Library Automation and Digital Archive
LONTAR
Fakultas Ilmu Komputer
Universitas Indonesia

Pencarian Sederhana

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Call Number SEM-368
Collection Type Indeks Artikel prosiding/Sem
Title Research on Condition Assesment Method of Intelligent Power Transformer.
Author Feng-Jiao Wu, Guan-Jun Zhang, Shi-Qiang Wang, Hao Xu, Da Wang, Min Lei;
Publisher Peoceeding of the 2011 Interntaional Conference on Electrical Engineering and Informatics
Subject Condition assesment, unascertained raditional number, parameter weight, membership function.
Location
Lokasi : Perpustakaan Fakultas Ilmu Komputer
Nomor Panggil ID Koleksi Status
SEM-368 TERSEDIA
Tidak ada review pada koleksi ini: 47425
Abstract- With the development of sensor and measurement technology, more and more parameters are monitored to asses power transformer condition. Different parameters may reflect different aspects of transformers insulation and the same parameter diagnosed by different methods may get different results. Facing such a complex transformer system and so much online and offline data, the most important things is to find an effective method to fuse all important information to evaluate transformer condition. In this paper, a comprehensive assessment model based on hierarchical fuzzy theory is proposed in evaluate transformer condition. In the model, a transformer is divided into different components based on its structure such as winding, capacitance bushing, iron core, insulation oil, cooler tap-changer and relay devices, and these parts usually meet some faults during operation. The transformers condition are evaluated by related monitoring parameters . While employing the model to asses the condition of a transformers, the key is to determine the weighs and membership functions of different parameters. An unascertained rational number method is introduced to determine the different weighs and some effective methods are proposed to determine the membership functions according to experts' experience and guidelines. Finally, an example is give to verify the effectiveness of the models.