Data driven approach for fault detection and diagnosis of turbine in thermal power plant using Independent Component Analysis (ICA) |
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| Affiliation: | 1. Electrical Engineering Department of Azarbaijan Shahid Madani University, Tabriz, Iran;2. MAPNA Electric & Control, Engineering & Manufacturing Co. (MECO), Tehran, Iran;1. Department of Automation, College of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China;2. College of Computer Science, South-Central University for Nationalities, Wuhan, Hubei 430074, China;1. National Energy Technology Laboratory, 3610 Collins Ferry Road, Morgantown, WV 26507, USA;2. Leidos Research Support Team, 3610 Collins Ferry Road, Morgantown, WV 26507, USA;3. ABB, Inc, 23000 Harvard Rd, Cleveland, OH 44122, USA;1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, Shandong Province, China;2. Shandong Electric Power Research Institute for State Grid Corporation of China, Jinan, Shandong Province, China |
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| Abstract: | In this paper, a statistical signal processing technique, known as Independent Component Analysis (ICA) for fault detection and diagnosis in the real Turbine system (V94.2 model) is suggested. The information of one of MAPNA’s power plants turbine system is utilized at first. In order to reduce the dimensionality of the data set, to identify the essential variables and to choose the most useful variables, PCA approach is applied. Then, the fault sources are diagnosed by ICA technique. The results indicate that suggested approach can distinguish main factors of abnormality, among many diverse parts of a typical turbine system. The presented results will show that suggested approach can avoid false alarms and fault misdiagnosis due to changes in operation conditions and model uncertainty. The presented results show the validity and effectiveness of ICA approach for faults detection and diagnosis in noisy states. |
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