Intelligent engineering asset management system for power transformer maintenance decision supports under various operating conditions

AJC Trappey, CV Trappey, L Ma, JCM Chang - Computers & Industrial …, 2015 - Elsevier
AJC Trappey, CV Trappey, L Ma, JCM Chang
Computers & Industrial Engineering, 2015Elsevier
Large sized transformers are an important part of global power systems and industrial
infrastructures. An unexpected failure of a power transformer can cause severe production
damage and significant loss throughput the power grid. In order to prevent power facilities
from malfunctions and breakdowns, the development of real-time monitoring and fault
prediction tools are of great interests to both researches and practitioners. This research
develops an intelligent engineering asset management system for power transformer …
Abstract
Large sized transformers are an important part of global power systems and industrial infrastructures. An unexpected failure of a power transformer can cause severe production damage and significant loss throughput the power grid. In order to prevent power facilities from malfunctions and breakdowns, the development of real-time monitoring and fault prediction tools are of great interests to both researches and practitioners. This research develops an intelligent engineering asset management system for power transformer maintenance. The system performs real-time monitoring of key parameters and uses data mining and fault prediction models to detect transformers’ potential failure under various operating conditions. Principal component analysis (PCA) and a back-propagation artificial neural network (BP-ANN) are the algorithms adopted for the prediction model. Historical industrial power transformer data from Taiwan and Australia are used to train and test the failure prediction models and to verify the proposed general methodology as comparative case studies. The PCA algorithm reduces the number of the primary dissolved gasses as the key factor values for BP-ANN prediction modeling inputs. The system yields effective predictions when verified using various operating condition data from Australia and Taiwan power companies. The accuracy rates are much higher when compared to the fault prediction results without using PCA. The intelligent system combining PCA and BP-ANN algorithms, developed in this research, can be adopted by asset managers in different regions to develop suitable maintenance and repair strategies for transformer failure preventions.
Elsevier
以上显示的是最相近的搜索结果。 查看全部搜索结果

Google学术搜索按钮

example.edu/paper.pdf
搜索
获取 PDF 文件
引用
References