Study On New Techniques Of Online Monitoring And Fault Diagnosis For Power Transformer

As an important means of fault diagnosis, dissolved gas-in-oil analysis (DGA) is one of the most important items in the preventive test code for electric power equipment at present. Because of a lot of problems such as complex operation, long periodic test and big artificial error, the method of off-line dissolved gas-in-oil analysis (DGA) can not monitor transformer inner insulation in real time. Therefore, there is no way to timely find out incipient fault of power transformers. And nothing consequently can stop the accident fault. Online monitoring of dissolved gas in transformer oil can conquer the shortage of traditional method and realize online detection, analysis and diagnosis. Thus operators can be given right, continuous and operational decision in time. Today, there are a lot of transformer online monitoring equipments put into service in our country. But according to the statistic, a lot of equipments in use can hardly bring the advantages into play because the accident rate of equipments is high and the accuracy rate of fault diagnosis is low. So how to strengthen reliability of transformer online monitoring devices and improve accuracy of fault diagnosis methods become the key problems that transformer online monitoring systems face.The fault diagnosis methods of power transformer are studied and analyzed with analysis of complicated relationship between fault symptoms and fault mechanisms. And a new system of monitoring dissolved gas in transformer oil by fiber optic gas sensors is designed based on spectrum absorption theory. The main content of this thesis includes several aspects as follows:Chapter 2 proposes an expert system of combination fault diagnosis with several codes methods such as Rogers three-ratio codes, Japan cooperative study group, non-code ratio method, the improved IEC three-ratio codes method, IEC-60599 method and Duval triangle method based on Borda model to overcome the limitation of one single fault diagnosis method. This system can realize collaborative diagnosis for transformer fault by integrating the six methods to improve the accuracy of transformer fault diagnosis. This system actively explores a good cooperative structure and mechanism of synthetic methods for transformer fault diagnosis. The organic combination of six methods can eliminate diagnosis preference influences of one single method on the last diagnosis and assessment result, which solves the decision fusion problem of various results. This system has more reliability than one single method and can thoroughly reflect the real fault features. Diagnosis results show this system has much higher accuracy rate than a single method with better diagnosis effect.Chapter 3 makes full use of the favorable information of transformer original fault data and introduces fuzzy theory into transformer fault diagnosis according to the influences of fuzzy factors on fault. The improved IEC three-ratio codes method is combined with Fuzzy C-Means (FCM) clustering algorithm to diagnose transformer faults. A new fault diagnosis model of combining the two methods is proposed. The instance simulation and test are performed by Matlab. Results show the problems of absolute boundary and deletion codes about codes methods are basically solved and better fault diagnosis effects are obtained by this new way.Chapter 4 introduces regression theory of support vector machines into prediction of dissolved gas in transformer oil and establishes prediction model of gas concentration based on the egression theory for transformer fault alarm and prediction. Test results show this new method can meet the requirement of project practice and is helpful to predict the operation state of power transformer.In order to improve fault prediction for power transformer, chapter 5 brings forward a new combination forecasting model of optimal weights with BP neural network, Gray theory, linear regression model and regression model of support vector machines synthesized to predict the operation state of power transformer and provides a new way of prediction. This new model can give full play to the integral superiority of the four methods and has higher precision than one single method. This new method can effectively decrease the prediction error of one single method and strengthen prediction robust. The disadvantages of unilateral information and consideration for one single method can be avoided. The examples analysis also shows higher accuracy, reliability and effectiveness by this combination forecasting model are obtained than every single method.The traditional transformer online monitoring system generally adopts oxygen carrier gas, chromatographic column. And the equipment is not simple with many shortages such as periodic calibration of chromatography column and sensor, low reliability of equipments and overelaborate detection procedure of gas composition. Therefore, chapter 6 proposes a transformer online monitoring system with fiber optic gas sensors according to differential fiber optic gas absorption principle based on Beer-Lambert's law. This system is mainly used for monitoring the four dissolved gases in transformer oil such as acetylene, methane, ethylene and carbon monoxide to evaluate and identify fault status of transformer. This system dose not need any carrier gas and chromatographic column but can realize online separation and testing of different gases. It has a lot of abilities such as high sensitivity, high reliability, fast acquisition, convenience, environmental protection and anti-electromagnetic interference without complex gas path and oil control path.Chapter 7 concludes the thesis and points out some directions for future research.