Thèses sur le sujet « PV system fault detection »
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García-Gutiérrez, Luis Antonio. « Développement d'un contrôle actif tolérant aux défaillances appliqué aux systèmes PV ». Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30071.
Texte intégralThis work contributes by developing an active fault tolerant control (AFTC) for Photovoltaic (PV) systems. The fault detection and diagnosis (FDD) methodology is based on the analysis of a model that compares real-time measurement. We use a high granularity PV array model in the FDD tool to allow faults to be detected in complex conditions. Firstly, the research focuses on fault detection in complex shadow conditions. A real-time approach is presented to emulate the electrical characteristics of PV modules under complex shadow conditions. Using a precise emulators approach is a real challenge to study the high non-linearity and the complexity of PV systems in partial shading. The real-time emulation was validated with simple experimental results under failure conditions to design specific fault-detection algorithms in a first sample. The second part of the research addresses the FDD method for DC/DC and DC/AC power converters that are connected to the grid. Primary results allowed us to validate the system's recovery for normal operating points after a fault with this complete AFTC approach. Emulations based on the simulation of distributed power converters, fault detection methodologies based on a model, and a hybrid diagnostician were then presented
Mahajan, Vijyant. « PV Module and system fault analysis ». Thesis, Mahajan, Vijyant (2014) PV Module and system fault analysis. Other thesis, Murdoch University, 2014. https://researchrepository.murdoch.edu.au/id/eprint/25561/.
Texte intégralChen, Yi-Ching. « Co-design of Fault-Tolerant Systems with Imperfect Fault Detection ». Thesis, Linköpings universitet, Programvara och system, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-104942.
Texte intégralDicharry, Jeff. « Power System Fault Detection Using Conductor Dynamics ». ScholarWorks@UNO, 2005. http://scholarworks.uno.edu/td/289.
Texte intégralKoubli, Eleni. « Impact of data quality on photovoltaic (PV) performance assessment ». Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/27508.
Texte intégralChoi, Sang-Sung. « Fault detection algorithm for Global Positioning System receivers ». Ohio : Ohio University, 1991. http://www.ohiolink.edu/etd/view.cgi?ohiou1183661191.
Texte intégralVinsonneau, Jocelyn A. F. « Fault detection and modelling for an automotive system ». Thesis, Coventry University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399534.
Texte intégralMcMichael, D. W. « On-line fault detection, a system-nonspecific approach ». Thesis, University of Oxford, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.232802.
Texte intégralLuo, Dapeng. « SYSTEM IDENTIFICATION AND FAULT DETECTION OF COMPLEX SYSTEMS ». Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3583.
Texte intégralPh.D.
Department of Mechanical, Materials and Aerospace Engineering;
Engineering and Computer Science
Mechanical Engineering
Tian, Ninghan. « ETFIDS : Efficient Transient Fault Injection and Detection System ». Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1544716635499045.
Texte intégralSandberg, Hampus. « Radiation Hardened System Design with Mitigation and Detection in FPGA ». Thesis, Linköpings universitet, Datorteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-132942.
Texte intégralPandey, Amit Nath. « Fault detection of multivariable system using its directional properties ». Texas A&M University, 2004. http://hdl.handle.net/1969.1/3354.
Texte intégralAngeli, Chrissanthi. « Intelligent fault detection techniques for an electro-hydraulic system ». Thesis, University of Sussex, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262693.
Texte intégralVengalathur, Sriram T. « Low cost fault detection system for railcars and tracks ». Texas A&M University, 2003. http://hdl.handle.net/1969/326.
Texte intégralBaldi, Pietro <1981>. « Fault detection, diagnosis and active fault tolerant control for a satellite attitude control system ». Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amsdottorato.unibo.it/6983/1/Baldi_Pietro_tesi.pdf.
Texte intégralBaldi, Pietro <1981>. « Fault detection, diagnosis and active fault tolerant control for a satellite attitude control system ». Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amsdottorato.unibo.it/6983/.
Texte intégralKurén, Jonathan, Simon Leijon, Petter Sigfridsson et Hampus Widén. « Fault Detection AI For Solar Panels ». Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413319.
Texte intégralMed en ökande användning av solpaneler runt om i världen ökar även betydelsen av att kunna upptäcka driftstörningar i panelerna. Genom att utnyttja den historiska uteffekten (kWh) från solpaneler samt meteorologisk data används maskininlärningsmodeller för att förutspå den förväntade uteffekten för ett givet solpanelssystem. Den förväntade uteffekten används sedan i en jämförelse med den faktiska uteffekten för att upptäcka om en driftstörning har uppstått i systemet. Resultatet av att använda den här metoden är att en förväntad uteffekt som efterliknar den faktiska uteffekten modelleras. Följaktligen, när ett fel simuleras (50% minskning av uteffekt), så är det möjligt för systemet att hitta alla introducerade fel vid analys över ett tidsspann på två veckor. Dessa resultat visar att det är möjligt att modellera en förväntad uteffekt av ett solpanelssystem med en maskininlärningsmodell och att använda den för att utvärdera om systemet producerar så mycket uteffekt som det bör göra. Systemet kan förbättras på några vis där tilläggandet av fler meteorologiska parametrar, öka precision av den meteorologiska datan och träna maskininlärningsmodellen på mer data är några möjligheter.
Kavi, Moses. « Smart protection system for future power system distribution networks with increased distributed energy resources ». Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/124628/1/Moses_Kavi_Thesis.pdf.
Texte intégralJaafari, Mousavi Mir Rasoul. « Underground distribution cable incipient fault diagnosis system ». Texas A&M University, 2005. http://hdl.handle.net/1969.1/4675.
Texte intégralHamdan, Abdul R. « Fault detection and rectification algorithms in a question-answering system ». Thesis, Loughborough University, 1987. https://dspace.lboro.ac.uk/2134/33743.
Texte intégralSjöberg, Ingrid. « Modelling and Fault Detection of an Overhead Travelling Crane System ». Thesis, Linköpings universitet, Reglerteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-150166.
Texte intégralFischer, Daniel Poehlman Skipper William. « Artificial intelligence techniques applied to fault detection systems / ». *McMaster only, 2004.
Trouver le texte intégralChiecchio, Jerome Jose Andres. « Aiding the Pilot in Flight Control Fault Detection ». Thesis, Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/6833.
Texte intégralRamaswamy, Sridhar. « An investigation of integrarted Global Positioning System and inertial navigation system fault detection ». Ohio : Ohio University, 2000. http://www.ohiolink.edu/etd/view.cgi?ohiou1172777336.
Texte intégralRamaswamy, Sridhar. « An investigation of integrated global positioning system and inertial navigation system fault detection ». Ohio University / OhioLINK, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1172777336.
Texte intégralAndersson, Kim. « Pressure Monitoring and Fault Detection of an Anti-g Protection System ». Thesis, Linköping University, Department of Electrical Engineering, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-56289.
Texte intégralWhen flying a fighter aircraft such as the JAS 39 Gripen, the pilot is exposed to high g-loads. In order to prevent the draining of blood from the brain during this stress an anti-g protection system is used. The system consists of a pair of trousers, called the anti-g trousers, with inflatable bladders. The bladders are filled with air, pressing tightly on to the legs in order to prevent the blood from leaving the upper part of the body.
The purpose of this thesis is to detect if the pressure of the anti-g trousers is deviating from the desired value. This is done by developing a detection algorithm which gives two kinds of alarm. One is given during minor deviations using a CUSUM test, and one is given at grave deviations, based on different conditions including residual, derivative and time. The thresholds, in which between the pressure should lie in a faultless system, are calculated from the g-load value. The thresholds are based upon given static guidelines for the pressure tolerance area and are modified in order to adapt to the estimated dynamics of the system.
The values of the input signals, pressure and g-load, were taken from real flight sessions. The validation has been performed using both faultless and faulty flight sequences, with low false alarm rate and no missed detections. All together the detection system is considered to work well.
Córdova, Ricapa Fernando. « Practical implementation of fault detection scheme in a three tank system ». Master's thesis, Pontificia Universidad Católica del Perú, 2016. http://tesis.pucp.edu.pe/repositorio/handle/123456789/7012.
Texte intégralTesis
Wang, Yifei. « Variable selection for wind turbine condition monitoring and fault detection system ». Thesis, Lancaster University, 2016. http://eprints.lancs.ac.uk/79827/.
Texte intégralBergkvist, Conny, et Stefan Wikner. « Self-organizing maps for virtual sensors, fault detection and fault isolation in diesel engines ». Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2757.
Texte intégralThis master thesis report discusses the use of self-organizing maps in a diesel engine management system. Self-organizing maps are one type of artificial neural networks that are good at visualizing data and solving classification problems. The system studied is the Vindax(R) development system from Axeon Ltd. By rewriting the problem formulation also function estimation and conditioning problems can be solved apart from classification problems.
In this report a feasibility study of the Vindax(R) development system is performed and for implementation the inlet air system is diagnosed and the engine torque is estimated. The results indicate that self-organizing maps can be used in future diagnosis functions as well as virtual sensors when physical models are hard to accomplish.
Bernath, Gregory N. « A baseline fault detection and exclusion algorithm for the global positioning system ». Ohio : Ohio University, 1994. http://www.ohiolink.edu/etd/view.cgi?ohiou1176497089.
Texte intégralShafiei, Mehdi. « Distribution network state estimation, time dependency and fault detection ». Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/124659/2/Mehdi_Shafiei_Thesis.pdf.
Texte intégralFani, Mehran. « Fault diagnosis of an automotive suspension system ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016.
Trouver le texte intégralHatzipantelis, Eleftherios. « The design and implementation of a statistical pattern recognition system for induction machine condition monitoring ». Thesis, University of Aberdeen, 1995. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU086061.
Texte intégralZhang, Xiaoxia. « Incipient anomaly detection and estimation for complex system health monitoring ». Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG025.
Texte intégralIncipient fault detection and diagnosis in engineering and multivariate industrial systems with a high-level noise are addressed in this Ph.D. thesis by a ’global’ non-parametric statistical approach. An incipient fault is supposed to induce an abnormal change in the measured value of the system variable. However, such change is weak, and it tends not to cause obvious changes in the signal distribution’s parameters. Especially in high noise level environment, the weak fault feature can be masked by the noise and becomes unpredictable. In such a condition, using traditional parametric-based methods generally fails in the fault detection. To cope with incipient fault detection and diagnosis, a ’global’ approach that can consider the total faults signature is needed. The incipient fault detection can be obtained by measuring the differences between the signal distributions before and after the fault occurrence. Some distribution-based ’global’ methods have been proposed, however, the detection capabilities of these existed approaches in high noise level environment should be improved. In this context, Jensen-Shannon divergence is considered a ’global’ fault indicator to deal with the incipient fault detection and diagnosis in a high noise level environment. Its detection performance for small abnormal variations hidden in noise is validated through simulation. In addition, the fault estimation problem is also considered in this work. A theoretical fault severity estimation model depending on the divergence value for the Gaussian condition is derived. The accuracy of the estimation model is evaluated on numerical models through simulations. Then, the ’global’ statistical approach is applied to two applications in engineering. The first one relates to non- destruction incipient cracks detection. The Jensen-Shannon divergence combined with Noisy Independent Component Analysis and Wavelet analysis was applied for detection and characterization of minor cracks in conductive structures with high-level perturbations based on experimental impedance signals. The second application addresses the incipient fault diagnosis in a multivariate non-linear process with a high-level noise. Tennessee Eastman Process (TEP) is one typical multivariate non-linear process, the Jensen-Shannon divergence in the Kernel Principal Component Analysis (KPCA) is developed for coping with incipient fault detection in this process
Alikiotis, Dimitri A. « Flight control sensor system parametric performance analysis for the fault inferring nonlinear detection system (FINDS) algorithm ». Ohio University / OhioLINK, 1987. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1183039157.
Texte intégralArdakani, Mohammad Hamed. « Data driven methods for updating fault detection and diagnosis system in chemical processes ». Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/650845.
Texte intégralLos procesos industriales modernos son cada vez más complejos y, en consecuencia, su control se ha convertido en una tarea desafiante. La detección y el diagnóstico de fallos (FDD), como un elemento clave de la supervisión del proceso, deben ser investigados debido a su papel esencial en los procesos de toma de decisiones. Entre los métodos disponibles de FDD, los enfoques basados en datos están recibiendo una atención creciente debido a su relativa simplicidad en la implementación. Independientemente de los tipos de FDD, una de las principales características de los sistemas FDD confiables es su capacidad de actualización, mientras que las nuevas condiciones que no fueron consideradas en su entrenamiento inicial, ahora aparecen en el proceso. Estas nuevas condiciones pueden surgir de forma gradual o abrupta, pero tienen el mismo nivel de importancia ya que en ambos casos conducen al bajo rendimiento de FDD. Para abordar las tareas de actualización, se han propuesto algunos métodos, pero no mayoritariamente en el área de investigación de la ingeniería química. Podrían ser categorizados en los que están dedicados a manejar Concept Drift (CD) (que aparecen gradualmente), y a los que tratan con clases nuevas (que aparecen abruptamente). Los métodos disponibles, además de la falta de estrategias claras para la actualización, sufren debilidades en su funcionamiento y de un tiempo de capacitación ineficiente, como se ha referenciado. En consecuencia, esta tesis está dedicada principalmente a la actualización de FDD impulsada por datos en procesos químicos. Los esquemas propuestos para manejar nuevas clases de fallos se basan en métodos no supervisados, mientras que para hacer frente a la CD se han investigado los marcos de actualización supervisados y no supervisados. Además, para mejorar la funcionalidad de los sistemas FDD, se han investigado algunos de los principales métodos de procesamiento de datos, incluida la imputación de valores perdidos, la selección de características y la extensión de características. Los algoritmos y marcos sugeridos para la actualización de FDD han sido evaluados a través de diferentes puntos de referencia y escenarios. Como parte de los resultados, los algoritmos sugeridos para el CD de manejo supervisado superan el rendimiento del aprendizaje incremental tradicional con respecto al puntaje MGM (puntuación adimensional definida basada en el puntaje F1 ponderado y el tiempo de entrenamiento) hasta en un 50% de mejora. Esta mejora se logra mediante los algoritmos propuestos que detectan y olvidan la información redundante, así como ajustan correctamente la ventana de datos para la actualización oportuna y el reciclaje del sistema de detección de fallas. Además, el marco de actualización FDD no supervisado propuesto para tratar fallas nuevas en condiciones de proceso estáticas y dinámicas logra hasta 90% en términos de la puntuación de NPP (puntuación adimensional definida basada en el número de la clase de muestras correcta predicha). Este resultado se basa en un marco innovador que puede asignar muestras a clases nuevas o a clases disponibles explotando una clase de técnicas de clasificación y enfoques de agrupamiento
Li, Zhengwei. « Adaptable, scalable, probabilistic fault detection and diagnostic methods for the HVAC secondary system ». Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43653.
Texte intégralKline, Paul A. « Fault detection and isolation for integrated navigation systems using the global positioning system ». Ohio : Ohio University, 1991. http://www.ohiolink.edu/etd/view.cgi?ohiou1183731476.
Texte intégralAdewole, Adeyemi Charles. « Investigation of methodologies for fault detection and diagnosis in electric power system protection ». Thesis, Cape Peninsula University of Technology, 2012. http://hdl.handle.net/20.500.11838/1273.
Texte intégralThe widespread deregulation and restructuring of electric power utilities throughout the world and the surge in competition amongst utility companies has brought about the desire for improved economic efficiency of electric utilities and the provision of better service to energy consumers. These end users are usually connected to the distribution network. Thus, there is a growing research interest in distribution network fault detection and diagnosis algorithms for reducing the down-time due to faults. This is done so as to improve the reliability indices of utility companies and enhance the availability of power supply to customers. The application of signal processing and computational intelligence techniques in power systems protection, automation, and control cannot be overemphasized. This research work focuses on power system distribution network and is aimed at the development of versatile algorithms capable of accurate fault detection and diagnosis of all fault types for operation in balanced/unbalanced distribution networks, under varying fault resistances, fault inception angles, load angles, and system operating conditions. Therefore, different simulation scenarios encompassing various fault types at several locations with different load angles, fault resistances, fault inception angles, capacitor switching, and load switching were applied to the IEEE 34 Node Test Feeder in order to generate the data needed. In particular, the effects of system changes were investigated by integrating various Distributed Generators (DGs) into the distribution feeder. The length of the feeder was also extended and investigations carried out. This was implemented by modelling the IEEE 34-node benchmark test feeder in DIgSILENT PowerFactory (DPF). In the course of this research, a hybrid combination of Discrete Wavelet Transform (DWT), decision-taking rule-based algorithms, and Artificial Neural Networks (ANNs) algorithms for electric power distribution network fault detection and diagnosis was developed. The integrated algorithms were capable of fault detection, fault type classification, identification of the faulty line segment, and fault location respectively. Several scenarios were simulated in the test feeder. The resulting waveforms were exported as ASCII or COMTRADE files to MATLAB for DWT signal processing. Experiments with various DWT mother wavelets were carried out on the waveforms obtained from the simulations. In particular, Daubechies db-2, db-3, db-4, db-5, and db-8 were considered. Others are Coiflet-3 and Symlet-4 mother wavelets respectively. The energy and entropy of the detail coefficients for each decomposition level based on a sampling frequency of 7.68 kHz were analysed. The best decomposition level for the diagnostic tasks was then selected based on the analysis of the wavelet energies and entropy in each level of decomposition. Consequently, level-1 db-4 detail coefficients were selected for the fault detection task, while level-5 db4 detail coefficients were used to compute the wavelet entropy per unit indices which were then used for fault classification, fault section identification, and fault location tasks respectively. Decision-taking rule-based algorithms were used for the fault detection and fault classification tasks respectively. The fault detection task verifies if a fault did indeed occur or not, while the fault classification task determines the fault class and the faulted phase(s). Similarly, Artificial Neural Networks (ANNs) were used for the fault section identification and fault location tasks respectively. For the fault section identification task, the ANNs were trained for pattern classification to identify the lateral or segment affected by the fault. Conversely, the fault location ANNs were trained for function approximation to predict the location of the fault from the substation in kilometres. Also, the IEEE 13 Node Benchmark Test Feeder was modelled in RSCAD software and batch mode simulations were carried out using the Real-Time Digital Simulator (RTDS) as a ‘proof of concept’ for the proposed method, in order to demonstrate the scalability, and to further validate the developed algorithms. The COMTRADE files of disturbance records retrieved from an external IED connected in closed-loop with the RTDS and the runtime simulation waveforms were used as test inputs to the developed Hybrid Fault Detection and Diagnosis (HFDD) method. Comparison of the method based on entropy with statistical methods based on standard deviation and Mean Absolute Deviation (MAD) has shown that the method based on entropy is very reliable, accurate, and robust. Results of preliminary studies carried out showed that the proposed HFDD method can be applied to any power system network irrespective of changes in the operating characteristics. However, certain decision indices would change and the decision-taking rules and ANN algorithms would need to be updated. The HFDD method is promising and would serve as a useful decision support tool for system operators and engineers to aid them in fault diagnosis thereby helping to reduce system down-time and improve the reliability and availability of electric power supply. Key words: Artificial neural network, discrete wavelet transform, distribution network, fault simulation, fault detection and diagnosis, power system protection, RTDS.
Jaradat, Mohammad Abdel Kareem Rasheed. « A hybrid system for fault detection and sensor fusion based on fuzzy clustering and artificial immune systems ». Texas A&M University, 2005. http://hdl.handle.net/1969.1/4780.
Texte intégralXiao, Wenchang. « Structural Health Monitoring and Fault Diagnosis based on Artificial Immune System ». Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/169.
Texte intégralWeerasinghe, Manori. « Fault detection and diagnosis for complex multivariable processes using neural networks ». Thesis, Liverpool John Moores University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.298141.
Texte intégralRaju, Madhanmohan. « Group based fault-tolerant physical intrusion detection system using fuzzy based distributed RSSI processing ». University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1393237072.
Texte intégralPelo, Herbert Leburu. « Evaluation of an advanced fault detection system using Koeberg nuclear power plant data / H.L. Pelo ». Thesis, North-West University, 2013. http://hdl.handle.net/10394/9686.
Texte intégralThesis (MSc (Engineering Sciences in Nuclear Engineering))--North-West University, Potchefstroom Campus, 2013.
Wong, Kam Cheung. « Intelligent methods of power system components monitoring by artificial neural networks and optimisation using evolutionary computing techniques ». Thesis, University of Sunderland, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285580.
Texte intégralSepasi, Mohammad. « Fault monitoring in hydraulic systems using unscented Kalman filter ». Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/206.
Texte intégralMoncayo, Hever Y. « Immunity-based detection, identification, and evaluation of aircraft sub-system failures ». Morgantown, W. Va. : [West Virginia University Libraries], 2009. http://hdl.handle.net/10450/10678.
Texte intégralTitle from document title page. Document formatted into pages; contains xiv, 118 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 109-118).
Pietersen, Willem Hermanus. « System identification for fault tolerant control of unmanned aerial vehicles ». Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/4164.
Texte intégralENGLISH ABSTRACT: In this project, system identification is done on the Modular Unmanned Aerial Vehicle (UAV). This is necessary to perform fault detection and isolation, which is part of the Fault Tolerant Control research project at Stellenbosch University. The equations necessary to do system identification are developed. Various methods for system identification is discussed and the regression methods are implemented. It is shown how to accommodate a sudden change in aircraft parameters due to a fault. Smoothed numerical differentiation is performed in order to acquire data necessary to implement the regression methods. Practical issues regarding system identification are discussed and methods for addressing these issues are introduced. These issues include data collinearity and identification in a closed loop. The regression methods are implemented on a simple roll model of the Modular UAV in order to highlight the various difficulties with system identification. Different methods for accommodating a fault are illustrated. System identification is also done on a full nonlinear model of the Modular UAV. All the parameters converges quickly to accurate values, with the exception of Cl R , CnP and Cn A . The reason for this is discussed. The importance of these parameters in order to do Fault Tolerant Control is also discussed. An S-function that implements the recursive least squares algorithm for parameter estimation is developed. This block accommodates for the methods of applying the forgetting factor and covariance resetting. This block can be used as a stepping stone for future work in system identification and fault detection and isolation.
AFRIKAANSE OPSOMMING: In hierdie projek word stelsel identifikasie gedoen op die Modulêre Onbemande Vliegtuig. Dit is nodig om foutopsporing en isolasie te doen wat ’n deel uitmaak van fout verdraagsame beheer. Die vergelykings wat nodig is om stelsel identifikasie te doen is ontwikkel. Verskeie metodes om stelsel identifikasie te doen word bespreek en die regressie metodes is uitgevoer. Daar word gewys hoe om voorsiening te maak vir ’n skielike verandering in die vliegtuig parameters as gevolg van ’n fout. Reëlmatige numeriese differensiasie is gedoen om data te verkry wat nodig is vir die uitvoering van die regressie metodes. Praktiese kwessies aangaande stelsel identifikasie word bespreek en metodes om hierdie kwessies aan te spreek word gegee. Hierdie kwessies sluit interafhanklikheid van data en identifikasie in ’n geslote lus in. Die regressie metodes word toegepas op ’n eenvoudige rol model van die Modulêre Onbemande Vliegtuig om die verskeie kwessies aangaande stelsel identifikasie uit te wys. Verskeie metodes vir die hantering vir ’n fout word ook illustreer. Stelsel identifikasie word ook op die volle nie-lineêre model van die Modulêre Onbemande Vliegtuig gedoen. Al die parameters konvergeer vinnig na akkurate waardes, met die uitsondering van Cl R , CnP and Cn A . Die belangrikheid van hierdie parameters vir fout verdraagsame beheer word ook bespreek. ’n S-funksie blok vir die rekursiewe kleinste-kwadraat algoritme is ontwikkel. Hierdie blok voorsien vir die metodes om die vergeetfaktor en kovariansie herstelling te implementeer. Hierdie blok kan gebruik word vir toekomstige werk in stelsel identifikasie en foutopsporing en isolasie.
Andrade, Vasco Brogueira. « Fault Detection and Performance Monitoring in PV Systems ». Master's thesis, 2017. https://repositorio-aberto.up.pt/handle/10216/91023.
Texte intégralAndrade, Vasco Brogueira. « Fault Detection and Performance Monitoring in PV Systems ». Dissertação, 2017. https://repositorio-aberto.up.pt/handle/10216/91023.
Texte intégral