Power Transformer Fault Diagnosis Technology Based On Grey System Theory

1. IntroductionIt is one of the most important things in power system work that how to keep the power supply system work safely and reliably along with the persistent and rapid development of national economy. Power transformer is not only the most important and expensive equipments in power system but also one of the most accident-prone equipments. Transformer oil is usually used for insulation and emitting heat in large power transformers nowadays. Transformer oil and solid organic insulative material will deteriorate gradually under the work voltage then they will be decomposed to produce a few low molecular hydrocarbons such as firedamp (CH4), ethane (C₂H₆), ethene (C₂H₄), ethine (C₂H₂) and carbon monoxide (CO), carbon dioxide(CO₂), hydrogen(H₂) because of the electricity, heat, oxidation and part electric arc etc. These gases are almost dissolved in the transformer oil. The inner over- heat-fault or discharge fault will quicken the speed of gases producing. The component and capacity of dissolved gases can reflect the degree of insulation aging or transformer fault in certain extent, so it can be used as characteristics which can reflect power equipment abnormity. Transformer inner potential fault can be found as soon as possible then be diagnosed if it endanger the safe running of transformer via analyzing component, capacity and producing speed of dissolved gases periodically. Analyzing the component and capacity of gases dissolved in transformer oil is one of the most efficient ways of monitoring the oil-filled electrical equipment running safely. Dissolved Gas Analysis analyze the capacity of H₂,C₂H₂,C₂H₄,C₂H₆,CH4,CO and CO₂ dissolved in transformer oil based on GB/T7252-2001《Guide to the analysis and the diagnosis of gases dissolved in transformer oil》. Characteristic gases analysis, Three-rations-method etc. are the methods commend by GB/T7252-2001《Guide to the analysis and the diagnosis of gases dissolved in transformer oil, and ANN, fuzzy mathematics etc. based on DGA are used in transformer condition evaluation and fault diagnosis, but all these methods are have their own problems. In theory, different types of fault decompose different gases and different degrees of the same fault, the capacity of gases are different, too. Single gas is not able to diagnosis the type of transformer fault even gained the data of dissolved gas. It means that the principle between single gas and the causation of transformer fault is not complete. Meanwhile, the correlation between dissolved gases is not quite sure, that is it has no clear qualitative and quantitative descriptions which fault can produce certain gases. So, the transformer fault system is a grey system because it has part transcendent known information and part unknown information. The essential of transformer fault diagnosis is a whitening process of a grey system. Experts both foreign and domestic introduce grey system theory to transformer fault diagnosis technology system hoping found solutions for power transformer condition evaluation, fault diagnosis and capacities of dissolved gases based on grey system theory.2. Research ContentUncertain problems which lack data and information are studied by grey system theory. Grey system theory is a new method of system science which research field is"part transcendent known information and part unknown information", little sample, lacking information uncertain system. The known part information is recreated and developed, then are distilled valuable information, to realize the correct description and efficient monitor. Grey system theory is mainly used in technical system analysis, evaluation, modeling, forecast, decision-making, control and optimization.Power transformer fault diagnosis technology system is composed of transformer condition evaluation, transformer fault diagnosis and component of dissolved gases. It is the main point of this thesis that dealing with former 3 parts of transformer fault diagnosis technology system by grey system theory.(1) Transformer Condition Evaluation Based on Grey Target Theory Transformer condition evaluation is the precondition of transformer condition-based maintenance. Grey target theory was introduced into power transformer condition evaluation after researching grey target theory and combining with successful application in other equipments. Condition parameters that are suitable for transformer condition were chosen based on grey target theory. Transformer condition grading plan was established based on statistic data. Weighted grey target theory was brought forward to the circumstance that transformer condition parameters have different contributions to transformer condition evaluation. The compare between examples and their real conditions show the grey target arithmetic is valid and applied, and possess a new way to solve the power transformer condition evaluation problem.(2) Transformer Fault Diagnosis Based on Grey Clustering AnalysisThree localizations of grey clustering based on traditional triangle whitening weight function were found out after analyzing characteristic gases of power transformer. Grey clustering based on trapezium function and Gauss function were brought forward to solved the three localizations. In trapezium gray clustering analysis, impact factor was brought forward to optimize the parameters model of transformer fault diagnosis. A new transformer fault diagnosis parameters model was brought, and this model gave the values of trapezium two upper points. In gray clustering analysis based on Gauss function, how to chooseσwas brought. The value ofσwas confirmed primarily after comparing with triangle whitening weight function and learning of 34 groups of transformer chromatography data. The compare data with triangle grey clustering analysis shows that both arithmetics are equal or better than triangle grey clustering analysis in application of transformer fault diagnosis, and are efficient and applicable in practice.(3) Transformer Dissolved Gases Capacities Prediction Based on Grey PredictionIt has important theory signification to ensure the running of power transformer safely and dependably by predicting the capacities of transformer dissolved gases and fault. Grey prediction model GM (1, 1) can model and prediction with data no more than 4. The reason of GM (1, 1) model invalid was researched. Improved GM (1, 1,ρ) model was brought forward which introduce parameterρto improve background value conformation. It is able to heighten the model precision that modified the first step predicted value by residual prediction model. In predicted process, introducing new actual measured data and controlling the number of serial can make the model precision even higher. Equal dimension and new information grey- Markov GM (1, 1,ρ) model has a higher precision than GM (1, 1) model.3. ConclusionPower transformer condition evaluated by grey target theory and contribution theory. Power transformer fault diagnosed by improved grey clustering analysis. Component of dissolved gases predicted by equal dimension and new information Grey- Markov GM (1, 1,ρ) model. Diagnosis examples show that grey target theory, grey clustering and equal dimension and new information Grey- Markov GM (1, 1,ρ) model are effective for power transformer fault diagnosis. Grey target theory can evaluate transformer condition correctly. Grey clustering analysis can diagnose type and part of transformer fault. Meanwhile, equal dimension and new information Grey- Markov GM (1, 1,ρ) model can predicate type and degree of transformer fault based on existing chromatography dada.Methods in this thesis have important academic significance and practical value for ensuring safe and reliable running of power transformer.