Hybrid Dissolved Gas-in-Oil Analysis Methods

Dissolved gas analysis is the most widely used diagnostic test in power transformers. The aim of this paper is to introduce the dissolved gas analysis (DGA) methods able to diagnose the transformer conditions. The faults cause the transformer oil, pressboard, and other insulating materials to decompose and generate gases, some of which dissolve in the oil. The results of DGA must be accurate if faults are to be diagnosed reliably. There are different established methods used in industry for interpreting DGA results. We will compare the result of IEEE Key Gas Methods and Rogers’ Ratios. The transformer conditions are evaluated by the Key Gas Method with total combustible gas method (TCGM) and then verified by the Rogers’ Ratios. As result, the aging pattern and trend of the power transformer deterioration can be determined. The 30 sample data from IEEE with known faults and dissolved gas concentrations were used as the basis of comparison.

Fault Diagnosis of Power Transformer Based on Improved ACGAN Under Imbalanced Data

The imbalance of dissolved gas analysis(DGA)data will lead to over-fitting,weak generalization and poor recognition performance for fault diagnosis models based on deep learning.To handle this problem,a novel transformer fault diagnosis method based on improved auxiliary classifier generative adversarial network(ACGAN)under imbalanced data is proposed in this paper,which meets both the requirements of balancing DGA data and supplying accurate diagnosis results.The generator combines one-dimensional convolutional neural networks(1D-CNN)and long short-term memories(LSTM),which can deeply extract the features from DGA samples and be greatly beneficial to ACGAN’s data balancing and fault diagnosis.The discriminator adopts multilayer perceptron networks(MLP),which prevents the discriminator from losing important features of DGA data when the network is too complex and the number of layers is too large.The experimental results suggest that the presented approach can effectively improve the adverse effects of DGA data imbalance on the deep learning models,enhance fault diagnosis performance and supply desirable diagnosis accuracy up to 99.46%.Furthermore,the comparison results indicate the fault diagnosis performance of the proposed approach is superior to that of other conventional methods.Therefore,the method presented in this study has excellent and reliable fault diagnosis performance for various unbalanced datasets.In addition,the proposed approach can also solve the problems of insufficient and imbalanced fault data in other practical application fields.

Power Transformer Fault Diagnosis Using Fuzzy Reasoning Spiking Neural P Systems

This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distributed parallel computing model is powerful and suitable graphical approach model in fuzzy diagnosis knowledge. In a sense this feature is required for establishing the power transformers faults identifications and capturing knowledge implicitly during the learning stage, using linguistic variables, membership functions with “low”, “medium”, and “high” descriptions for each gas signature, and inference rule base. Membership functions are used to translate judgments into numerical expression by fuzzy numbers. The performance method is analyzed in terms for four gas ratio (IEC 60599) signature as input data of FRSN P systems. Test case results evaluate that the proposals method for power transformer fault diagnosis can significantly improve the diagnosis accuracy power transformer.

Application of Extreme Learning Machine in Fault Classification of Power Transformer

Reliability of power system is very essential for every nation to generate and transmit power without interruption. Power transformer is one of the most significant electrical apparatus and hence it must be kept in good health. Identification and classification of faults in power transformer is a major research area. Conventional method of fault classification in transformer uses gas concentrations data and interprets them using international standards. These standards are not able to classify the faults correctly under certain conditions. To overcome this limitation, several soft computing tools namely artificial neural network (ANN), Support Vector Machine (SVM) etc. are used to automate the process of classification of faults in transformers. However, there is a scope exists to improve the classification accuracy. Hence, this research work focuses to design Extreme Learning Machine (ELM) method for classifying fault very accurately using enthalpy of dissolved gas content in transformer oil as an input feature. The ELM method is tested with two databases: one based on IEC TC10 database (DB1) and the other one based on data collected from utilities in India (DB2). The application of ELM to Power Transformer fault classification based on enthalpy as input feature outperforms over the conventional classification based on gas concentration as input feature.