A major problem with alarm processing and fault diagnosis in power systems is the reliance on the circuit alarm status. If there is too much information available and the time of arrival of the information is random due to weather conditions etc., the alarm activity is not easily interpreted by system operators. In respect of these problems, this thesis sets out the work that has been carried out to design and evaluate a diagnostic tool which assists power system operators during a heavy period of alarm activity in condition monitoring. The aim of employing this diagnostic tool is to monitor and raise uncertain alarm information for the system operators, which serves a proposed solution for restoring such faults. The diagnostic system uses elements of AI namely expert systems, and fuzzy logic that incorporate abductive reasoning. The objective of employing abductive reasoning is to optimise an interpretation of Supervisory Control and Data Acquisition (SCADA) based uncertain messages when the SCADA based messages are not satisfied with simple logic alone. The method consists of object-oriented programming, which demonstrates reusability, polymorphism, and readability. The principle behind employing objectoriented techniques is to provide better insights and solutions compared to conventional artificial intelligence (Al) programming languages. The characteristics of this work involve the development and evaluation of a fuzzy-expert system which tries to optimise the uncertainty in the 16-lines 12-bus sample power system. The performance of employing this diagnostic tool is assessed based on consistent data acquisition, readability, adaptability, and maintainability on a PC. This diagnostic tool enables operators to control and present more appropriate interpretations effectively rather than a mathematical based precise fault identification when the mathematical modelling fails and the period of alarm activity is high. This research contributes to the field of power system control, in particular Scottish Hydro-Electric PLC has shown interest and supplied all the necessary information and data. The AI based power system is presented as a sample application of Scottish Hydro-Electric and KEPCO (Korea Electric Power Corporation).

It is generally accepted that transient faults are a major cause of failure in micro processor systems. Industrial controllers with embedded microprocessors are particularly at risk from this type of failure because their working environments are prone to transient disturbances which can generate transient faults. In order to improve the reliability of processor systems for industrial applications within a limited budget, fault tolerant techniques for uniprocessors are implemented. These techniques aim to identify characteristics of processor operation which are attributed to erroneous behaviour. Once detection is achieved, a programme of restoration activity can be initiated. This thesis initially develops a previous model of erroneous microprocessor behaviour from which characteristics particular to mal-operation are identified. A new technique is proposed, based on software implemented fault tolerance which, by recognizing a particular behavioural characteristic, facilitates the self-detection of erroneous execution. The technique involves inserting detection mechanisms into the target software. This can be quite a complex process and so a prototype software tool called Post-programming Automated Recovery UTility (PARUT) is developed to automate the technique's application. The utility can be used to apply the proposed behavioural fault tolerant technique for a selection of target processors. Fault injection and emulation experiments assess the effectiveness of the proposed fault tolerant technique for three application programs implemented on an 8, 16, and 32- bit processors respectively. The modified application programs are shown to have an improved detection capability and hence reliability when the proposed fault tolerant technique is applied. General assessment of the technique cannot be made, however, because its effectiveness is application specific. The thesis concludes by considering methods of generating non-hazardous application programs at the compilation stage, and design features for incorporation into the architecture of a microprocessor which inherently reduce the hazard, and increase the detection capability of the target software. Particular suggestions are made to add a 'PARUT' phase to the translation process, and to orientate microprocessor design towards the instruction opcode map.

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In the first part of the work, we consider the problem of giving upper bounds for Ix(T) - z(T)I, the error between the final states of a nominal finite dimensional system x = Ax + Bu, x(O) = Xo, and of the system disturbed by multiple structured perturbations of the form r z(t) = Az(t) + LDkFk(CkZ(t), t) + Bu(t) k=l which accounts for the uncertainties on the entries of the matrix A. In approaching the problem we introduce a framework which involves some weight-functions and provides a scaling technique that allows for enlarging the class of perturbations and for getting lower bounds for the error. In the second part, we contribute towards the problem of robustness of stability of i: = Ax. To account for the uncertainties we consider linear but time-varying structured perturbations yielding the disturbed system z(O) = x; i: = Ax + BD(t)Cx x(O) = Xo We determine the real time-varying stability radius rR,t = {IIDIILoo j the equilibrium of (*) is not asymptotically stable} for the linear oscillator by means of a special algorithm. Also we study its asymptotic behaviour for small dampings by using an averaging method. Finally we study n-dimensional systems under periodic perturbations and give a result which generalises the characterisation of destabilising perturbation from time-invariant to that of time-varying periodic perturbations.

We consider the problem of robustness, in particular that of robust stability. Such a problem is amenable to analysis by frequency domain techniques, and also using state space methods. Using some recent state space theory yielding the exact radius of the ball around a nominally stable system within which all additive perturbations retain stability, we show how control action may be implemented to increase the radius of this ball. We present further some material on how destabilizing perturbations may be constructed from solutions of Riccati equations, and how the above mentioned radii may be found with respect to an alternative norm to the one used above. Finally we give some remarks on the use of Lyapunov functions for systems.

The results contained within this thesis concern an abstract framework for a robustness analysis of exponential stability of infinite dimensional systems. The abstract analysis relies on the strong relationship between exponential stability and L2-stability which exists for many classes of linear systems. In Chapter 1a "stability radius", for systems governed by semigroups, is developed, for a class of "structured" perturbations of its generator. The abstract theory is illustrated by examples of perturbations of the boundary data for homogeneous boundary value problems and also perturbations arising due to neglected delay terms in differential delay equations. In Chapter 2a related problem of a non standard linear quadratic problem is studied, which leads to a stability analysis for certain nonlinear systems. In Chapter 3 an abstract L2-stability theory is developed and then applied to integrodifferential equations and time-varying systems, to investigate the robustness of exponential stability of such systems.

The past few years have witnessed a rapid growth in the use of fuzzy logic controllers for the control of processes which are complex and ill-defined. These control systems, inspired by the approximate reasoning capabilities of humans under conditions of uncertainty and imprecision, consist of linguistic 'if-then' rules which depend on fuzzy set theory for representation and evaluation using computers. Even though the fuzzy rules can be built from purely heuristic knowledge such as a human operator's control strategy, a number of difficulties face the designer of such systems. For any reasonably complex chemical process, the number of rules required to ensure adequate control in all operating regions may be extremely large. Eliciting all of these rules and ensuring their consistency and completeness can be a daunting task. An alternative to modelling the operator's response is to model the process and then to incorporate the process model into some sort of model-based control scheme. The concept of Model Based Predictive Control (MB PC) has been heralded as one of the most significant control developments in recent years. It is now widely used in the chemical and petrochemical industry and it continues to attract a considerable amount of research. Its popularity can be attributed to its many remarkable features and its open methodology. The wide range of choice of model structures, prediction horizon and optimisation criteria allows the control designer to easily tailor MBPC to his application. Features sought from such controllers include better performance, ease of tuning, greater robustness, ability to handle process constraints, dead time compensation and the ability to control nonminimum phase and open loop unstable processes. The concept of MBPC is not restricted to single-input single-output (SISO) processes. Feedforward action can be introduced easily for compensation of measurable disturbances and the use of state-space model formulation allows the approach to be generalised easily to multi-input multi-output (MIMO) systems. Although many different MBPC schemes have emerged, linear process models derived from input-output data are often used either explicitly to predict future process behaviour and/or implicitly to calculate the control action even though many chemical processes exhibit nonlinear process behaviour. It is well-recognised that the inherent nonlinearity of many chemical processes presents a challenging control problem, especially where quality and/or economic performance are important demands. In this thesis, MBPC is incorporated into a nonlinear fuzzy modelling framework. Even though a control algorithm based on a 1-step ahead predictive control strategy has initially been examined, subsequent studies focus on determining the optimal controller output using a long-range predictive control strategy. The fuzzy modelling method proposed by Takagi and Sugeno has been used throughout the thesis. This modelling method uses fuzzy inference to combine the outputs of a number of auto-regressive linear sub-models to construct an overall nonlinear process model. The method provides a more compact model (hence requiring less computations) than fuzzy modelling methods using relational arrays. It also provides an improvement in modelling accuracy and effectively overcomes the problems arising from incomplete models that characterise relational fuzzy models. Difficulties in using traditional cost function and optimisation techniques with fuzzy models have led other researchers to use numerical search techniques for determining the controller output. The emphasis in this thesis has been on computationally efficient analytically derived control algorithms. The performance of the proposed control system is examined using simulations of the liquid level in a tank, a continuous stirred tank reactor (CSTR) system, a binary distillation column and a forced circulation evaporator system. The results demonstrate the ability of the proposed system to outperform more traditional control systems. The results also show that inspite of the greatly reduced computational requirement of our proposed controller, it is possible to equal or better the performance of some of the other fuzzy model based control systems that have been proposed in the literature. It is also shown in this thesis that the proposed control algorithm can be easily extended to address the requirements of time-varying processes and processes requiring compensation for disturbance inputs and dead times. The application of the control system to multivariable processes and the ability to incorporate explicit constraints in the optimisation process are also demonstrated.

The advent of microprocessors has created the possibility of developing low cost adaptive controllers for small process plants which in the past badly needed but could not afford such controllers. To examine the practicality of developing advanced low cost microprocessor based controller, this thesis describes the development of a non-linear adaptive controller for a nylon crimping plant which is a typical example of small process plants. In order to test the algorithm on site, an algorithm development/implement device basing on a novel multi-tasking concept was developed. This novel microprocessor based device can perform program development, on-line algorithm test and data logging at the same time, while, still maintaining its small size for easy transportation. When the control algorithm was fully developed and tested, a low cost dedicated controller using an Intel 8085 processor was designed to house the algorithm and as a direct replacement of the original analogue controller.

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No description available.