Active fault-tolerant control of nonlinear systems with wind turbine application

The thesis concerns the theoretical development of Active Fault-Tolerant Control (AFTC) methods for nonlinear system via T-S multiple-modelling approach. The thesis adopted the estimation and compensation approach to AFTC within a tracking control framework. In this framework, the thesis considers several approaches to robust T-S fuzzy control and T-S fuzzy estimation: T-S fuzzy proportional multiple integral observer (PMIO); T-S fuzzy proportional-proportional integral observer (PPIO); T-S fuzzy virtual sensor (VS) based AFTC; T-S fuzzy Dynamic Output Feedback Control TSDOFC; T-S observer-based feedback control; Sliding Mode Control (SMC). The theoretical concepts have been applied to an offshore wind turbine (OWT) application study. The key developments that present in this thesis are:• The development of three active Fault Tolerant Tracking Control (FTTC) strategies for nonlinear systems described via T-S fuzzy inference modelling. The proposals combine the use of Linear Reference Model Fuzzy Control (LRMFC) with either the estimation and compensation concept or the control reconfiguration concept.• The development of T-S fuzzy observer-based state estimate fuzzy control strategy for nonlinear systems. The developed strategy has the capability to tolerate simultaneous actuator and sensor faults within tracking and regulating control framework. Additionally, a proposal to recover the Separation Principle has also been developed via the use of TSDOFC within the FTTC framework.• The proposals of two FTTC strategies based on the estimation and compensation concept for sustainable OWTs control. The proposals have introduced a significant attribute to the literature of sustainable OWTs control via (1) Obviating the need for Fault Detection and Diagnosis (FDD) unit, (2) Providing useful information to evaluate fault severity via the fault estimation signals.• The development of FTTC architecture for OWTs that combines the use of TSDOFC and a form of cascaded observers (cascaded analytical redundancy). This architecture is proposed in order to ensure the robustness of both the TSDOFC and the EWS estimator against the generator and rotor speed sensor faults.• A sliding mode baseline controller has been proposed within three FTTC strategies for sustainable OWTs control. The proposals utilise the inherent robustness of the SMC to tolerate some matched faults without the need for analytical redundancy. Following this, the combination of SMC and estimation and compensation framework proposed to ensure the close-loop system robustness to various faults.• Within the framework of the developed T-S fuzzy based FTTC strategies, a new perspective to reduce the T-S fuzzy control design conservatism problem has been proposed via the use of different control techniques that demand less design constraints. Moreover, within the SMC based FTTC, an investigation is given to demonstrate the SMC robustness against a wider than usual set of faults is enhanced via designing the sliding surface with minimum dimension of the feedback signals

Gaussian process models for SCADA data based wind turbine performance/condition monitoring

Wind energy has seen remarkable growth in the past decade, and installed wind turbine capacity is increasing significantly every year around the globe. The presence of an excellent offshore wind resource and the need to reduce carbon emissions from electricity generation are driving policy to increase offshore wind generation capacity in UK waters. Logistic and transport issues make offshore maintenance costlier than onshore and availability correspondingly lower, and as a result, there is a growing interest in wind turbine condition monitoring allowing condition based, rather than corrective or scheduled, maintenance.;Offshore wind turbine manufacturers are constantly increasing the rated size the turbines, and also their hub height in order to access higher wind speeds with lower turbulence. However, such scaling up leads to significant increments in terms of materials for both tower structure and foundations, and also costs required for transportation, installation, and maintenance. Wind turbines are costly affairs that comprise several complex systems connected altogether (e.g., hub, drive shaft, gearbox, generator, yaw system, electric drive and so on).;The unexpected failure of these components can cause significant machine unavailability and/or damage to other components. This ultimately increases the operation and maintenance (O&M) cost and subsequently cost of energy (COE). Therefore, identifying faults at an early stage before catastrophic damage occurs is the primary objective associated with wind turbine condition monitoring.;Existing wind turbine condition monitoring strategies, for example, vibration signal analysis and oil debris detection, require costly sensors. The additional costs can be significant depending upon the number of wind turbines typically deployed in offshore wind farms and also, costly expertise is generally required to interpret the results. By contrast, Supervisory Control and Data Acquisition (SCADA) data analysis based condition monitoring could underpin condition based maintenance with little or no additional cost to the wind farm operator.;A Gaussian process (GP) is a stochastic, nonlinear and nonparametric model whose distribution function is the joint distribution of a collection of random variables; it is widely suitable for classification and regression problems. GP is a machine learning algorithm that uses a measure of similarity between subsequent data points (via covariance functions) to fit and or estimate the future value from a training dataset. GP models have been applied to numerous multivariate and multi-task problems including spatial and spatiotemporal contexts.;Furthermore, GP models have been applied to electricity price and residential probabilistic load forecasting, solar power forecasting. However, the application of GPs to wind turbine condition monitoring has to date been limited and not much explored.;This thesis focuses on GP based wind turbine condition monitoring that utilises data from SCADA systems exclusively. The selection of the covariance function greatly influences GP model accuracy. A comparative analysis of different covariance functions for GP models is presented with an in-depth analysis of popularly used stationary covariance functions. Based on this analysis, a suitable covariance function is selected for constructing a GP model-based fault detection algorithm for wind turbine condition monitoring.;By comparing incoming operational SCADA data, effective component condition indicators can be derived where the reference model is based on SCADA data from a healthy turbine constructed and compared against incoming data from a faulty turbine. In this thesis, a GP algorithm is constructed with suitable covariance function to detect incipient turbine operational faults or failures before they result in catastrophic damage so that preventative maintenance can be scheduled in a timely manner.;In order to judge GP model effectiveness, two other methods, based on binning, have been tested and compared with the GP based algorithm. This thesis also considers a range of critical turbine parameters and their impact on the GP fault detection algorithm.;Power is well known to be influenced by air density, and this is reflected in the IEC Standard air density correction procedure. Hence, the proper selection of an air density correction approach can improve the power curve model. This thesis addresses this, explores the different types of air density correction approach, and suggests the best way to incorporate these in the GP models to improve accuracy and reduce uncertainty.;Finally, a SCADA data based fault detection algorithm is constructed to detect failures caused by the yaw misalignment. Two fault detection algorithms based on IEC binning methods (widely used within the wind industry) are developed to assess the performance of the GP based fault detection algorithm in terms of their capability to detect in advance (and by how much) signs of failure, and also their false positive rate by making use of extensive SCADA data and turbine fault and repair logs.;GP models are robust in identifying early anomalies/failures that cause the wind turbine to underperform. This early detection is helpful in preventing machines to reach the catastrophic stage and allow enough time to undertake scheduled maintenance, which ultimately reduces the O&M, cost and maximises the power performance of wind turbines. Overall, results demonstrate the effectiveness of the GP algorithm in improving the performance of wind turbines through condition monitoring.Wind energy has seen remarkable growth in the past decade, and installed wind turbine capacity is increasing significantly every year around the globe. The presence of an excellent offshore wind resource and the need to reduce carbon emissions from electricity generation are driving policy to increase offshore wind generation capacity in UK waters. Logistic and transport issues make offshore maintenance costlier than onshore and availability correspondingly lower, and as a result, there is a growing interest in wind turbine condition monitoring allowing condition based, rather than corrective or scheduled, maintenance.;Offshore wind turbine manufacturers are constantly increasing the rated size the turbines, and also their hub height in order to access higher wind speeds with lower turbulence. However, such scaling up leads to significant increments in terms of materials for both tower structure and foundations, and also costs required for transportation, installation, and maintenance. Wind turbines are costly affairs that comprise several complex systems connected altogether (e.g., hub, drive shaft, gearbox, generator, yaw system, electric drive and so on).;The unexpected failure of these components can cause significant machine unavailability and/or damage to other components. This ultimately increases the operation and maintenance (O&M) cost and subsequently cost of energy (COE). Therefore, identifying faults at an early stage before catastrophic damage occurs is the primary objective associated with wind turbine condition monitoring.;Existing wind turbine condition monitoring strategies, for example, vibration signal analysis and oil debris detection, require costly sensors. The additional costs can be significant depending upon the number of wind turbines typically deployed in offshore wind farms and also, costly expertise is generally required to interpret the results. By contrast, Supervisory Control and Data Acquisition (SCADA) data analysis based condition monitoring could underpin condition based maintenance with little or no additional cost to the wind farm operator.;A Gaussian process (GP) is a stochastic, nonlinear and nonparametric model whose distribution function is the joint distribution of a collection of random variables; it is widely suitable for classification and regression problems. GP is a machine learning algorithm that uses a measure of similarity between subsequent data points (via covariance functions) to fit and or estimate the future value from a training dataset. GP models have been applied to numerous multivariate and multi-task problems including spatial and spatiotemporal contexts.;Furthermore, GP models have been applied to electricity price and residential probabilistic load forecasting, solar power forecasting. However, the application of GPs to wind turbine condition monitoring has to date been limited and not much explored.;This thesis focuses on GP based wind turbine condition monitoring that utilises data from SCADA systems exclusively. The selection of the covariance function greatly influences GP model accuracy. A comparative analysis of different covariance functions for GP models is presented with an in-depth analysis of popularly used stationary covariance functions. Based on this analysis, a suitable covariance function is selected for constructing a GP model-based fault detection algorithm for wind turbine condition monitoring.;By comparing incoming operational SCADA data, effective component condition indicators can be derived where the reference model is based on SCADA data from a healthy turbine constructed and compared against incoming data from a faulty turbine. In this thesis, a GP algorithm is constructed with suitable covariance function to detect incipient turbine operational faults or failures before they result in catastrophic damage so that preventative maintenance can be scheduled in a timely manner.;In order to judge GP model effectiveness, two other methods, based on binning, have been tested and compared with the GP based algorithm. This thesis also considers a range of critical turbine parameters and their impact on the GP fault detection algorithm.;Power is well known to be influenced by air density, and this is reflected in the IEC Standard air density correction procedure. Hence, the proper selection of an air density correction approach can improve the power curve model. This thesis addresses this, explores the different types of air density correction approach, and suggests the best way to incorporate these in the GP models to improve accuracy and reduce uncertainty.;Finally, a SCADA data based fault detection algorithm is constructed to detect failures caused by the yaw misalignment. Two fault detection algorithms based on IEC binning methods (widely used within the wind industry) are developed to assess the performance of the GP based fault detection algorithm in terms of their capability to detect in advance (and by how much) signs of failure, and also their false positive rate by making use of extensive SCADA data and turbine fault and repair logs.;GP models are robust in identifying early anomalies/failures that cause the wind turbine to underperform. This early detection is helpful in preventing machines to reach the catastrophic stage and allow enough time to undertake scheduled maintenance, which ultimately reduces the O&M, cost and maximises the power performance of wind turbines. Overall, results demonstrate the effectiveness of the GP algorithm in improving the performance of wind turbines through condition monitoring

Effective algorithms for real-time wind turbine condition monitoring and fault-detection

Reliable condition monitoring (CM) can be an effective means to significantly reduce wind turbine (WT) downtime, operations and maintenance costs and plan preventative maintenance in advance. The WT generator voltage and current output, if sampled at a sufficiently high rate (kHz range), can provide a rich source of data for CM. However, the electrical output of the WT generator is frequently shown to be complex and noisy in nature due to the varying and turbulent nature of the wind. Thus, a fully satisfactory technique that is capable to provide accurate interpretation of the WT electrical output has not been achieved to date. The objective of the research described in this thesis is to develop reliable WT CM using advanced signal processing techniques so that fast analysis of non-stationary current measurements with high diagnostic accuracy is achieved. The diagnostic accuracy and reliability of the proposed techniques have been evaluated using data from a laboratory test rig where experiments are performed under two levels of rotor electrical asymmetry faults. The experimental test rig was run under fixed and variable speed driving conditions to investigate the kind of results expected under such conditions. An effective extended Kalman filter (EKF) based method is proposed to iteratively track the characteristic fault frequencies in WT CM signals as the WT speed varies. The EKF performance was compared with some of the leading WT CM techniques to establish its pros and cons. The reported experimental findings demonstrate clear and significant gains in both the computational efficiency and the diagnostic accuracy using the proposed technique. In addition, a novel frequency tracking technique is proposed in this thesis to analyse the non-stationary current signals by improving the capability of a continuous wavelet transform (CWT). Simulations and experiments have been performed to verify the proposed method for detecting early abnormalities in WT generators. The improved CWT is finally applied for developing a new real-time CM technique dedicated to detect early abnormalities in a commercial WT. The results presented highlight the advantages of the improved CWT over the conventional CWT to identify frequency components of interest and cope with the non-linear and non-stationary fault features in the current signal, and go on to indicate its potential and suitability for WT CM.</div

Machine Learning assisted Digital Twin for event identification in electrical power system

The challenges of stable operation in the electrical power system are increasing with the infrastructure shifting of the power grid from the centralized energy supply with fossil fuels towards sustainable energy generation. The predominantly RES plants, due to the non-linear electronic switch, have brought harmonic oscillations into the power grid. These changes lead to difficulties in stable operation, reduction of outages and management of variations in electric power systems. The emergence of the Digital Twin in the power system brings the opportunity to overcome these challenges. Digital Twin is a digital information model that accurately represents the state of every asset in a physical system. It can be used not only to monitor the operation states with actionable insights of physical components to drive optimized operation but also to generate abundant data by simulation according to the guidance on design limits of physical systems. The work addresses the topic of the origin of the Digital Twin concept and how it can be utilized in the optimization of power grid operation.Die Herausforderungen für den zuverfässigen Betrieb des elektrischen Energiesystems werden mit der Umwandlung der Infrastruktur in Stromnetz von der zentralen Energieversorgung mit fossilen Brennstoffen hin zu der regenerativen Energieeinspeisung stetig zugenommen. Der Ausbau der erneuerbaren Energien im Zuge der klimapolitischen Zielsetzung zur CO²-Reduzierung und des Ausstiegs aus der Kernenergie wird in Deutschland zügig vorangetrieben. Aufgrund der nichtlinearen elektronischen Schaltanlagen werden die aus EE-Anlagen hervorgegangenen Oberschwingungen in das Stromnetz eingebracht, was nicht nur die Komplexität des Stromnetzes erhöht, sondern auch die Stabilität des Systems beeinflusst. Diese Entwicklungen erschweren den stabilen Betrieb, die Verringerung der Ausfälle und das Management der Netzschwankungen im elektrischen Energiesystem. Das Auftauchen von Digital Twin bringt die Gelegenheit zur Behebung dieser Herausforderung. Digital Twin ist ein digitales Informationsmodell, das den Zustand des physikalischen genau abbildet. Es kann nicht nur zur Überwachung der Betriebszustände mit nachvollziehbarem Einsichten über physischen Komponenten sondern auch zur Generierung der Daten durch Simulationen unter der Berücksichtigung der Auslegungsgrenze verwendet werden. Diesbezüglich widmet sich die Arbeit zunächste der Fragestellung, woher das Digital Twin Konzept stammt und wie das Digitan Twin für die Optimierung des Stromnetzes eingesetzt wird. Hierfür werden die Perspektiven über die dynamische Zustandsschätzung, die Überwachung des des Betriebszustands, die Erkennung der Anomalien usw. im Stromnetz mit Digital Twin spezifiziert. Dementsprechend wird die Umsetzung dieser Applikationen auf dem Lebenszyklus-Management basiert. Im Rahmen des Lebenszyklusschemas von Digital Twin sind drei wesentliche Verfahren von der Modellierung des Digital Twins zur deren Applizierung erforderlich: Parametrierungsprozess für die Modellierung des Digital Twins, Datengenerierung mit Digital Twin Simulation und Anwendung mit Machine Learning Algorithmus für die Erkennung der Anomalie. Die Validierung der Zuverlässigkeit der Parametrierung für Digital Twin und der Eventserkennung erfolgt mittels numerischer Fallstudien. Dazu werden die Algorithmen für Online und Offline zur Parametrierung des Digital Twins untersucht. Im Rahmen dieser Arbeit wird das auf CIGRÉ basierende Referenznetz zur Abbildung des Digital Twin hinsichtlich der Referenzmessdaten parametriert. So sind neben der Synchronmaschine und Umrichter basierende Einspeisung sowie Erreger und Turbine auch regler von Umrichter für den Parametrierungsprozess berücksichtigt. Nach der Validierung des Digital Twins werden die zahlreichen Simulationen zur Datengenerierung durchgeführt. Jedes Event wird mittels der Daten vo Digital Twin mit einem "Fingerprint" erfasst. Das Training des Machine Learning Algorithmus wird dazu mit den simulierten Daten von Digital Twin abgewickelt. Das Erkennungsergebnis wird durch die Fallstudien validiert und bewertet

Condition Monitoring of Induction Motors Based on Stator Currents Demodulation

International audienceOver the past several decades, induction machine condition monitoring have received increasing attention from researchers and engineers. Several induction machine faults detection techniques have been proposed that are based on vibration, temperature, and currents/power monitoring, etc. Motor current signature analysis is a cost-effective method, which has been widely investigated. Specifically, it has been demonstrated that mechanical and electrical induction machine faults can be effectively diagnosed using stator currents demodulation. Therefore, this paper proposes to investigate the use of demodulation techniques for bearing faults detection and diagnosis based on stator currents analysis. If stator currents are assumed to be mono-component signals, the demodulation techniques include the synchronous demodulator, the Hilbert transform, the Teager energy operator, the Concordia transform, the maximum likelihood approach and the principal component analysis. For a multi-component signal, further preprocessing techniques are required such as the Empirical Mode Decomposition (EMD) or the Ensemble EMD (EEMD). The studied demodulation techniques are demonstrated for bearing faults diagnosis using simulation data, issued from a coupled electromagnetic circuits approach-based simulation tool, and experiments on a 0.75kW induction machine test bed