Kalman filter and a fuzzy logic processor for series arcing fault detection in a home electrical network

Abstract : This paper presents a method for detecting series arcing faults in AC home electrical networks. The proposed algorithm is based on both a Kalman filter, used for identifying fault symptoms and a decision block, which confirms the presence of a series arc fault to activate a tripping signal. The current measured at one end of the power line is estimated using a model of two steady-state variables (X1 and X2). Firstly, residuals and the third order difference of state X2 are used as input parameters of a Fuzzy logic processor for detecting fault symptoms. Secondly, the fault symptoms are processed by a detection logic block, which confirms the presence of an electrical arcing fault. The algorithm is tested on a variety of loads in single or masking load configurations chosen accordingly to the requirements of the UL 1699 and IEC 62606 standards. The algorithm is also tested in the steady state or at load start (transient state). This method's performance is studied and discussed in the final part. Experimental results show that the method we propose can detect arcing faults efficiently, avoiding false tripping, whilst taking into account a high degree of diagnosis accuracy and average detection time.
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https://hal.univ-lorraine.fr/hal-02065585
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Submitted on : Tuesday, March 12, 2019 - 5:56:41 PM
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Edwin Calderon-Mendoza, Patrick Schweitzer, S. Weber. Kalman filter and a fuzzy logic processor for series arcing fault detection in a home electrical network. International Journal of Electrical Power and Energy Systems, Elsevier, 2019, 107, pp.251-263. ⟨10.1016/j.ijepes.2018.11.002⟩. ⟨hal-02065585⟩

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