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This paper presents an improved morphological gradient for transformer inrush identification. Dissociative structuring element is presented and used to perform gradient operation, which can decrease the computational burden. Besides, a new index is introduced to measure the feature of extracted morphological gradient and to distinguish inrush from fault current. Simulation studies are conducted using...
Sympathetic interaction between transformers would lead to mal-operation of transformer differential protection. Based on the fact that the waveform of internal fault current has the sinusoidal features while the sympathetic inrush has not, a weighted mathematical morphological method is proposed for the identification of sympathetic inrush. In the simulation studies, the identification results have...
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...
Power transformer is one of the most important and expensive equipment in a power system. Building systems to monitor their real-time behaviours and diagnose their faults automatically with comprehensive knowledge-base are the key issues. This paper provides a new framework for power transformer monitoring and fault diagnosis based on a multi-agent system. The Gaia methodology is applied to clarify,...
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...
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...
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