Fuzzy Model for Power Transformer Condition Monitoring and Fault Detection

Abstract

Power transformers are equipment of great importance, and their availability is crucial for the security and continuity of the electricity supply for domestic and industrial users. During their life cycle, transformers are exposed to various environmental and operational conditions that affect their performance, especially when these exceed the operational design limits. This paper describes the use of Fuzzy Logic models as supporting tools for the automatic classification of power transformer operating conditions. The proposed methodology involves a binary classification (failure or no failure), followed by a multi-classification into seven types of failures. For this purpose, a power transformer fault database was developed, compiling information from operational data curated by power transformer experts. The results show a high predictive capacity for transformer fault conditions, with 96% balanced accuracy, and acceptable effectiveness in detecting different faults. This approach may serve as useful guidance in power transformer condition monitoring, helping engineers to reduce the time required to detect and repair incipient faults.