A Novel Extension Method for Transformer Fault Diagnosis
نویسندگان
چکیده
منابع مشابه
A Novel Feature Selection Method for Fault Diagnosis
A new method for automated feature selection is introduced. The application domain of this technique is fault diagnosis, where robust features are needed for modeling the wear level and therefore diagnosing it accurately. A robust feature in this field is one that exhibits a strong correlation with the wear level. The proposed method aims at selecting such robust features, while at the same tim...
متن کاملTransformer fault diagnosis using continuous sparse autoencoder.
This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer fault recognition. Firstly, based on dissolved gas analysis method, IEC three ratios are calculated by the con...
متن کاملA Novel Intelligent Fault Diagnosis Approach for Critical Rotating Machinery in the Time-frequency Domain
The rotating machinery is a common class of machinery in the industry. The root cause of faults in the rotating machinery is often faulty rolling element bearings. This paper presents a novel technique using artificial neural network learning for automated diagnosis of localized faults in rolling element bearings. The inputs of this technique are a number of features (harmmean and median), whic...
متن کاملStudy on Transformer Fault Diagnosis Based on Dynamic Fault Tree
In this paper, according to theoretical diagnosis of fault tree, the author builds a diagnosis model based on dynamic fault tree and illustrates the model’s construction method and diagnosis logic in detail. According to case analysis, compared with conventional fault tree diagnosis, the above-mentioned method is advanced in fault-tolerant ability. Plus, the diagnosis results record some interm...
متن کاملNovel Fault Diagnosis Method for Wind Power System
This paper proposes a novel approach based on the chaos eye method (CEM) and extension neural network (ENN) for fault diagnosis of wind power systems. First, we used sensors to capture the vibration signals of the wind power system to detect subtle changes. Subsequently, the chaotic synchronization detection method was used to form a chaos error distribution diagram. The distribution diagram ce...
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: IEEE Power Engineering Review
سال: 2002
ISSN: 0272-1724
DOI: 10.1109/mper.2002.4312484