Structural instrumentation and monitoring of the Block Island Offshore Wind Farm

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On the use of context information for an improved application of data-based algorithms in condition monitoring

xi, 124 p.En el campo de la monitorización de la condición, los algoritmos basados en datos cuentan con un amplio recorrido. Desde el uso de los gráficos de control de calidad que se llevan empleando durante casi un siglo a técnicas de mayor complejidad como las redes neuronales o máquinas de soporte vectorial que se emplean para detección, diagnóstico y estimación de vida remanente de los equipos. Sin embargo, la puesta en producción de los algoritmos de monitorización requiere de un estudio exhaustivo de un factor que es a menudo obviado por otros trabajos de la literatura: el contexto. El contexto, que en este trabajo es considerado como el conjunto de factores que influencian la monitorización de un bien, tiene un gran impacto en la algoritmia de monitorización y su aplicación final. Por este motivo, es el objeto de estudio de esta tesis en la que se han analizado tres casos de uso. Se ha profundizado en sus respectivos contextos, tratando de generalizar a la problemática habitual en la monitorización de maquinaria industrial, y se ha abordado dicha problemática de monitorización de forma que solucionen el contexto en lugar de cada caso de uso. Así, el conocimiento adquirido durante el desarrollo de las soluciones puede ser transferido a otros casos de uso que cuenten con contextos similares

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

Advanced structural health monitoring strategies for condition-based maintenance planning of offshore wind turbine support structures

Condition-based maintenance strategies need to be adopted as distance-to-shore and water depth increase in the offshore wind industry. The aim of the research presented herein is to develop advance structural health monitoring strategies that enhance the condition-based maintenance of offshore wind turbine support structures. The focus is on the selection of technologies, the implementation process, the analysis of the asset’s structural response under complex loading, the economic justification for structural health monitoring implementation and the effective structural health monitoring data analysis. Research activities consist of the provision of a comprehensive study for structural health monitoring technologies’ utilisation in the offshore wind industry. This is followed by parametric structural modelling, simulation and validation of an operational offshore wind turbine tower, support structure and soil-structure interaction, using commercial software. The evaluation of the asset’s response under complex loading subject to design changes and failure mechanisms is also undertaken. A combination of existing and newly developed methodologies is deployed for the effective data management of structural health monitoring systems and validated with industrial data for the case of strain monitoring. These include unsupervised learning algorithms (neural networks), deterministic and probabilistic methods for noise cleansing and missing data imputation. Guidelines for the structural health monitoring implementation from design stage of a wind farm are proposed and applied to a baseline scenario. This is utilised to assess the economic impact that structural health monitoring has in the lifecycle of the assets. The achieved results show that the implementation of structural health monitoring in offshore wind turbine following the Statistical Pattern Recognition paradigm and the proposed guidelines has the potential to reduce the Operational Expenditure. This reduction is much greater than the cost associated with the implementation of these systems. Monitoring from the commissioning of the assets is crucial for the system’s calibration and establishing thresholds. The developed noise cleansing and missing data imputation methodologies can successfully be employed together to produce more complete low-disturbed datasets

Advanced Algorithms for Automatic Wind Turbine Condition Monitoring

Reliable and efficient condition monitoring (CM) techniques play a crucial role in minimising wind turbine (WT) operations and maintenance (O&M) costs for a competitive development of wind energy, especially offshore. Although all new turbines are now fitted with some form of condition monitoring system (CMS), very few operators make use of the available monitoring information for maintenance purposes because of the volume and the complexity of the data. This Thesis is concerned with the development of advanced automatic fault detection techniques so that high on-line diagnostic accuracy for important WT drive train mechanical and electrical CM signals is achieved. Experimental work on small scale WT test rigs is described. Seeded fault tests were performed to investigate gear tooth damage, rotor electrical asymmetry and generator bearing failures. Test rig data were processed by using commercial WT CMSs. Based on the experimental evidence, three algorithms were proposed to aid in the automatic damage detection and diagnosis during WT non-stationary load and speed operating conditions. Uncertainty involved in analysing CM signals with field fitted equipment was reduced, and enhanced detection sensitivity was achieved, by identifying and collating characteristic fault frequencies in CM signals which could be tracked as the WT speed varies. The performance of the gearbox algorithm was validated against datasets of a full-size WT gearbox, that had sustained gear damage, from the National Renewable Energy Laboratory (NREL) WT Gearbox Condition Monitoring Round Robin project. The fault detection sensitivity of the proposed algorithms was assessed and quantified leading to conclusions about their applicability to operating WTs

On the use of context information for an improved application of data-based algorithms in condition monitoring

xi, 124 p.En el campo de la monitorización de la condición, los algoritmos basados en datos cuentan con un amplio recorrido. Desde el uso de los gráficos de control de calidad que se llevan empleando durante casi un siglo a técnicas de mayor complejidad como las redes neuronales o máquinas de soporte vectorial que se emplean para detección, diagnóstico y estimación de vida remanente de los equipos. Sin embargo, la puesta en producción de los algoritmos de monitorización requiere de un estudio exhaustivo de un factor que es a menudo obviado por otros trabajos de la literatura: el contexto. El contexto, que en este trabajo es considerado como el conjunto de factores que influencian la monitorización de un bien, tiene un gran impacto en la algoritmia de monitorización y su aplicación final. Por este motivo, es el objeto de estudio de esta tesis en la que se han analizado tres casos de uso. Se ha profundizado en sus respectivos contextos, tratando de generalizar a la problemática habitual en la monitorización de maquinaria industrial, y se ha abordado dicha problemática de monitorización de forma que solucionen el contexto en lugar de cada caso de uso. Así, el conocimiento adquirido durante el desarrollo de las soluciones puede ser transferido a otros casos de uso que cuenten con contextos similares

SECURE REAL-TIME SMART GRID COMMUNICATIONS: A MICROGRID PERSPECTIVE

Microgrids are a key component in the evolution of the power grid. Microgrids are required to operate in both grid connected and standalone island mode using local sources of power. A major challenge in implementing microgrids is the communications and control to support transition from grid connected mode and operation in island mode. In this dissertation we propose a distributed control architecture to govern the operation of a microgrid. The func- tional communication requirements of primary, secondary and tertiary microgrid controls are considered. Communication technology media and protocols are laid out and a worst-case availability and latency analysis is provided. Cyber Security challenges to microgrids are ex- amined and we propose a secure communication architecture to support microgrid operation and control. A security model, including network, data, and attack models, is defined and a security protocol to address the real-time communication needs of microgrids is proposed. We propose a novel security protocol that is custom tailored to meet those challenges. The chosen solution is discussed in the context of other security options available in the liter- ature. We build and develop a microgrid co-simulation model of both the power system and communication networks, that is used to simulate the two fundamental microgrid power transition functions - transition from island to grid connected mode, and grid connected to island mode. The proposed distributed control and security architectures are analyzed in terms of performance. We further characterize the response of the power and communication subsystems in emergency situations: forced islanding and forced grid modes. Based on our findings, we generalize the results to the smart grid

Low-energy sensor network protocols and application to smart wind turbines

The Internet of Things (IoT) has shown promise as an enabling technology for a wide variety of applications, from smart homes to infrastructure monitoring and management. However, a number of challenges remain before the grand vision of an everything-sensed, everything-connected world can be achieved. One of these challenges is the energy problem: how can embedded, networked sensor devices be sustainably powered over the lifetime of an application? To that end, this dissertation focuses on reducing energy consumption of communication protocols in wireless sensor networks and the IoT. The motivating application is wind energy infrastructure monitoring and management, or smart wind turbines. A variety of approaches to low-energy protocol design are studied. The result is a family of low-energy communication protocols, including one specifically designed for nodes deployed on wind turbine blades. This dissertation also presents background information on monitoring and management of wind turbines, and a vision of how the proposed protocols could be integrated and deployed to enable smart wind turbine applications