Implementation of an intelligent control system

A laboratory testbed facility which was constructed at NASA LeRC for the development of an Intelligent Control System (ICS) for reusable rocket engines is described. The framework of the ICS consists of a hierarchy of various control and diagnostic functions. The traditional high speed, closed-loop controller resides at the lowest level of the ICS hierarchy. Above this level resides the diagnostic functions which identify engine faults. The ICS top level consists of the coordination function which manages the interaction between an expert system and a traditional control system. The purpose of the testbed is to demonstrate the feasibility of the OCS concept by implementing the ICS as the primary controller in a simulation of the Space Shuttle Main Engine (SSME). The functions of the ICS which are implemented in the testbed are as follows: an SSME dynamic simulation with selected fault mode models, a reconfigurable controller, a neural network for sensor validation, a model-based failure detection algorithm, a rule based failure detection algorithm, a diagnostic expert system, an intelligent coordinator, and a user interface which provides a graphical representation of the event occurring within the testbed. The diverse nature of the ICS has led to the development of a distributed architecture consisting of specialized hardware and software for the implementation of the various functions. This testbed is made up of five different computer systems. These individual computers are discussed along with the schemes used to implement the various ICS components. The communication between computers and the timing and synchronization between components are also addressed

Fault analysis using state-of-the-art classifiers

Fault Analysis is the detection and diagnosis of malfunction in machine operation or process control. Early fault analysis techniques were reserved for high critical plants such as nuclear or chemical industries where abnormal event prevention is given utmost importance. The techniques developed were a result of decades of technical research and models based on extensive characterization of equipment behavior. This requires in-depth knowledge of the system and expert analysis to apply these methods for the application at hand. Since machine learning algorithms depend on past process data for creating a system model, a generic autonomous diagnostic system can be developed which can be used for application in common industrial setups. In this thesis, we look into some of the techniques used for fault detection and diagnosis multi-class and one-class classifiers. First we study Feature Selection techniques and the classifier performance is analyzed against the number of selected features. The aim of feature selection is to reduce the impact of irrelevant variables and to reduce computation burden on the learning algorithm. We introduce the feature selection algorithms as a literature survey. Only few algorithms are implemented to obtain the results. Fault data from a Radio Frequency (RF) generator is used to perform fault detection and diagnosis. Comparison between continuous and discrete fault data is conducted for the Support Vector Machines (SVM) and Radial Basis Function Network (RBF) classifiers. In the second part we look into one-class classification techniques and their application to fault detection. One-class techniques were primarily developed to identify one class of objects from all other possible objects. Since all fault occurrences in a system cannot be simulated or recorded, one-class techniques help in identifying abnormal events. We introduce four one-class classifiers and analyze them using Receiver-Operating Characteristic (ROC) curve. We also develop a feature extraction method for the RF generator data which is used to obtain results for one-class classifiers and Radial Basis Function Network two class classification. To apply these techniques for real-time verification, the RIT Fault Prediction software is built. LabView environment is used to build a basic data management and fault detection using Radial Basis Function Network. This software is stand alone and acts as foundation for future implementations

Sensor and Sensorless Fault Tolerant Control for Induction Motors Using a Wavelet Index

Fault Tolerant Control (FTC) systems are crucial in industry to ensure safe and reliable operation, especially of motor drives. This paper proposes the use of multiple controllers for a FTC system of an induction motor drive, selected based on a switching mechanism. The system switches between sensor vector control, sensorless vector control, closed-loop voltage by frequency (V/f) control and open loop V/f control. Vector control offers high performance, while V/f is a simple, low cost strategy with high speed and satisfactory performance. The faults dealt with are speed sensor failures, stator winding open circuits, shorts and minimum voltage faults. In the event of compound faults, a protection unit halts motor operation. The faults are detected using a wavelet index. For the sensorless vector control, a novel Boosted Model Reference Adaptive System (BMRAS) to estimate the motor speed is presented, which reduces tuning time. Both simulation results and experimental results with an induction motor drive show the scheme to be a fast and effective one for fault detection, while the control methods transition smoothly and ensure the effectiveness of the FTC system. The system is also shown to be flexible, reverting rapidly back to the dominant controller if the motor returns to a healthy state

Fault behaviour and fault detection in islanded inverter-only microgrids

The increase in popularity of the microgrid concept requires the analysis and solution of the numerous technical issues arising from the operation and integration of the microgrid into the original distribution network. The work presented in this thesis is centred on the study of the fault behaviour of inverter-only microgrids and on the development of a suitable fault detection technique. This task is approached by first understanding the behaviour of a microgrid during a fault and the factors affecting it. A complete description and analysis of the key elements in the study of microgrid fault behaviour is presented. Then, three microgrid models with different inverter control methods (i.e. Synchronous Reference Frame control, Natural Reference Frame control and droop control) and with various current limiting strategies are built in PSCAD and their fault behaviour is simulated, analyzed and compared. It is found that the control of the inverter is able to shape the response of the microgrid in the event of a fault. The constraints to this capability are the inverter’s ratings (current and voltage limits) and the characteristic changes in the network introduced by faults. Moreover, it is found that the control in the Natural Reference Frame gives better fault response, in terms of voltage control and simplicity in implementation, compared with the popular control in the Synchronous Reference Frame. The behaviour of the system is then further analyzed by developing quasi steadystate inverter models suitable for numerical fault analysis. The models are developed starting from the inverter control and analyzing how it changes in the event of a fault. By combining control gains and circuit parameters, they result in being capable of capturing the key features of inverters’ fault behaviour. Depending on the control strategy, some of these models are balanced and therefore are directly applicable in numerical fault analysis based on sequence components. Others are unbalanced and therefore require a fault analysis based on a direct phase coordinates representation of the network. Examples on how to perform numerical fault analysis calculations with balanced and unbalanced models are given and the numerical results well compare with the ones obtained from time-domain simulations using PSCAD. From the knowledge of the microgrid fault behaviour developed analyzing the responses in time-domain simulations and by using the developed inverter models to numerically calculate voltages and currents in the microgrid during different faults at various locations, a fault detection strategy based on voltage sequence components is proposed. Indeed, it is the behaviour of the inverter control during faults which makes the monitoring of voltage sequence components the best discriminator between normal operation and fault operation. The three building blocks of the fault detection strategy which are capable of a fast extraction and comparison of voltage sequence components are described and then the performance of the fault detection strategy for different faults and microgrid operating conditions is tested in PSCAD and discussed. Finally, examples are given on how this voltage detection can be used in the design of a microgrid protection system

Methods of Handling Missing Data in One Shot Response Based Power System Control

Indiana University-Purdue University Indianapolis (IUPUI)The thesis extends the work done in [1] [2] by Rovnyak, et al. where the authors have described about transient event prediction and response based one shot control using decision trees trained and tested in a 176 bus model of WECC power system network. This thesis contains results from rigorous simulations performed to measure robustness of the existing one shot control subjected to missing PMU's data ranging from 0-10%. We can divide the thesis into two parts in which the first part includes understanding of the work done in [2] using another set of one-shot control combinations labelled as CC2 and the second part includes measuring their robustness while assuming missing PMU's data. Previous work from [2] involves use of decision trees for event detection based on different indices to classify a contingency as a 'Fault' or 'No fault' and another set of decision trees that decides either to actuate 'Control' or 'No control'. The actuation of control here means application of one-shot control combination to possibly bring the system to a new equilibrium point which would otherwise attain loss of synchronism. The work done in [2] also includes assessing performance of the one shot control without event detection. The thesis is organized as follows- Chapter 1 of the thesis highlights the effect of missing PMUs' data in a power system network and the need to address them appropriately. It also provides a general idea of transient stability and response of a transient fault in a power system. Chapter 2 forms the foundation of the thesis as it describes the work done in [1] [2] in detail. It describes the power system model used, contingencies set, and different indices used for decision trees. It also describes about the one shot control combination (CC1) deduced by Rovnyak, et.al. of which performance is later tested in this thesis assuming different missing data scenarios. In addition to CC1, the chapter also describes another set of control combination (CC2) whose performance is also tested assuming the same missing data scenarios. This chapter also explains about the control methodology used in [2]. Finally the performance metrics of the DTs are explained at the end of the chapter. These are the same performance metrics used in [2] to measure the robustness of the one shot control. Chapter 2 is thus more a literature review of previous work plus inclusion of few simulation results obtained from CC2 using exactly the same model and same control methodology. Chapter 3 describes different techniques of handling missing data from PMUs most of which have been used in and referred from different previous papers. Finally Chapter 4 presents the results and analysis of the simulation. The thesis is wrapped up explaining future enhancements and room for improvements

Controlo tolerante a falhas de um canal de rega

A presente dissertação pretende conceber e implementar um sistema de controlo tolerante a falhas, no canal experimental de rega da Universidade de Évora, utilizando um modelo implementado em MATLAB/SIMULINK®. Como forma de responder a este desafio, analisaram-se várias técnicas de diagnóstico de falhas, tendo-se optado por técnicas baseadas em redes neuronais para o desenvolvimento de um sistema de detecção e isolamento de falhas no canal de rega, sem ter em conta o tipo de sistema de controlo utilizado. As redes neuronais foram, assim, os processadores não lineares utilizados e mais aconselhados em situações onde exista uma abundância de dados do processo, porque aprendem por exemplos e são suportadas por teorias estatísticas e de optimização, focando não somente o processamento de sinais, como também expandindo os horizontes desse processamento. A ênfase dos modelos das redes neuronais está na sua dinâmica, na sua estabilidade e no seu comportamento. Portanto, o trabalho de investigação do qual resultou esta Dissertação teve como principais objectivos o desenvolvimento de modelos de redes neuronais que representassem da melhor forma a dinâmica do canal de rega, de modo a obter um sistema de detecção de falhas que faça uma comparação entre os valores obtidos nos modelos e no processo. Com esta diferença de valores, da qual resultará um resíduo, é possível desenvolver tanto o sistema de detecção como de isolamento de falhas baseados nas redes neuronais, possibilitando assim o desenvolvimento dum sistema de controlo tolerante a falhas, que engloba os módulos de detecção, de isolamento/diagnóstico e de reconfiguração do canal de rega. Em síntese, na Dissertação realizada desenvolveu-se um sistema que permite reconfigurar o processo em caso de ocorrência de falhas, melhorando significativamente o desempenho do canal de rega.The present dissertation intends to design and implement a fault-tolerant control system , in the experimental irrigation canal of the University of Évora, using an implemented model in MATLAB/SIMULINK®. As a way to respond to that challenge, several fault diagnosis techniques were analyzed , having been chosen techniques based on neural networks to the development of a fault-detection and Isolation system in the irrigation canal, not taking into account the kind of control system used. The neural networks were, indeed, the nonlinear processors used and more recommended in situations where there is an abundant process data, because they learn by examples and are supported by statistical and optimization theories, focusing not only on the processing of signals, but also on the expansion of the processing horizons. The emphasis of the models of neural networks is in its dynamics, its stability and its behavior. Thus, the research work which resulted in this dissertation had as its main goal the development of neural networks models that could represent in the best way the irrigation canal dynamics under study , in order to obtain a fault-detection system that can make a comparison between the resulting values in the models and the process. With this difference of values, from which a residue will result, it is possible to develop both a fault-detection and an Isolation system based on neural networks, providing the development of a fault-tolerant control system that comprehends the detection modules, the isolation/diagnostic and the system reconfiguration modules. Summing up, this dissertation led to a system that allows reconfiguring the processin the event of such faults, improving significantly the performance of the irrigation canal

Agent-Based Faults Monitoring in Automatic Teller Machines

Automated Teller Machine (ATM) has gained widespread acceptance as a convenient medium to facilitate financialtransaction without need for human agent. However, ATM deployers are facing challenges in maximizing the uptime of theirATMs as a result of wide gap in fault detection, notification and correction of the ATMs. One way to ameliorate thissituation is through intelligent monitoring of ATM by resident software agents that monitor the device real time and reportfaulty components real time to facilitate quick response. We proposed an architecture for rule-based, intelligent agent basedmonitoring and management of ATMs. Agents are used to perform remote monitoring on the ATMs and control functionsuch software maintenance. Such agents can detect basic events or correlate existing events that are stored in a database todetect faults. A system administrator can securely modify the monitoring policies and control functions of agents. Theframework presented here includes software fault monitor, hardware fault monitor and transaction monitor. A set of utilitysupport agents: caller agent and log agent are used to alert network operator and log error and transaction information in adatabase respectively. at-1, stuck-at-0 faults in digital circuits validate the point that faulty circuits dissipates more andhence draw more power.Key words: Automated Teller Machine (ATM), Intelligent Agents, Mobile Agents, Event Monitoring

Architecture Level Safety Analyses for Safety-Critical Systems

The dependency of complex embedded Safety-Critical Systems across Avionics and Aerospace domains on their underlying software and hardware components has gradually increased with progression in time. Such application domain systems are developed based on a complex integrated architecture, which is modular in nature. Engineering practices assured with system safety standards to manage the failure, faulty, and unsafe operational conditions are very much necessary. System safety analyses involve the analysis of complex software architecture of the system, a major aspect in leading to fatal consequences in the behaviour of Safety-Critical Systems, and provide high reliability and dependability factors during their development. In this paper, we propose an architecture fault modeling and the safety analyses approach that will aid in identifying and eliminating the design flaws. The formal foundations of SAE Architecture Analysis & Design Language (AADL) augmented with the Error Model Annex (EMV) are discussed. The fault propagation, failure behaviour, and the composite behaviour of the design flaws/failures are considered for architecture safety analysis. The illustration of the proposed approach is validated by implementing the Speed Control Unit of Power-Boat Autopilot (PBA) system. The Error Model Annex (EMV) is guided with the pattern of consideration and inclusion of probable failure scenarios and propagation of fault conditions in the Speed Control Unit of Power-Boat Autopilot (PBA). This helps in validating the system architecture with the detection of the error event in the model and its impact in the operational environment. This also provides an insight of the certification impact that these exceptional conditions pose at various criticality levels and design assurance levels and its implications in verifying and validating the designs

Advanced fault diagnosis techniques and their role in preventing cascading blackouts

This dissertation studied new transmission line fault diagnosis approaches using new technologies and proposed a scheme to apply those techniques in preventing and mitigating cascading blackouts. The new fault diagnosis approaches are based on two time-domain techniques: neural network based, and synchronized sampling based. For a neural network based fault diagnosis approach, a specially designed fuzzy Adaptive Resonance Theory (ART) neural network algorithm was used. Several ap- plication issues were solved by coordinating multiple neural networks and improving the feature extraction method. A new boundary protection scheme was designed by using a wavelet transform and fuzzy ART neural network. By extracting the fault gen- erated high frequency signal, the new scheme can solve the difficulty of the traditional method to differentiate the internal faults from the external using one end transmis- sion line data only. The fault diagnosis based on synchronized sampling utilizes the Global Positioning System of satellites to synchronize data samples from the two ends of the transmission line. The effort has been made to extend the fault location scheme to a complete fault detection, classification and location scheme. Without an extra data requirement, the new approach enhances the functions of fault diagnosis and improves the performance. Two fault diagnosis techniques using neural network and synchronized sampling are combined as an integrated real time fault analysis tool to be used as a reference of traditional protective relay. They work with an event analysis tool based on event tree analysis (ETA) in a proposed local relay monitoring tool. An interactive monitoring and control scheme for preventing and mitigating cascading blackouts is proposed. The local relay monitoring tool was coordinated with the system-wide monitoring and control tool to enable a better understanding of the system disturbances. Case studies were presented to demonstrate the proposed scheme. An improved simulation software using MATLAB and EMTP/ATP was devel- oped to study the proposed fault diagnosis techniques. Comprehensive performance studies were implemented and the test results validated the enhanced performance of the proposed approaches over the traditional fault diagnosis performed by the transmission line distance relay