This paper proposes a new algorithm to detect and classify faults in electric power transmission line. The method is developed based on advanced digital signal processing (DSP) and time-frequency distribution (TFD) technique. Since it shows suitable properties to extract time-varying signature of non-stationary signals and high-frequency transients introduced by typical power quality (PQ) disturbances in electric power systems. The proposed method first separates fault disturbance component from the steady-state signal, and represents it in time-frequency domain. Thereby for feature extraction to single out faults from other common electric PQ disturbances such as voltage sags and oscillatory transients. Once fault is detected, TFD-based new index Instantaneous Fault Disturbance Ratio (IFDR), which provides energy information of fault disturbance compared to steady-state signal, is utilized to classify different types of faults. The analysis results show that the proposed method is able to classify faults successfully by setting up thresholds obtained via IFDR index for different types of faults. In this work, different types of fault signals are generated using PSCAD/EMTDC simulation software. Further, fault signal data are imported into MATLAB for post processing, and time-frequency analysis using Signal Processing Toolbox in MATLAB.
|Konference||45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019|
|Periode||14/10/2019 → 17/10/2019|
|Sponsor||IEEE, IEEE Industrial Electronics Society (IES)|
|Navn||Proceedings of the Annual Conference of the IEEE Industrial Electronics Society|