Characterization of power quality disturbances using hybrid technique of linear Kalman filter and fuzzy-expert system

This paper presents a hybrid technique for characterizing power quality (PQ) disturbances. The hybrid technique is based on Kalman filter for extracting three parameters (amplitude, slope of amplitude, harmonic indication) from the captured distorted waveform. Discrete wavelet transform (DWT) is used to help Kalman filter to give a good performance; the captured distorted waveform is passed through the DWT to determine the noise inside it and the covariance of this noise is fed together with the captured voltage waveform to the Kalman filter. The three parameters are the inputs to fuzzy-expert system that uses some rules on these inputs to characterize the PQ events in the captured waveform. This hybrid technique can classify two simultaneous PQ events such as sag and harmonic or swell and harmonic. Several simulation and experimental data are used to validate the proposed technique. The results depict that the proposed technique has the ability to accurately identify and characterize PQ disturbances.

► A hybrid technique of DWT, Kalman filter and fuzzy expert system for characterizing PQ disturbances is presented.
► DWT is used to enhance the performance of Kalman filter.
► Kalman filter is used to extract three parameters (amplitude, slope of amplitude and harmonic index) from the distorted waveform.
► Fuzzy expert is applied with 13 rules on the three parameters to characterize the disturbance in the distorted waveform.
► Several simulation and experimental data are used to validate the proposed technique.