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Fault Diagnosis Based on Multi-Sensor State Fusion Estimation

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

Document Type: Research Article

Publication date: 2011-10-01

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