Entropy Measures Applied on Time-Frequency Domain for Detection and Identification of Power Quality Disturbances

Abstract

Power Quality (PQ) disturbances present noteworthy ramifications on electricity consumers. They can affect manufacturing process, causing malfunction of equipment and inducing economic losses. This paper presents new features to identify power quality disturbances such as voltage sag, swell, interruption, harmonics and combined defaults. At first, Phase Locked Loops (PLL) techniques are applied to identify the frequency and phase of harmonic components of voltage signal in real time which help to detect any kind of disturbances. Then new features based on the Rényi and Shannon entropy from time-frequency domain obtained with Short-Time Fourier Transform (STFT) are proposed to identify the PQ disturbances. The results obtained in this study show that the proposed features are able to effectively discriminate the different types of disturbances even in the presence of noise.