Abstract
Dissolved gas analysis (DGA) has been widely used in various scenarios of power transformers’ online monitoring and diagnoses. However, the diagnostic accuracy of traditional DGA methods still leaves much room for improvement. In this context, numerous new DGA diagnostic models that combine artificial intelligence with traditional methods have emerged. In this paper, a new DGA artificial intelligent diagnostic system is proposed. There are two modules that make up the diagnosis system. The two modules are the optimal feature combination (OFC) selection module based on 3-stage GA–SA–SVM and the ABC–SVM fault diagnosis module. The diagnosis system has been completely realized and embodied in its outstanding performances in diagnostic accuracy, reliability, and efficiency. Comparing the result with other artificial intelligence diagnostic methods, the new diagnostic system proposed in this paper performed superiorly.
Highlights
IntroductionTransformers are distributed in almost all domains of the entire electrical network, changing the values of AC voltage (current) at given points to another or several values without altering the frequency
We argue that a sound transformer diagnostic method should be strengthened in the following aspects: (1) Economic efficiency and (2) solving the allowable-time problem
This paper aims to propose an online-diagnosis method that is economical and capable of overcoming the allowable-time problem
Summary
Transformers are distributed in almost all domains of the entire electrical network, changing the values of AC voltage (current) at given points to another or several values without altering the frequency. They guarantee the normal operation of the power grid, and affect people’s living environment [1]. The operating conditions of the transformer (including temperature and electromagnetic conditions) are harsh and not conducive to its long-term health [2,3,4]. The failure of power transformers is often attended by disastrous consequences, which include equipment burning and large-scale blackouts. The operational safety of power transformers deserves serious concern
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