The Data Compression And De-nosing Of Dynamic Power Quality Disturbance

The monitor of power quality has gone through the traditional stage of assessing statistic indicator of power quality comparing national standards to a new stage, electric utility industry and customers want to record and analyze dynamic power quality disturbance. The traditional power quality monitors can't record transient wave of power quality, foreign monitor equipments with high precision can't monitor power quality continually, so this paper proposes a new scheme for data compression and de-noising technique, using this algorithm not only continually monitor dynamic power quality events without increasing any additional hardware investment, but also decreasing communication cost remarkably, so the objective of real-time monitor for power quality network is achieved. This algorithm can supply power quality management center with visual voltage and current waves, at the same time, the managers can use these reconstructed signals to detection, localization, quantification, classification and events cause searching.Generally, the frequency range of DPQD signals is very band, so the sampling frequency may be very high for the accurate analysis, which will bring a large data storage and communication cost, so it's necessary to process data compression; simultaneously, the sampling data is usually overlapped by additive system and measure white Gaussian noise, so noise suppressing is also obligatory. There are many sorts of DPQD signals, and their noise levels are different, so this paper proposes a data-adaptive algorithm for data compression and de-noising technique.The best transform to compress a particular signal may not be good for anothersignal, therefore, instead of restricting our attention to a particular basis, we consider a library of bases. A self-contained model library is made up of wavelet bases and local cosine bases. This collection of bases is highly adaptable and versatile for representing various DPQD signals.This algorithm has solved the time translation-invariant problem of DWT and LCT by introducing such techniques as Beylkin algorithm and divide-conquer algorithm and so on. In this algorithm, dynamic programming is used to calculate optimal local cosine tree during the LCT for signals, and the optimal entropy code is gained by the best partition of time axes.The information theory-based minimum description length criterion is used to estimate signal model. This algorithm has data-adapted capability for different noise levels and different sorts of signals. It can not only select the "best" number of retained decomposing coefficients without any parameter setting (such as thresholding) or subjective judgments, but also choose the "best" transform model according to signals dynamically. Computer results show that the algorithm can satisfy electric utility industry's desire, simultaneously, illustrations prove that MDL criterion gives the best compromise between the fidelity of signal and the efficiency of signal compression.This paper design an assistant criterion (compression ration and error of signal reconstruction balancing strategy) for signal model selection, this criterion with MDL aims to gain the compromise between the two opposite object function of CR and MSE (mean square error). This criterion selects the best t signal model and the best number of coefficients for the signal reconstruction.Compared with four prevalent data compression and de-noising algorithms, analysis results show that integrated performance indicators of this algorithm is excellent. Time and frequency characteristics of DPQD are reserved perfectly and its result is satisfying.