Design and Control of Electrical Motor Drives

Dear Colleagues, I am very happy to have this Special Issue of the journal Energies on the topic of Design and Control of Electrical Motor Drives published. Electrical motor drives are widely used in the industry, automation, transportation, and home appliances. Indeed, rolling mills, machine tools, high-speed trains, subway systems, elevators, electric vehicles, air conditioners, all depend on electrical motor drives.However, the production of effective and practical motors and drives requires flexibility in the regulation of current, torque, flux, acceleration, position, and speed. Without proper modeling, drive, and control, these motor drive systems cannot function effectively.To address these issues, we need to focus on the design, modeling, drive, and control of different types of motors, such as induction motors, permanent magnet synchronous motors, brushless DC motors, DC motors, synchronous reluctance motors, switched reluctance motors, flux-switching motors, linear motors, and step motors.Therefore, relevant research topics in this field of study include modeling electrical motor drives, both in transient and in steady-state, and designing control methods based on novel control strategies (e.g., PI controllers, fuzzy logic controllers, neural network controllers, predictive controllers, adaptive controllers, nonlinear controllers, etc.), with particular attention to transient responses, load disturbances, fault tolerance, and multi-motor drive techniques. This Special Issue include original contributions regarding recent developments and ideas in motor design, motor drive, and motor control. The topics include motor design, field-oriented control, torque control, reliability improvement, advanced controllers for motor drive systems, DSP-based sensorless motor drive systems, high-performance motor drive systems, high-efficiency motor drive systems, and practical applications of motor drive systems. I want to sincerely thank authors, reviewers, and staff members for their time and efforts. Prof. Dr. Tian-Hua Liu Guest Edito

Failure Prognosis of Wind Turbine Components

Wind energy is playing an increasingly significant role in the World\u27s energy supply mix. In North America, many utility-scale wind turbines are approaching, or are beyond the half-way point of their originally anticipated lifespan. Accurate estimation of the times to failure of major turbine components can provide wind farm owners insight into how to optimize the life and value of their farm assets. This dissertation deals with fault detection and failure prognosis of critical wind turbine sub-assemblies, including generators, blades, and bearings based on data-driven approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the Remaining Useful Life (RUL) of faulty components accurately and efficiently. The main contributions of this dissertation are in the application of ALTA lifetime analysis to help illustrate a possible relationship between varying loads and generators reliability, a wavelet-based Probability Density Function (PDF) to effectively detecting incipient wind turbine blade failure, an adaptive Bayesian algorithm for modeling the uncertainty inherent in the bearings RUL prediction horizon, and a Hidden Markov Model (HMM) for characterizing the bearing damage progression based on varying operating states to mimic a real condition in which wind turbines operate and to recognize that the damage progression is a function of the stress applied to each component using data from historical failures across three different Canadian wind farms

Data-driven model-based approaches to condition monitoring and improving power output of wind turbines

The development of the wind farm has grown dramatically in worldwide over the past 20 years. In order to satisfy the reliability requirement of the power grid, the wind farm should generate sufficient active power to make the frequency stable. Consequently, many methods have been proposed to achieve optimizing wind farm active power dispatch strategy. In previous research, it assumed that each wind turbine has the same health condition in the wind farm, hence the power dispatch for healthy and sub-healthy wind turbines are treated equally. It will accelerate the sub-healthy wind turbines damage, which may leads to decrease generating efficiency and increases operating cost of the wind farm. Thus, a novel wind farm active power dispatch strategy considering the health condition of wind turbines and wind turbine health condition estimation method are the proposed. A modelbased CM approach for wind turbines based on the extreme learning machine (ELM) algorithm and analytic hierarchy process (AHP) are used to estimate health condition of the wind turbine. Essentially, the aim of the proposed method is to make the healthy wind turbines generate power as much as possible and reduce fatigue loads on the sub-healthy wind turbines. Compared with previous methods, the proposed methods is able to dramatically reduce the fatigue loads on subhealthy wind turbines under the condition of satisfying network operator active power demand and maximize the operation efficiency of those healthy turbines. Subsequently, shunt active power filters (SAPFs) are used to improve power quality of the grid by mitigating harmonics injected from nonlinear loads, which is further to increase the reliability of the wind turbine system

The blessings of explainable AI in operations & maintenance of wind turbines

Wind turbines play an integral role in generating clean energy, but regularly suffer from operational inconsistencies and failures leading to unexpected downtimes and significant Operations & Maintenance (O&M) costs. Condition-Based Monitoring (CBM) has been utilised in the past to monitor operational inconsistencies in turbines by applying signal processing techniques to vibration data. The last decade has witnessed growing interest in leveraging Supervisory Control & Acquisition (SCADA) data from turbine sensors towards CBM. Machine Learning (ML) techniques have been utilised to predict incipient faults in turbines and forecast vital operational parameters with high accuracy by leveraging SCADA data and alarm logs. More recently, Deep Learning (DL) methods have outperformed conventional ML techniques, particularly for anomaly prediction. Despite demonstrating immense promise in transitioning to Artificial Intelligence (AI), such models are generally black-boxes that cannot provide rationales behind their predictions, hampering the ability of turbine operators to rely on automated decision making. We aim to help combat this challenge by providing a novel perspective on Explainable AI (XAI) for trustworthy decision support.This thesis revolves around three key strands of XAI – DL, Natural Language Generation (NLG) and Knowledge Graphs (KGs), which are investigated by utilising data from an operational turbine. We leverage DL and NLG to predict incipient faults and alarm events in the turbine in natural language as well as generate human-intelligible O&M strategies to assist engineers in fixing/averting the faults. We also propose specialised DL models which can predict causal relationships in SCADA features as well as quantify the importance of vital parameters leading to failures. The thesis finally culminates with an interactive Question- Answering (QA) system for automated reasoning that leverages multimodal domain-specific information from a KG, facilitating engineers to retrieve O&M strategies with natural language questions. By helping make turbines more reliable, we envisage wider adoption of wind energy sources towards tackling climate change

Accurate Bolt Tightening using Model-Free Fuzzy Control for Wind Turbine Hub Bearing Assembly

"In the modern wind turbine industry, one of the core processes is the assembly of the bolt-nut connections of the hub, which requires tightening bolts and nuts to obtain well-distributed clamping force all over the hub. This force deals with nonlinear uncertainties due to the mechanical properties and it depends on the final torque and relative angular position of the bolt/nut connection. This paper handles the control problem of automated bolt tightening processes. To develop a controller, the process is divided into four stages, according to the mechanical characteristics of the bolt/nut connection: a Fuzzy Logic Controller (FLC) with expert knowledge of tightening process and error detection capability is proposed. For each one of the four stages, an individual FLC is designed to address the highly non-linearity of the system and the error scenarios related to that stage, to promptly prevent and avoid mechanical damage. The FLC is implemented and real time executed on an industrial PC and finally validated. Experimental results show the performance of the controller to reach precise torque and angle levels as well as desired clamping force. The capability of error detection is also validated.

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

딥러닝 기반 와류기인 선박 프로펠러 진동 탐지 기술

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부(멀티스케일 기계설계전공), 2022. 8. 김윤영.국제해사기구(IMO)의 탄소 배출량 저감 규제 등의 규제에 따라 조선 해운업계는 선박의 초대형화와 에너지 저감장치(ESD) 등 친환경 장치 적용으로 대응하고 있다. 이에 따라 선박의 프로펠러, 러더, ESD 등 수중 구조물의 설계 변화가 요구되고 있다. 새로운 설계 요구조건에 맞춰 주요 제원이 결정되며 전산유체해석 및 수조시험을 통한 성능설계, 진동해석 및 구조강도해석을 통한 구조설계가 진행된다. 수중구조물 제작 이후에는 품질검사를 거쳐 시운전 중에 성능과 진동평가를 마치면 선박이 인도된다. 친환경 장치가 설치된 대형 상선의 선미 구조물은 형상이 복잡하여 유동 및 진동특성의 설계 민감도가 크고 생산 공차에 따른 피로수명의 산포가 크기 때문에 초기 설계단계에서 모든 품질문제를 걸러 내기 어려운 문제가 있다. 특히 유동장에 있는 수중구조물의 경우 특정 유속에서 와류 이탈이 발생하게 되며 와류 이탈 주파수가 구조물의 고유진동수가 일치하는 경우 공진에 인한 와류기인진동(Vortex Induced Vibration; VIV) 문제가 종종 발생되어 수중구조물 피로손상의 원인이 되고 있다. VIV 문제가 있는 상태로 선박이 인도될 경우 설계수명을 만족하지 못하고 단기간에 파손이 되는 경우가 많아 조선소에 큰 피해를 주기 때문에 선박 인도 직전인 선박 시운전 단계에서 진동이나 응력 계측을 통해 VIV 발생 여부의 확인이 필요하다. 구조물에 작용하는 하중을 계측하는 전통적인 방법은 구조물에 스트레인게이지를 설치하고 수중 텔레미터리를 설치하여 구조물의 스트레인을 직접 계측하는 방법이지만 계측을 위해 많은 비용이 소요되고 계측 실패의 가능성이 매우 높다는 문제가 있다. 본 논문에서는 대형 상선 프로펠러의 대표적인 손상 원인인 Vortex Induced Vibration을 시운전 단계에서 선체 진동 계측을 통해 간접적으로 검출할 수 있는 방법을 제안하였다. 특정 VIV가 문제가 되는 경우는 유속에서 와류 이탈 주파수가 구조물의 고유진동수가 일치하는 경우 공진에 의해 와류이탈 강도가 증가하고 유속이 증가하더라도 와류이탈 주파수가 유지되는 Lock-in 현상이 발생하는 경우로 간접 계측을 통해 이를 명시적으로 확인할 수 있다. 이를 위해서는 진동 전문가의 반복적인 진동 계측 및 평가 프로세스가 필요한데 본 연구에서는 전문가를 대신한 딥러닝 알고리즘을 이용한 VIV 탐지 시스템을 제안하였다. 진동 분석과 VIV 검출 자동화를 위해 이미지 기반의 Object detection을 위해 널리 이용되고 있는 CNN(Convolution Neural Network) 알고리즘을 이용하였다. 본 연구에서는 Object detection을 수행하되 Classification은 수행하지 않아도 되는 특징이 있어 이에 특화된 CNN 모델 개발을 위해 Hyper parameter를 조정하여 Hidden Layer를 증가하는 방법으로 30개의 CNN모델을 검토하였고 최종적으로 과적합이 없이 탐지 성능이 높은 5개의 Hidden layer 가진 모델을 제안하였다. CNN 학습을 위해 필요한 대규모의 데이터 생성을 위해 진동 모드 중첩법 기반의 간이 선박 모델을 제안하였고 프로펠러 기진력을 모사하였다. 간이 모델을 이용하여 실제 진동계측 결과와 유사한 진동 특성을 보이는 10,000개의 데이터를 생성하여 학습에 이용하였고 1,000개의 데이터를 이용하여 테스트한 결과 82%이상의 탐지 성공률을 보였다. 제안된 탐지시스템의 검증을 위해 축소모델 시험을 수행하였다. 프로펠러에서 Vortex shedding 주파수와 블레이드의 수중 고유진동수가 일치하도록 설계된 1/10 스케일의 선박 추진 시스템 축소 모델을 이용하여 프로펠러에서 Vortex Induced Vibration을 발생시키고 프로펠러 주변 구조물에서 가속도계를 이용하여 Lock-in 현상에 의한 진동을 측정하였다. 이 신호를 이용하여 개발된 시스템으로 VIV의 검출이 가능함을 보였다. 마지막으로 VIV문제가 발생했던 원유운반선의 시운전 중 기관실 내에서 계측된 선체 구조 진동값을 이용하여 개발된 탐지 시스템의 타당성을 검증하고 실제 선박에서의 적용 가능성도 확인하였다. 개발된 시스템은 VIV 검출은 위한 자동화 시스템으로 활용이 가능할 것으로 보이며 향후 실선 데이터가 확보될 경우 유용성이 증가할 것으로 기대된다Due to the International Maritime Organization’s (IMO) regulations on carbon emission reduction, the shipbuilding and shipping industry increases the size of ships and adopts energy-saving devices (ESD) on ships. Accordingly, design changes of underwater structures such as propellers, rudders, and ESD of ships are required in line with these trends. The lock-in phenomenon caused by vortex-induced vibration (VIV) is a potential cause of vibration fatigue and singing of the propellers of large merchant ships. The VIV occurs when the vibration frequency of a structure immersed in a fluid is locked in its resonance frequencies within a flow speed range. Here, a deep learning-based algorithm is proposed for early detection of the VIV phenomenon. A salient feature in this approach is that the vibrations of a hull structure are used instead of the vibrations of its propeller, implying that indirect hull structure data relatively easy to acquire are utilized. The RPM-frequency representations of the measured vibration signals, which stack the vibration frequency spectrum respective to the propeller RPMs, are used in the algorithm. The resulting waterfall charts, which look like two-dimensional image data, are fed into the proposed convolutional neural network architecture. To generate a large data set needed for the network training, we propose to synthetically produce vibration data using the modal superposition method without computationally-expensive fluid-structure interaction analysis. This way, we generated 100,000 data sets for training, 1,000 sets for hyper-parameter tuning, and 1,000 data sets for the test. The trained network was found to have a success rate of 82% for the test set. We collected vibration data in our laboratory's small-scale ship propulsion system to test the proposed VIV detection algorithm in a more realistic environment. The system was so designed that the vortex shedding frequency and the underwater natural frequency match each other. The proposed VIV detection algorithm was applied to the vibration data collected from the small-scale system. The system was operated in the air and found to be sufficiently reliable. Finally, the proposed algorithm applied to the collected vibration data from the hull structure of a commercial full-scale crude oil carrier in her sea trial operation detected the propeller singing phenomenon correctly.CHAPTER 1. INTRODUCTION 1 1.1 Motivation 1 1.2 Research objectives 8 1.3 Outline of thesis 9 CHAPTER 2. PROPELLER VORTEX-INDUCED VIBRATION MEASUREMNT METHOD 24 2.1 Structural vibration measurement methods 24 2.2 Direct measuremt method for propeller vibration 26 2.3 Indirect measuremt method for propeller vibration 28 CHAPTER 3. DEEP LEARNING NETWORK FOR VIV IDENTIFICATION 39 3.1 Convolution Neural Network 39 3.2 Data generation using mode superposition 46 3.3 Structure of the proposed CNN model 50 3.4 Deep neural networks 53 3.5 Training and diagnosis steps 55 3.6 Performance of the diagnositc model 56 CHAPTER 4. EXPERIMETS AND RESULTS 76 4.1 Experimental apparatus and data collection 76 4.2 Results and discussion 78 CHAPTER 5. ENHANCEMENT OF DETECTION PERFORMANCE USING MULTI-CHANNEL APPROACH 98 CHAPTER 6. VORTEX-INDUCED VIBRATIOIN IDENTIFICATION IN THE PROPELLER OF A CRUDE OIL CARRIER 105 CHAPTER 7. CONCLUSION 114 REFERENCES 118 ABSTRACT(KOREAN) 127박

Design Optimization of Wind Energy Conversion Systems with Applications

Modern and larger horizontal-axis wind turbines with power capacity reaching 15 MW and rotors of more than 235-meter diameter are under continuous development for the merit of minimizing the unit cost of energy production (total annual cost/annual energy produced). Such valuable advances in this competitive source of clean energy have made numerous research contributions in developing wind industry technologies worldwide. This book provides important information on the optimum design of wind energy conversion systems (WECS) with a comprehensive and self-contained handling of design fundamentals of wind turbines. Section I deals with optimal production of energy, multi-disciplinary optimization of wind turbines, aerodynamic and structural dynamic optimization and aeroelasticity of the rotating blades. Section II considers operational monitoring, reliability and optimal control of wind turbine components