Research On Fault Prediction And Diagnosis Of Power Transformer Based On Dissolved Gas Analysis

In modern electric power system,the significance of power transformers is obvious.As a transfer station and hub for power transmission,transformers affect the electricity output and power quality directly.Continuously improving the level of research on fault prediction,which will help find out defects and potential failure of power transformers in time,work out maintenance plans and develop protective measures.Then,it will ensure the security and stable operation of power systems.At this stage,both domestic and foreign experts and scholars are carrying out researches on fault prediction and diagnosis of power transformers actively.However,there is still a lot of room for improvement.Therefore,it is still very important and valuable to conduct this kind of studies.In this paper,the research about fault prediction and diagnosis is based on the index of dissolved gas content in transformer oil.As it is free from external electromagnetic interference,the content of dissolved gas will help determine the fault types,and which is an important method to identify transformer defects and latent faults.The research contents are as follows:Firstly,a combined forecasting model based on DGA(dissolved gas analysis)in transformer oil is founded.In this paper,the author obtained the data of the dissolved gas content in transformer oil at first,and then used three single prediction models respectively,which were support vector machines,BP(Back Propagation)neural network and gray model,to get the prediction results based on seven kinds of characteristic gases.By using the improved particle swarm algorithm to determine the weight of the three single models,and with the obtained optimal weights,a combined prediction result will be formed.Through analyzing the final results,it proves that the effect of combined forecasting prediction is obviously better than any single prediction method.The combined prediction model in this paper will definitely reflect the change trend of the characteristic gas better.Secondly,a study with examples on transformer fault diagnosis is conducted in this thesis.Based on the predicted data of dissolved gas,we reconstruct a new set of fault features,distill new characteristic quantities by analysis on principal component,and then optimize the parameters selection of the kernel function.Therefore,a transformer fault diagnosis model based on main component analysis and parameter optimization of SVM(support vector machine)is established.By comparison and analysis of examples,it will verify the accuracy,practicability and effectiveness of this model.Finally,we explore the engineering application on fault prediction and diagnosis of power transformer.Combining the combined model and the transformer fault diagnosis model,a new transformer fault prediction and diagnosis model is formed.Based on the foregoing theories,a transformer fault prediction and diagnosis software for practical projects is constructed.