Application of Advanced Early Warning Systems with Adaptive Protection

This project developed and field-tested two methods of Adaptive Protection systems utilizing synchrophasor data. One method detects conditions of system stress that can lead to unintended relay operation, and initiates a supervisory signal to modify relay response in real time to avoid false trips. The second method detects the possibility of false trips of impedance relays as stable system swings “encroach” on the relays’ impedance zones, and produces an early warning so that relay engineers can re-evaluate relay settings. In addition, real-time synchrophasor data produced by this project was used to develop advanced visualization techniques for display of synchrophasor data to utility operators and engineers

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

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Harnessing data for wind turbines : machine learning digital-enabled asset management strategies

As interests in offshore wind farms continue to grow, so does the demand to reduce the cost of energy (COE). Maintenance cost and downtime can reduce the COE through greater information on offshore wind assets concerning the operational loads and structural integrity. This has had a significant impact on the interests of digital-enabled asset management (DEAM) using digital twins. Digital twins’ technologies can replicate operational assets computationally, providing more information and increasing one’s confidence in operations and maintenance (O&M). DEAM is a multi-disciplinary field and making advances in this field requires aspects of multiple modelling domains, this thesis aims to develop this and help aid in the future of DEAM. The work carried out in the thesis has been validated against operational data recordings from offshore structures. This provides value and confidence to the results of the state-of-the-art models for real-world engineering systems. This research presents a portfolio of four research areas that have been published in a variety of peer-reviewed journal articles and conference papers. The areas are: 1) A proposal for standardisation of pre-processing data. Current standards have not addressed how to deal with data for machine learning, and this paper aims to begin this discussion with an example. This work implements a trend condition monitoring model that makes predictions on the power of an offshore structure using supervisory control and data acquisition (SCAD) data. There are 5 different machine learning (ML) models used and the data is validated using unused data with the modelling errors quantified. 2) A novel approach to dealing with the limitations of small data sets. This is an innovative way of transferring information from a homogeneous population to increase the accuracy of an artificial neural network (ANN). The ML model is a comparison of a conventional ANN compared to the proposed hard-parameter transfer ANN model. The ML model makes a classification of the error signature from the gearbox using both SCADA data and condition monitoring system (CMS) data. The validation of the comparison uses unseen data during the training process and the errors are measured. 3) Is a case study on Wikinger offshore wind farm population homogeneity where the operational and environmental conditions are compared for all three wind turbines. This case study provides a framework to follow when investigating an offshore wind farm population. This uses operational data from three wind turbines with both SCADA, CMS data, and processed data from RAMBOLL. The outcomes from this paper are used to determine the type of ML model used in the last study. 4) Is the model development of a population-based structural health monitoring (PBSHM) model. This study investigates three domain adaptation techniques suited to strong homogeneous populations. The ML model takes SCADA data as an input and predicts the damage equivalent moments (DEM) on the jacket foundation structure. To validate the PBSHM model data from a structure that is not used during the training of the model is used to quantify the precision of the model. The individual contributions of the developments in each of the constituent areas relate to an overall improvement in modelling approaches that are necessary for DEAM and aid in the realisation of true digital twins. All the areas relate to offshore wind ML and are related to operational data. The link between the measured data and the individual models aid in gaining more information and greater insights into the O&M.As interests in offshore wind farms continue to grow, so does the demand to reduce the cost of energy (COE). Maintenance cost and downtime can reduce the COE through greater information on offshore wind assets concerning the operational loads and structural integrity. This has had a significant impact on the interests of digital-enabled asset management (DEAM) using digital twins. Digital twins’ technologies can replicate operational assets computationally, providing more information and increasing one’s confidence in operations and maintenance (O&M). DEAM is a multi-disciplinary field and making advances in this field requires aspects of multiple modelling domains, this thesis aims to develop this and help aid in the future of DEAM. The work carried out in the thesis has been validated against operational data recordings from offshore structures. This provides value and confidence to the results of the state-of-the-art models for real-world engineering systems. This research presents a portfolio of four research areas that have been published in a variety of peer-reviewed journal articles and conference papers. The areas are: 1) A proposal for standardisation of pre-processing data. Current standards have not addressed how to deal with data for machine learning, and this paper aims to begin this discussion with an example. This work implements a trend condition monitoring model that makes predictions on the power of an offshore structure using supervisory control and data acquisition (SCAD) data. There are 5 different machine learning (ML) models used and the data is validated using unused data with the modelling errors quantified. 2) A novel approach to dealing with the limitations of small data sets. This is an innovative way of transferring information from a homogeneous population to increase the accuracy of an artificial neural network (ANN). The ML model is a comparison of a conventional ANN compared to the proposed hard-parameter transfer ANN model. The ML model makes a classification of the error signature from the gearbox using both SCADA data and condition monitoring system (CMS) data. The validation of the comparison uses unseen data during the training process and the errors are measured. 3) Is a case study on Wikinger offshore wind farm population homogeneity where the operational and environmental conditions are compared for all three wind turbines. This case study provides a framework to follow when investigating an offshore wind farm population. This uses operational data from three wind turbines with both SCADA, CMS data, and processed data from RAMBOLL. The outcomes from this paper are used to determine the type of ML model used in the last study. 4) Is the model development of a population-based structural health monitoring (PBSHM) model. This study investigates three domain adaptation techniques suited to strong homogeneous populations. The ML model takes SCADA data as an input and predicts the damage equivalent moments (DEM) on the jacket foundation structure. To validate the PBSHM model data from a structure that is not used during the training of the model is used to quantify the precision of the model. The individual contributions of the developments in each of the constituent areas relate to an overall improvement in modelling approaches that are necessary for DEAM and aid in the realisation of true digital twins. All the areas relate to offshore wind ML and are related to operational data. The link between the measured data and the individual models aid in gaining more information and greater insights into the O&M

A machine learning approach to Structural Health Monitoring with a view towards wind turbines

The work of this thesis is centred around Structural Health Monitoring (SHM) and is divided into three main parts. The thesis starts by exploring di�erent architectures of auto-association. These are evaluated in order to demonstrate the ability of nonlinear auto-association of neural networks with one nonlinear hidden layer as it is of great interest in terms of reduced computational complexity. It is shown that linear PCA lacks performance for novelty detection. The novel key study which is revealed ampli�es that single hidden layer auto-associators are not performing in a similar fashion to PCA. The second part of this study concerns formulating pattern recognition algorithms for SHM purposes which could be used in the wind energy sector as SHM regarding this research �eld is still in an embryonic level compared to civil and aerospace engineering. The purpose of this part is to investigate the e�ectiveness and performance of such methods in structural damage detection. Experimental measurements such as high frequency responses functions (FRFs) were extracted from a 9m WT blade throughout a full-scale continuous fatigue test. A preliminary analysis of a model regression of virtual SCADA data from an o�shore wind farm is also proposed using Gaussian processes and neural network regression techniques. The third part of this work introduces robust multivariate statistical methods into SHM by inclusively revealing how the in uence of environmental and operational variation a�ects features that are sensitive to damage. The algorithms that are described are the Minimum Covariance Determinant Estimator (MCD) and the Minimum Volume Enclosing Ellipsoid (MVEE). These robust outlier methods are inclusive and in turn there is no need to pre-determine an undamaged condition data set, o�ering an important advantage over other multivariate methodologies. Two real life experimental applications to the Z24 bridge and to an aircraft wing are analysed. Furthermore, with the usage of the robust measures, the data variable correlation reveals linear or nonlinear connections

Design, modelling, simulation and integration of cyber physical systems: Methods and applications

The main drivers for the development and evolution of Cyber Physical Systems (CPS) are the reduction of development costs and time along with the enhancement of the designed products. The aim of this survey paper is to provide an overview of different types of system and the associated transition process from mechatronics to CPS and cloud-based (IoT) systems. It will further consider the requirement that methodologies for CPS-design should be part of a multi-disciplinary development process within which designers should focus not only on the separate physical and computational components, but also on their integration and interaction. Challenges related to CPS-design are therefore considered in the paper from the perspectives of the physical processes, computation and integration respectively. Illustrative case studies are selected from different system levels starting with the description of the overlaying concept of Cyber Physical Production Systems (CPPSs). The analysis and evaluation of the specific properties of a sub-system using a condition monitoring system, important for the maintenance purposes, is then given for a wind turbine

Artificial neural networks for vibration based inverse parametric identifications: A review

Vibration behavior of any solid structure reveals certain dynamic characteristics and property parameters of that structure. Inverse problems dealing with vibration response utilize the response signals to find out input factors and/or certain structural properties. Due to certain drawbacks of traditional solutions to inverse problems, ANNs have gained a major popularity in this field. This paper reviews some earlier researches where ANNs were applied to solve different vibration-based inverse parametric identification problems. The adoption of different ANN algorithms, input-output schemes and required signal processing were denoted in considerable detail. In addition, a number of issues have been reported, including the factors that affect ANNs’ prediction, as well as the advantage and disadvantage of ANN approaches with respect to general inverse methods Based on the critical analysis, suggestions to potential researchers have also been provided for future scopes

Edge IoT Driven Framework for Experimental Investigation and Computational Modeling of Integrated Food, Energy, and Water System

As the global population soars from today’s 7.3 billion to an estimated 10 billion by 2050, the demand for Food, Energy, and Water (FEW) resources is expected to more than double. Such a sharp increase in demand for FEW resources will undoubtedly be one of the biggest global challenges. The management of food, energy, water for smart, sustainable cities involves a multi-scale problem. The interactions of these three dynamic infrastructures require a robust mathematical framework for analysis. Two critical solutions for this challenge are focused on technology innovation on systems that integrate food-energy-water and computational models that can quantify the FEW nexus. Information Communication Technology (ICT) and the Internet of Things (IoT) technologies are innovations that will play critical roles in addressing the FEW nexus stress in an integrated way. The use of sensors and IoT devices will be essential in moving us to a path of more productivity and sustainability. Recent advancements in IoT, Wireless Sensor Networks (WSN), and ICT are one lever that can address some of the environmental, economic, and technical challenges and opportunities in this sector. This dissertation focuses on quantifying and modeling the nexus by proposing a Leontief input-output model unique to food-energy-water interacting systems. It investigates linkage and interdependency as demand for resource changes based on quantifiable data. The interdependence of FEW components was measured by their direct and indirect linkage magnitude for each interaction. This work contributes to the critical domain required to develop a unique integrated interdependency model of a FEW system shying away from the piece-meal approach. The physical prototype for the integrated FEW system is a smart urban farm that is optimized and built for the experimental portion of this dissertation. The prototype is equipped with an automated smart irrigation system that uses real-time data from wireless sensor networks to schedule irrigation. These wireless sensor nodes are allocated for monitoring soil moisture, temperature, solar radiation, humidity utilizing sensors embedded in the root area of the crops and around the testbed. The system consistently collected data from the three critical sources; energy, water, and food. From this physical model, the data collected was structured into three categories. Food data consists of: physical plant growth, yield productivity, and leaf measurement. Soil and environment parameters include; soil moisture and temperature, ambient temperature, solar radiation. Weather data consists of rainfall, wind direction, and speed. Energy data include voltage, current, watts from both generation and consumption end. Water data include flow rate. The system provides off-grid clean PV energy for all energy demands of farming purposes, such as irrigation and devices in the wireless sensor networks. Future reliability of the off-grid power system is addressed by investigating the state of charge, state of health, and aging mechanism of the backup battery units. The reliability assessment of the lead-acid battery is evaluated using Weibull parametric distribution analysis model to estimate the service life of the battery under different operating parameters and temperatures. Machine learning algorithms are implemented on sensor data acquired from the experimental and physical models to predict crop yield. Further correlation analysis and variable interaction effects on crop yield are investigated