Application Of Wavelet Analysis In Power Quality Disturbance Detection And Analysis

With the wide application of large-scale sensitive electronic devices in the power system, power quality disturbances phenomenon have already become the focus that numerous fields have paid close attention to. The power quality disturbances are detected and analyzed accurately, as to realize the characteristic extraction and identification,which is the essential presupposition of monitoring and managing power quality disturbances phenomenon. Generally speaking, the sampling frequency of the monitor may be very high in order to analyze the transient phenomena,which will bring a large data storage and communication cost. Simultaneously,sampling data are usually overlapped by noise which will impact the analysis results. Some research is processed here on the problems above as follow:The definition, cause, classification, and evaluation criterion of power quality problems are discussed, and five common power quality transients are described detailedly.Three methods, including Fourier transform, short-time Fourier transform and Wavelet transform are discussed. This paper also explains the theories and how to use the theories in power quality analysis, and indicates their advantages and disadvantages.According to the non-steady characteristic of power quality disturbances, the good time-frequency localization makes the singularity of signal on non-zero points can be signified by the wavelet transform modulus maximum. Using Mallat algorithm, the wavelet transform modulus maximum of singularity is extracted through multiresolution decomposition, by which the accurate detection of fault signal can be realized. A method based on the difference of low frequency coefficient modules of the decomposed signal detects disturbance on zero-cross points.Proposed a new threshold function based on wavelet transform by utilizing the different characters of evolution of the wavelet transform modulus maximum across scale of efficient signal and noise. The de-noising and data compression results of different threshold and different threshold function are analyzed to verify the feasibility and effectiveness of the new threshold function. The information of singularity points can be reserved well and the de-noised and compacted signal is a good estimation of the original signal.