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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...
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,...
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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