Power asset health modeling and evaluation

The reliability of the power assets is important for the security of the power system. In this dissertation, we focus on the condition assessment and health evaluation of the power transformers. The data used to assess the health condition of the transformer includes the statistical failure data and...

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Bibliographic Details
Main Author: Shi, Zhengyu
Other Authors: Xu Yan
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/175530
Description
Summary:The reliability of the power assets is important for the security of the power system. In this dissertation, we focus on the condition assessment and health evaluation of the power transformers. The data used to assess the health condition of the transformer includes the statistical failure data and sample testing data. The procedure of modeling and evaluation of the transformer health can be divided into three steps. First, the common failure model of transformer is proposed as two-parameter Weibull Model. The parameters are computed by least square methods. Then the Health Index is calculated with the testing sample data. The formulation and assessment standards are obtained from previous studies and standards. Several testing samples are used to illustrate the performance of the proposed Health Index. The analysis indicates that the main cause of transformer health deterioration is faults occurring on the transformer. At last, based on the common model and the Health Index. We modify the original failure probability model with the Health Index to evaluate the health condition and remaining life of specific transformers. The Health Index is compared with the common value, and the original failure probability model is modified with the comparison results. With the sample transformer, we evaluate its health condition and used the modified model to evaluate its remaining life. Finally, we discuss the future work we can do to enhance the accuracy of the proposed method and its application for other types of power assets.