Validation of the Proposed Method 58 3.5.1 Case Study 1: Dataset A1 58 3.5.2 Case Study 2: Dataset A2 74 3.5.3 Analysis and Discussion 89 3.6 Summary and Discussion 95 Chapter 4 Frequency-learning Generative Network (FLGN) for Data Augmentation 96 4.1 Background: Fourier Series 97 4.2 Frequency-learning Generative Network (FLGN) 99 4.2.1 Problem Formulation 99 4.2.2 Overall Procedure of FLGN 100 4.2.3 Deep-learning Implementation Details to Reflect Signals Processing Knowledge 105 4.3 Experimental Implementation Setting 106 4.3.1 Hyper-parameter Setting 107 4.3.2 Evaluation Scheme 107 4.4 Description of the Validation Datasets 111 4.5 Validation of the Proposed Method 119 4.5.1 Case Study 1: Simulated Signal 119 4.5.2 Case Study 2: RK4 Testbed Dataset 128 4.5.3 Case Study 3: MAFAULDA 141 4.5.4 Analysis and Discussion 153 4.6 Summary and Discussion 158 Chapter 5 Multi-task Learning of Classification and Denoising (MLCD) for Health Classification 159 5.1 Background: Multi-task Learning 160 5.2 Multi-task Learning of Classification and Denoising (MLCD) 161 5.2.1 Overall Procedure of MLCD 162 5.2.2 Integration with LSTM: MLCD-LSTM 165 5.2.3 Integration with 1D CNN: MLCD-1D CNN 166 5.3 Preprocessing Techniques 170 5.4 Description of the Validation Datasets 172 5.5 Validation of the Proposed Method 176 5.5.1 Case Study 1: MLCD-LSTM 176 5.5.2 Case Study 2: MLCD-1D CNN 183 5.6 Summary and Discussion 190 Chapter 6 Conclusion 191 6.1 Contributions and Significance 191 6.2 Suggestions for Future Research 194 References 196 국문 초록 209박

Wind turbine drivetrains:State-of-the-art technologies and future development trends

This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains – the system that converts kinetic energy of the wind to electrical energy – in different stages of their life cycle: design, manufacturing, installation, operation, lifetime extension, decommissioning and recycling. Offshore development and digitalization are also a focal point in this study. Drivetrain in this context includes the whole power conversion system: main bearing, shafts, gearbox, generator and power converter. The main aim of this article is to review the drivetrain technology development as well as to identify future challenges and research gaps. The main challenges in drivetrain research identified in this paper include drivetrain dynamic responses in large or floating turbines, aerodynamic and farm control effects, use of rare-earth material in generators, improving reliability through prognostics, and use of advances in digitalization. These challenges illustrate the multidisciplinary aspect of wind turbine drivetrains, which emphasizes the need for more interdisciplinary research and collaboration

Advances in Modelling and Control of Wind and Hydrogenerators

Rapid deployment of wind and solar energy generation is going to result in a series of new problems with regards to the reliability of our electrical grid in terms of outages, cost, and life-time, forcing us to promptly deal with the challenging restructuring of our energy systems. Increased penetration of fluctuating renewable energy resources is a challenge for the electrical grid. Proposing solutions to deal with this problem also impacts the functionality of large generators. The power electronic generator interactions, multi-domain modelling, and reliable monitoring systems are examples of new challenges in this field. This book presents some new modelling methods and technologies for renewable energy generators including wind, ocean, and hydropower systems

Advances in Modelling and Control of Wind and Hydrogenerators

Rapid deployment of wind and solar energy generation is going to result in a series of new problems with regards to the reliability of our electrical grid in terms of outages, cost, and life-time, forcing us to promptly deal with the challenging restructuring of our energy systems. Increased penetration of fluctuating renewable energy resources is a challenge for the electrical grid. Proposing solutions to deal with this problem also impacts the functionality of large generators. The power electronic generator interactions, multi-domain modelling, and reliable monitoring systems are examples of new challenges in this field. This book presents some new modelling methods and technologies for renewable energy generators including wind, ocean, and hydropower systems

Microgrids

Microgrids are a growing segment of the energy industry, representing a paradigm shift from centralized structures toward more localized, autonomous, dynamic, and bi-directional energy networks, especially in cities and communities. The ability to isolate from the larger grid makes microgrids resilient, while their capability of forming scalable energy clusters permits the delivery of services that make the grid more sustainable and competitive. Through an optimal design and management process, microgrids could also provide efficient, low-cost, clean energy and help to improve the operation and stability of regional energy systems. This book covers these promising and dynamic areas of research and development and gathers contributions on different aspects of microgrids in an aim to impart higher degrees of sustainability and resilience to energy systems

Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation

An economic development model analyzes the adoption of alternative strategy capable of leveraging the economy, based essentially on RES. The combination of wind turbine, PV installation with new technology battery energy storage, DSM network and RES forecasting algorithms maximizes RES integration in isolated islands. An innovative model of power system (PS) imbalances is presented, which aims to capture various features of the stochastic behavior of imbalances and to reduce in average reserve requirements and PS risk. Deep learning techniques for medium-term wind speed and solar irradiance forecasting are presented, using for first time a specific cloud index. Scalability-replicability of the FLEXITRANSTORE technology innovations integrates hardware-software solutions in all areas of the transmission system and the wholesale markets, promoting increased RES. A deep learning and GIS approach are combined for the optimal positioning of wave energy converters. An innovative methodology to hybridize battery-based energy storage using supercapacitors for smoother power profile, a new control scheme and battery degradation mechanism and their economic viability are presented. An innovative module-level photovoltaic (PV) architecture in parallel configuration is introduced maximizing power extraction under partial shading. A new method for detecting demagnetization faults in axial flux permanent magnet synchronous wind generators is presented. The stochastic operating temperature (OT) optimization integrated with Markov Chain simulation ascertains a more accurate OT for guiding the coal gasification practice