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Fault Detection And Isolation In Wind Turbines Using Pca And Statistical Hypothesis Testing
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Book Synopsis Fault Detection and Isolation in Wind Turbines Using PCA and Statistical Hypothesis Testing by : Ricard Ollé Navarro
Download or read book Fault Detection and Isolation in Wind Turbines Using PCA and Statistical Hypothesis Testing written by Ricard Ollé Navarro and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Aquest projecte té l'objectiu de demostrar l'efectivitat de dues estratègies de detecció de dany en l'estructura d'un aerogenerador, basades en l'obtenció d'un patró de referencia a través de l'anàlisi de components principals (PCA) en l'estructura sana de l'aparell. Quan recollim les dades de l'estructura de la qual en volem comprovar la seva integritat, les projectem sobre el patró i mitjançant dos tipus de test d'hipòtesis diferents –inferència estadística univariable i multivariable-, en podem definir el seu diagnòstic. També té l'objectiu d'utilitzar les dues estratègies per intentar, no només saber si l'estructura està danyada o no, sinó que també detectar quin tipus de dany l'afecta. Per tal de verificar el correcte funcionament dels plans de detecció de dany, analitzarem dades provinents d'estructures afectades per diferents tipus de dany que han estat generades a partir d'un simulador (FAST software). D'aquesta manera, podrem distingir si som capaços de diferenciar les dades d'un aerogenerador sa, de les d'un danyat.
Book Synopsis Fault Detection in Wind Turbines Using PCA and Statistical Hypothesis Testing by : Josep Ma Serrahima de Cambra
Download or read book Fault Detection in Wind Turbines Using PCA and Statistical Hypothesis Testing written by Josep Ma Serrahima de Cambra and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: L'augment de la mida dels aerogeneradors per la generació d'electricitat i la seva construcció en llocs remots per maximitzar la producció suposa un augment en costos de manteniment i operació. Per tal de reduir aquests costos, eliminar manteniments programats i millorar la seguretat, apareix la necessitat de sistemes de control a distància. Structural health monitoring és el procés d'implantació d'una estratègia de detecció de fallades a l'estructura. Aplicat als aerogeneradors, fins i tot en condicions de vent canviants és necessària la detecció de dany. La primera part del projecte millora una metodologia prèviament aplicada als aerogeneradors (inferència estadística simple) reduint el temps de detecció; el segon mètode aplicat utilitza la inferència múltiple per detectar dany. Ambdós mètodes són provats per 24 mostres d'aerogeneradors en diferents condicions (sanes i danyades), i els resultats són encoratjadors: utilitzant la inferència simple el temps de detecció és reduït fins obtenir una detecció gairebé instantània; alhora, aquest projecte serveix com a prova pilot amb la inferència múltiple utilitzada per la detecció de dany en aerogeneradors, amb una correcta diagnosis d'estructures sanes i danyades.
Book Synopsis Fault Detection of Single and Interval Valued Data Using Statistical Process Monitoring Techniques by : Mohamed N. Nounou
Download or read book Fault Detection of Single and Interval Valued Data Using Statistical Process Monitoring Techniques written by Mohamed N. Nounou and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principal component analysis (PCA) is a linear data analysis technique widely used for fault detection and isolation, data modeling, and noise filtration. PCA may be combined with statistical hypothesis testing methods, such as the generalized likelihood ratio (GLR) technique in order to detect faults. GLR functions by using the concept of maximum likelihood estimation (MLE) in order to maximize the detection rate for a fixed false alarm rate. The benchmark Tennessee Eastman Process (TEP) is used to examine the performance of the different techniques, and the results show that for processes that experience both shifts in the mean and/or variance, the best performance is achieved by independently monitoring the mean and variance using two separate GLR charts, rather than simultaneously monitoring them using a single chart. Moreover, single-valued data can be aggregated into interval form in order to provide a more robust model with improved fault detection performance using PCA and GLR. The TEP example is used once more in order to demonstrate the effectiveness of using of interval-valued data over single-valued data.
Book Synopsis Advances in Principal Component Analysis by : Ganesh R. Naik
Download or read book Advances in Principal Component Analysis written by Ganesh R. Naik and published by Springer. This book was released on 2017-12-11 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reports on the latest advances in concepts and further developments of principal component analysis (PCA), addressing a number of open problems related to dimensional reduction techniques and their extensions in detail. Bringing together research results previously scattered throughout many scientific journals papers worldwide, the book presents them in a methodologically unified form. Offering vital insights into the subject matter in self-contained chapters that balance the theory and concrete applications, and especially focusing on open problems, it is essential reading for all researchers and practitioners with an interest in PCA.
Book Synopsis Fault Detection and Isolation of Wind Turbines Using Immune System Inspired Algorithms by : Esmaeil AliZadeh
Download or read book Fault Detection and Isolation of Wind Turbines Using Immune System Inspired Algorithms written by Esmaeil AliZadeh and published by . This book was released on 2017 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently, the research focus on renewable sources of energy has been growing intensively. This is mainly due to potential depletion of fossil fuels and its associated environmental concerns, such as pollution and greenhouse gas emissions. Wind energy is one of the fastest growing sources of renewable energy, and policy makers in both developing and developed countries have built their vision on future energy supply based on and by emphasizing the wind power. The increase in the number of wind turbines, as well as their size, have led to undeniable care and attention to health and condition monitoring as well as fault diagnosis of wind turbine systems and their components.In this thesis, two main immune inspired algorithms are used to perform Fault Detection and Isolation (FDI) of a Wind Turbine (WT), namely the Negative Selection Algorithm (NSA) as well as the Dendritic Cell Algorithm (DCA).First, an NSA-based fault diagnosis methodology is proposed in which a hierarchical bank of NSAs is used to detect and isolate both individual as well as simultaneously occurring faults common to the wind turbines. A smoothing moving window filter is then utilized to further improve the reliability and performance of the proposed FDI scheme. Moreover, the performance of the proposed scheme is compared with the state-of-the-art data-driven technique, namely Support Vector Machine (SVM) to demonstrate and illustrate the superiority and advantages of the proposed NSA-based FDI scheme. Finally, a nonparametric statistical comparison test is implemented to evaluate the proposed methodology with that of the SVM under various fault severities.In the second part, another immune inspired methodology, namely the Dendritic Cell Algorithm (DCA) is used to perform online sensor fault FDI. A noise filter is also designed to attenuate the measurement noise, resulting in better FDI results. The proposed DCA-based FDI scheme is then compared with the previously developed NSA-based FDI scheme, and a nonparametric statistical comparison test is also performed.Both of the proposed immune inspired frameworks are applied to a well-known wind turbine benchmark model in order to validate the effectiveness of the proposed methodologies.
Download or read book Wind Turbines written by Frede Blaabjerg and published by MDPI. This book was released on 2019-01-11 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "Wind Turbines" that was published in Energies
Book Synopsis Condition Monitoring of Wind Turbine Structures Through Univariate and Multivariate Hypothesis Testing by : Francesc Pozo
Download or read book Condition Monitoring of Wind Turbine Structures Through Univariate and Multivariate Hypothesis Testing written by Francesc Pozo and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This chapter presents a fault detection method through uni- and multivariate hypothesis testing for wind turbine (WT) faults. A data-driven approach is used based on supervisory control and data acquisition (SCADA) data. First, using a healthy WT data set, a model is constructed through multiway principal component analysis (MPCA). Afterward, given a WT to be diagnosed, its data are projected into the MPCA model space. Since the turbulent wind is a random process, the dynamic response of the WT can be considered as a stochastic process, and thus, the acquired SCADA measurements are treated as a random process. The objective is to determine whether the distribution of the multivariate random samples that are obtained from the WT to be diagnosed (healthy or not) is related to the distribution of the baseline. To this end, a test for the equality of population means is performed in both the univariate and the multivariate cases. Ultimately, the test results establish whether the WT is healthy or faulty. The performance of the proposed method is validated using an advanced benchmark that comprehends a 5-MW WT subject to various actuators and sensor faults of different types.
Book Synopsis Fault Detection and Isolation of Wind Turbines - A Real Field Data Approach by : Pep Lluís Negre Carrasco
Download or read book Fault Detection and Isolation of Wind Turbines - A Real Field Data Approach written by Pep Lluís Negre Carrasco and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The future of wind energy passes through the installation of o shore wind farms. In such locations a non-planned maintenance is highly costly, therefore, a fault-tolerant control system that is able to maintain the wind turbine connected after the occurrence of certain faults can avoid major economic losses. The purpose of this Master's thesis is to design a Fault Detection and Isolation (FDI) system, which is responsible of detecting the wind turbine faults and identify their origin. In this sense, a robust fault detection based on system identi fication and adaptive threshold generation is proposed. Real fi eld data is used to identify the nominal model that produces the estimated output for residual generation. To avoid long term deviations, this estimated output is computed from the nominal model and an observer that follows the so-called Luenberger scheme. Moreover, an adaptive threshold based on model error modelling that take into account the nominal model uncertainty i.e. makes the FDI system robust is presented. Since the wind turbine is a highly non-linear system with a complex operating range, all these techniques are extrapolated to the entire wind turbine range using Linear Parameter Varying (LPV) models. Finally, an analysis based on residual sensitivity is developed with the aim of making the FDI system able to isolate the faults.
Book Synopsis Model-based Fault Detection and Isolation Techniques for Wind Turbines by : Abdulhamed Hwas
Download or read book Model-based Fault Detection and Isolation Techniques for Wind Turbines written by Abdulhamed Hwas and published by . This book was released on 2013 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main objective of the work of this thesis is to design model-based fault detection and isolation techniques for a large-scale wind turbine. A mathematical model of the 5MW wind turbine was developed; the model was sufficiently detailed to be used for simulation purposes. The stages of the modelling procedure were to divide the overall wind turbine system into appropriate sub-models suitable for separate modelling. Each sub-model was then presented and combined in order to obtain a completed non-linear wind turbine model. Two methods are proposed to calculate the gains of a proportional-integral (PI) pitch angle controller for the non-linear model: the first method is analytical and the second method is based on simulation. The simulation results demonstrated good performance for both proposed PI schemes. In order to design an electrical torque controller, an internal model controlbased PI controller was used to find the gains of the current and of the torque controller; good static and dynamic performance were achieved. In this thesis, a quantitative model-based method for early detection and diagnosis of wind turbine faults is proposed. The method is based on designing an observer by using a linear model of the system; the observer innovation signal is monitored to detect faults. The fault detection system was designed and optimised to be maximally sensitive to system faults and minimally sensitive to system disturbances and noise; a multi-objective optimisation method was utilised to address this dual problem. Simulation results are presented to demonstrate the performance of the proposed method. Next, a non-linear observer-based scheme for early fault detection and isolation of wind turbines was developed. The method is based on designing a nonlinear observer using a non-linear model of the wind turbine. The state-dependent differential Riccati equation was used to design a non-linear observer. The comparison of system outputs with non-linear observer estimation confirmed good performance of the non-linear observer. Based on the non-linear observer, a residual generator for monitoring wind turbine model was formulated. Simulation results illustrated that the proposed method is a robust method in detecting and isolating a single fault or multi-faults in wind turbine sensors.
Book Synopsis Fault Detection and Isolation for Wind Turbine Dynamic Systems by : Y. Liu
Download or read book Fault Detection and Isolation for Wind Turbine Dynamic Systems written by Y. Liu and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Fault Detection & Prediction in Wind Turbines by : Herp Jürgen
Download or read book Fault Detection & Prediction in Wind Turbines written by Herp Jürgen and published by . This book was released on 2016 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Development of a Fault Detection and Isolation System Using Statistical Analysis by :
Download or read book Development of a Fault Detection and Isolation System Using Statistical Analysis written by and published by . This book was released on 2007 with total page 59 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Principal Component Analysis Based Fault Detection and Isolation by : Jin Cao
Download or read book Principal Component Analysis Based Fault Detection and Isolation written by Jin Cao and published by . This book was released on 2004 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Fault Detection in Structures (wind Turbine) Through Statistical Techniques, Singular Spectrum Analysis (SSA) and Frequency-based Methods by : Victor Verdezoto Pereira
Download or read book Fault Detection in Structures (wind Turbine) Through Statistical Techniques, Singular Spectrum Analysis (SSA) and Frequency-based Methods written by Victor Verdezoto Pereira and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This master thesis aims to demonstrate the effectiveness of a fault detection strategy in the structure of the wind turbine through a new strategy involving singular spectrum analysis (SSA), statistical methods and methods based on frequency. Based on a dynamic model of wind turbine, the time series behavior was obtained in the first instance without presenting any system failure and a second instance of system failure. From these series we can intervene with SSA and with statistical methods (variance, means, covariance, Fisher criteria) to design the fault detection system. The baseline will be designed with 850 healthy samples and a total sampling time of 6.25 seconds. This baseline which will provide us with the components to be compared so that the system can detect various faults that occur (fault types: fixed value, gain factor, offset and dynamics changed) in an efficient way. The results show that a sensor fault system with a high percentage of effectiveness was designed. The greater or lesser effectiveness thereof will depend on the established base line and the components used.
Book Synopsis Preventive Maintenance and Fault Detection for Wind Turbine Generators Using a Statistical Model by : Ian Kuiler
Download or read book Preventive Maintenance and Fault Detection for Wind Turbine Generators Using a Statistical Model written by Ian Kuiler and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Vigilant fault diagnosis and preventive maintenance has the potential to significantly decrease costs associated with wind generators. As wind energy continues the upward growth in technology and continued worldwide adoption and implementation, the application of fault diagnosis techniques will become more imperative. Fault diagnosis and preventive maintenance techniques for wind turbine generators are still at an early stage compared to matured strategies used for generators in conventional power plants. The cost of wind energy can be further reduced if failures are predicted in advance of a major structural failure, which leads to less unplanned maintenance. High maintenance cost of wind turbines means that predictive strategies like fault diagnosis and preventive maintenance techniques are necessary to manage life cycle costs of critical components. Squirrel-Cage Induction Generators (SCIG) are the prevailing generator type and are more robust and cheaper to manufacturer compared to other generator types used in wind turbines. A statistical model was developed using SCADA data to estimate the relationships between winding temperatures and other variables. Predicting faults in stator windings are challenging because the unhealthy condition rapidly evolves into a functional failure.
Book Synopsis Automated On-line Early Fault Diagnosis of Wind Turbines Based on Hybrid Dynamic Classifier by : Houari Toubakh
Download or read book Automated On-line Early Fault Diagnosis of Wind Turbines Based on Hybrid Dynamic Classifier written by Houari Toubakh and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis addresses the problem of automatic detection and isolation of drift-like faults in wind turbines (WTs). The main aim of this thesis is to develop a generic on-line adaptive machine learning and data mining scheme that integrates drift detection and isolation mechanism in order to achieve the simple and multiple drift-like fault diagnosis in WTs, in particular pitch system and power converter. The proposed scheme is based on the decomposition of the wind turbine into several components. Then, a classifier is designed and used to achieve the diagnosis of faults impacting each component. The goal of this decomposition into components is to facilitate the isolation of faults and to increase the robustness of the scheme in the sense that when the classifier related to one component is failed, the classifiers for the other components continue to achieve the diagnosis for faults in their corresponding components. This scheme has also the advantage to take into account the WT hybrid dynamics. Indeed, some WT components (as pitch system and power converter) manifest both discrete and continuous dynamic behaviors. In each discrete mode, or a configuration, different continuous dynamics are defined.
Book Synopsis Fault Detection and Fault Tolerant Control in Wind Turbines by : Christian Tutivén Gálvez
Download or read book Fault Detection and Fault Tolerant Control in Wind Turbines written by Christian Tutivén Gálvez and published by . This book was released on 2018 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Renewable energy is an important sustainable energy in the world. Up to now, as an essential part of low emissions energy in a lot of countries, renewable energy has been important to the national energy security, and played a significant role in reducing carbon emissions. It comes from natural resources, such as wind, solar, rain, tides, biomass, and geothermal heat. Among them, wind energy is rapidly emerging as a low carbon, resource efficient, cost effective sustainable technology in the world. Due to the demand of higher power production installations with less environmental impacts, the continuous increase in size of wind turbines and the recently developed offshore (floating) technologies have led to new challenges in the wind turbine systems.Wind turbines (WTs) are complex systems with large flexible structures that work under very turbulent and unpredictable environmental conditions for a variable electrical grid. The maximization of wind energy conversion systems, load reduction strategies, mechanical fatigue minimization problems, costs per kilowatt hour reduction strategies, reliability matters, stability problems, and availability (sustainability) aspects demand the use of advanced (multivariable and multiobjective) cooperative control systems to regulate variables such as pitch, torque, power, rotor speed, power factors of every wind turbine, etc. Meanwhile, with increasing demands for efficiency and product quality and progressing integration of automatic control systems in high-cost and safety-critical processes, the fields of fault detection and isolation (FDI) and fault tolerant control (FTC) play an important role. This thesis covers the theoretical development and also the implementation of different FDI and FTC techniques in WTs. The purpose of wind turbine FDI systems is to detect and locate degradations and failures in the operation of WT components as early as possible, so that maintenance operations can be performed in due time (e.g., during time periods with low wind speed). Therefore, the number of costly corrective maintenance actions can be reduced and consequently the loss of wind power production due to maintenance operations is minimized. The objective of FTC is to design appropriate controllers such that the resulting closed-loop system can tolerate abnormal operations of specific control components and retain overall system stability with acceptable system performance. Different FDI and FTC contributions are presented in this thesis and published in different JCR-indexed journals and international conference proceedings. These contributions embrace a wide range of realistic WTs faults as well as different WTs types (onshore, fixed offshore, and floating). In the first main contribution, the normalized gradient method is used to estimate the pitch actuator parameters to be able to detect faults in it. In this case, an onshore WT is used for the simulations. Second contribution involves not only to detect faults but also to isolate them in the pitch actuator system. To achieve this, a discrete-time domain disturbance compensator with a controller to detect and isolate pitch actuator faults is designed. Third main contribution designs a super-twisting controller by using feedback of the fore-aft and side-to-side acceleration signals of the WT tower to provide fault tolerance capabilities to the WT and improve the overall performance of the system. In this instance, a fixed-jacket offshore WT is used. Throughout the aforementioned research, it was observed that some faults induce to saturation of the control signal leading to system instability. To preclude that problem, the fourth contribution of this thesis designs a dynamic reference trajectory based on hysteresis. Finally, the fifth and last contribution is related to floating-barge WTs and the challenges that this WTs face. The performance of the proposed contributions are tested in simulations with the aero-elastic code FAST.
