Browsing by Author "Chang, Jimmy C. M."

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Integrating Real-Time Monitoring and Asset Health Prediction for Power Transformer Intelligent Maintenance and Decision Support
(2015-01-01)
Trappey, Amy J. C.
;
Trappey, Charles V.
;
Ma, Lin
;
Chang, Jimmy C. M.
;
管理科學系
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 health prediction tools are of great interests to both researchers and practitioners. An advanced monitoring tool performs real-time monitoring of key parameters to detect signals of potential failure through data mining techniques and prediction models. Asset managers use the result to develop a suitable maintenance and repair strategy for failure prevention. Principal component analysis (PCA) and back-propagation artificial neural network (BP-ANN) are the algorithms adopted in the research. This chapter utilizes industrial power transformers\' historical data from Taiwan and Australia to train and test the failure prediction models and to verify the proposed methodology. First, PCA detects the conditions of transformers by identifying the state of dissolved gasses. Then, the BP-ANN health prediction model is trained using the key factor values. The integrated engineering asset management database includes nine gases in oil as input factors (N-2, O-2, CO2, CO, H-2, CH4, C2H4, C2H6, and C2H2). After applying the principal components algorithm, the research identifies five factors from the Taiwan operational transformer data and six factors from the Australia data. The integrated PCA and BP-ANN fault diagnosis system yields effective and accurate predictions when tested using Taiwan and Australia data. The accuracy rates are much higher (i.e., 92 and 96 % respectively) when compared to previous result of 69 and 75 %. This research is benchmarked against the DGA heuristic approaches including IEEE\'s Doernenburg and Rogers and IEC\'s Duval Triangle for the experimental fault diagnoses.
Intelligent engineering asset management system for power transformer maintenance decision supports under various operating conditions
(2015-06-01)
Trappey, Amy J. C.
;
Trappey, Charles V.
;
Ma, Lin
;
Chang, Jimmy C. M.
;
管理科學系
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 nianagement 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., (C) 2015 Published by Elsevier Ltd.