Multi-agent and knowledge-based system for power transformer fault diagnosis

摘要

Transformer reliability and stability are the key concerns. In order to increase their efficiency, an automatic monitoring and fault diagnosing of the power transformers are required. Dissolved Gas Analysis (DGA) is one of the most important tools to diagnose the condition of oil-immersed transformer. Agents technology as a new, robust and helpful technique, successfully applied for various applications. Integration of the Multi-Agent System (MAS) with knowledge base provides a robust system for various applications, such as fault diagnosis and automated actions performing, etc. For this purpose, the present study was conducted in the field of MAS based on Gaia methodology and knowledge base. The developed MAS followed by Gaia methodology represents a generic framework that is capable to manage agents executions and message delivery. Real-time data is sampled from a power transformer and saved into a database, and it is also available to the user on request. Three types of knowledge-based systems, namely the rule-based reasoning, ontology and fuzzy ontology, were applied for the MAS. Therefore, the developed MAS is shown to be successfully applied for condition monitoring of power transformer using the real-time data. The Roger’s method was used with all of the knowledge-based systems named above, and the accuracy of the results was compared and discussed. Of the knowledge-based systems studied, fuzzy ontology is found to be the best performing one in terms of results accuracy, compared to the rule-based reasoning and ontology. The application of the developed fuzzy ontology allowed to improve the accuracy by over 22%. Unlike the previous works in this field, that were not capable of dealing with the uncertainty situations, the present work based on fuzzy ontology has a clear advantage of successfully solving the problem with some degree of uncertainty. This is especially important, as the most of the real-world situations involve some uncertainty. Overall, the work contributes the use of the knowledge base and the multi-agent system for the fault diagnosis of the power transformer, including the novel application of fuzzy ontology for dealing with the uncertain situations. The advantages of the proposed method are the ease of the upgrade, flexibility, efficient fault diagnosis and reliability. The application of the proposed technique would benefit the power system reliability, as it would result in reduction of the number of engineering experts required, lower maintenance expenses and extended lifetime of power transformer.