The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Transformer electromagnetic design closely related to leakage magnetic field[1][2]. Increasing with the capacity of transformer, the rise of temperature will affect the normal operation of the transformer. This paper will use the existing commercial software and instrument to leakage magnetic field simulation, calculation of temperature rise and the corresponding experiment. In order to better optimize...
This paper proposes an application framework to reinforce the existing process for ontology-based transformer fault diagnosis with formal probabilistic semantics using the Bayesian Network. This framework allows users to quantify a certain fault with Bayesian Network, based on the knowledge embedded in a transformer ontology regarding relationships of faults and their features such as causes, symptoms...
This paper proposes an approach using weighted mathematical morphology (WMM) to effectively identify inrush current. The identification is based on the feature that the waveform of inrush current is quite different from sinusoid whereas internal fault current is nearly sinusoidal. Compared with the traditional method based on the second harmonic, the proposed approach reduces the data window from...
Dissolved gas analysis (DGA) has proved to be one of the most useful techniques to detect the incipient faults of power transformers. This paper presents a novel method named multi-kernel support vector classifier (MKSVC), to analyze the DGA for fault diagnosis of transformers. Different from the conventional support vector machine (SVM), MKSVC uses a combined kernel formed through a linear combination...
Discriminating between the magnetizing inrush and the internal fault of a power transform is a major challenge when designing differential transformer protection. In this paper, a novel method, which is based on mathematical morphology (MM) and artificial neural network (ANN), is proposed to solve this problem. Firstly, an MM based stage is used to extract shape features from differential currents,...
Accurate and efficient fault diagnosis is vital to ensure the normal operation for power transformers. In this paper, a new approach to transformer fault diagnosis is introduced based on the idea of exchanging information with explicit, formal and machine accessible descriptions of meaning by using the Semantic Web. An ontology model is developed for accurate and efficient fault diagnosis for power...
This paper presents a simplified distributed parameter model for minor winding deformation fault analysis of power transformers on the basis of frequency response analysis (FRA). The FRA data of an experimental transformer is employed as a reference trace, which are compared with the simulations of the simplified distributed parameter model concerning minor winding deformation faults. In order to...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.