Fault Diagnosis Based on Multi-Sensor State Fusion Estimation

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Abstract:

This paper presents a online fault diagnosis method for transformers is given by combining state estimation and parameter identification. It is assumed that the discrete state model corresponding to the sensor with the highest sampling rate and the measurement equations corresponding to multirate sensors are known. It can be proven that the proposed algorithm is the optimal in the sense of linear minimum covariance. The feasibility and the effectiveness of the algorithm are shown through simulations on the estimation of the current of a simple two-coil transformer, and through the comparison with the multi-rate filter method. Computer simulations show that this method can effectively determine what kind of fault happens and which parameter is related to the fault. In addition, the parameter identification remains accurate when the fault happens.

Keywords: DATA FUSION; FAULT DIAGNOSIS; KALMAN FILTER; STATE ESTIMATION; TRANSFORMER

Document Type: Research Article

DOI: http://dx.doi.org/10.1166/sl.2011.1560

Publication date: October 1, 2011

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  • The growing interest and activity in the field of sensor technologies requires a forum for rapid dissemination of important results: Sensor Letters is that forum. Sensor Letters offers scientists, engineers and medical experts timely, peer-reviewed research on sensor science and technology of the highest quality. Sensor Letters publish original rapid communications, full papers and timely state-of-the-art reviews encompassing the fundamental and applied research on sensor science and technology in all fields of science, engineering, and medicine. Highest priority will be given to short communications reporting important new scientific and technological findings.
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