Frequency and time fault diagnosis methods of power transformers
نویسندگان
چکیده
منابع مشابه
Multiple SVMs Modelling Method for Fault Diagnosis of Power Transformers
For enhancing the accuracy of fault diagnosis for power transformers, a multiple SVMs scheme is proposed in this paper. In this scheme, SVM is used to establish the base classifier for its good performance and fast learning speed. Secondly, the several base classifiers based on single SVM will be combined by consulting ensemble techniques. And then a multiple SVM s method is obtained. The real ...
متن کاملA Dynamic Integrated Fault Diagnosis Method for Power Transformers
In order to diagnose transformer fault efficiently and accurately, a dynamic integrated fault diagnosis method based on Bayesian network is proposed in this paper. First, an integrated fault diagnosis model is established based on the causal relationship among abnormal working conditions, failure modes, and failure symptoms of transformers, aimed at obtaining the most possible failure mode. And...
متن کاملMonitoring and Diagnosis Methods for High Voltage Power Transformers
Transformatoarele de putere de înaltă tensiune reprezintă unele din cele mai costisitoare echipamente din staţiile electrice din sistemul electroenergetic. Defectele şi opririle accidentale ale acestora nu cauzează numai costuri cu reparaţiile; ele conduc şi la pierderi ecomonice datorate întreruperii alimentării consumatorilor. Testele preventive şi monitorizarea on-line sunt benefice pentru p...
متن کاملImproved Svm and Ann in Incipient Fault Diagnosis of Power Transformers Using Clonal Selection Algorithms
Based on statistical learning theory (SLT), the support vector machine (SVM) is well recognized as a powerful computational tool for problems with nonlinearity having high dimensionalities. Solving the problem of feature and kernel parameter selection is a difficult task in machine learning and of high practical relevance in blurred fault diagnosis. We explored the feasibility of applying an ar...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Measurement Science Review
سال: 2018
ISSN: 1335-8871
DOI: 10.1515/msr-2018-0023