Energy Operator and Wavelet Transform Approach to Online Detection of Power Quality Disturbances

In this paper, a fast and accurate detection algorithm is proposed. It ensures the real-time detection of power quality (PQ) disturbances with neither involving a significant computational burden nor requiring a large amount of memory. The proposed algorithm relies on DWT to decompose the distorted signal into two other signals: one represents the smoothed version of the original distorted signal and the other a detailed version. From the detailed signal, the high frequency disturbances can be detected; and from the smoothed one, the low frequency disturbances can be detected using TEO. And this multiresolution decomposition approach has another advantage: the noise is detached from the smoothed signal. This makes the TEO method immune to noise. At the same time, as compared with the signal energy, the noise energy is so low that it has little influence to the detailed signal; so the DWT based detecting is accurate too. The efficiency of the proposed detection methods are demonstrated by simulation and experimental results