Power quality and power disturbances have become an important factor for power systems. Chaotic ferroresonance is one of the disturbances that may cause overvoltages and overcurrents; hence, it can endanger the system reliability and continuous safe operating. A power system that generates chaotic oscillations is a dynamic system, which can be modeled with a Duffing equation. This paper introduces the application of a modified extended Kalman filter for improving the detection of chaotic behavior of power system signals. A modification algorithm is used to increase the estimation performance of the former casual extended Kalman filter. The proposed method is employed to distinguish the abnormalities from a signal contaminated with chaotic ferroresonance for promoting efficiency in power system characteristics detection.

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