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In this thesis a transformer fault diagnosis system using probabilistic neural network (PNN) is proposed and implemented. Many artificial neural networks (ANN) have been proposed to deal with the transformer fault diagnosis. However, when dissolved gas records change, adaptation capability becomes a problem in ANN applications. PNN analyzes the dissolved gas contents in the oil-immersed transformer to identify various fault types. Numerical gas ratios of oil and cellulose decomposition were used to create the training examples. Retraining can also be done by adding new examples without any iteration. With diagnostic gas records, computer simulations were conducted to show the effectiveness of the proposed system. The Internet based power transformer monitoring system was also proposed in this thesis . LabVIEW was used to develop the Man-Machine Interface (MMI), and DataSocket tool was used to share the information on Internet. Application of the harmonic load flow based on the Equivalent- Current Injection was used to solve harmonic problems. There are two sub-models including the fundamental and harmonic frequency models. The standard Fourier analysis was used to deal with the harmonic loads to get injection currents. A passive filter was also developed to improve harmonics to satisfy restriction standards of the Taiwan Power Company.

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The focus of the work in this thesis is concerned with the investigation and development of indirect measurement techniques. The methodology adopted is a combination of practical experimental, analytical deductive reasoning and simulation studies. This has led to proposals for a number of indirect tyre pressure monitoring systems, which are able to detect pressure loss under specific circumstances. The outcome overall is a proposal for a new supervisory system comprising of a modular framework, allowing various algorithms and techniques to be implemented in a complementary manner as they emerge and data sources become available. A number of contributions to the field have been made, which to the knowledge of the author, provide potential for further algorithm development and are imminently applicable given the above. The methods include a tyre pressure diagnosis via a wheel angular velocity comparator, the development of a model-based tyre pressure diagnosis via application of an unknown input observer and a parameter estimation scheme, a model-based tyre pressure diagnosis approach via an enhanced Kalman filter configured to estimate states including the input, a model-based tyre pressure diagnosis via cautious least squares, an investigation and critique of the effects of the choice of sampling interval on discrete-time models and estimation thereof. It is considered, that the extensive literature review provides a valuable historic insight into the tyre fault detection problem. It is clear, from the development and testing of the algorithms (and also the literature), that no single indirect pressure detection method is able to reliably detect changes in all driving scenarios which the regulations typically stipulate (depending on jurisdiction). In the absence of any information about the road input, the majority of the detection work must be shouldered by the wheel angular velocity comparator algorithm. As image recognition and sensor technology develops, it becomes possible to make estimates about the road surface and this removes some of the uncertainty on the input of the model-based parameter estimation approaches. Further work is detailed which goes some way towards realising the next steps in a development cycle suitable for a vehicle manufacturer to take through to the implementation stage.

The main topic of this licentiate thesis is fast faultdetection. The thesis summaries the work performed in theproject“Fast fault detection for distributionsystems”. In the first chapters of the thesis the term“fast”is used in a general manner. The term is laterdefined based upon considerations and conclusions made in thefirst chapters and then related to a specific time. To be able to understand and appreciate why fast faultdetection is necessary, power system faults and theirconsequences are briefly discussed. The consequences of a faultare dependent of a number of different factors, one of thefactors being the duration of the fault. The importance of the speed of the fault detection dependson the type of equipment used to clear the fault. A circuitbreaker which interrupt currents only when they pass through anatural zero crossing might be less dependent on the speed ofthe fault detection than a fault current limiter which limitsthe fault current before it has reached its first prospectivecurrent peak. In order to be able to detect a fault in a power system, thepower system must be observed, i.e., measurements of relevantquantities must be performed so that the fault detectionequipment can obtain information of the state of the system.The fault detection equipment and some general methods of faultdetection are briefly described. Some algorithms and their possible adaptation to fast faultdetection are described. A common principle of many algorithmsare that they assume that either a signal or the power systemobject can be described by a model. Sampled data values arethen fitted to the model so that an estimate of relevantparameters needed for fault detection is obtained. An algorithmwhich do not fit samples to a model but use instantaneouscurrent values for fault detection is also described andevaluated. Since the exact state of a power system never is known dueto variations in power production and load, a model of thepower system or of the signal can never be perfect, i.e., theestimated parameter can never be truly correct. Furthermore,errors from the data acquisition system contribute to the totalerror of the estimated parameter. Two case studies are used to study the performance of the(modified) algorithms. For those studies it has been shown thatthe algorithms can detect a fault within approximately 1msafter fault inception and that one of the algorithms candiscriminate between a fault and two types of common powersystem transients (capacitor and transformer energization). The second case study introduced a system with two sourceswhich required a directional algorithm to discriminate betweenfaults inside or outside the protection zone. It is concluded that under certain assumptions it ispossible to detect power system faults within approximately 1msand that it is possible to discriminate a power system faultfrom power system transient that regularly occurs within powersystems but which not are faults. / NR 20140805

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This dissertation addresses fault detection and isolation (FDI) for nonlinear systems based on models using two different approaches. The first approach detects and isolates single and multiple faults, particularly when there are restrictions in measuring process variables. The FDI model-based method is based on nonlinear state estimators, in which the estimates are calculated under high filtering, and a high fidelity residuals model, obtained from the difference between measurements and estimates. In the second approach, a robust fault detection and isolation (RFDI) system, that handles both parameter estimation and parameters with uncertainties, is proposed in which complex models can be simplified with nonlinear functions so that they can be formulated as differential algebraic equations (DAE). In utilizing this framework, faults are identified by performing a statistical analysis. Finally, comparisons with existing data-driven approaches show that the proposed model-based methods are capable of distinguishing a fault from the diverse array of possible faults, a common occurrence in complex processes. / text

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