Research On Compression And Analysis Of Power Quality Data

With continuous development of industrial society, the importance of power quality is becoming higher, so any power quality problems are likely to cause an immeasurable loss. Intelligent power system and high level monitoring system can solve the problem, but bring huge amount of power quality data. Storage and transmission have become power worker’s concerns. So it is very critical to analyze power quality data accurately and compress power quality data effectively.In power quality data compression,relevant scientific researcher put forward various method based on wavelet transform and verified its validity. As a result of its weakness in adaptivity and unsatisfactory performance when used in non-stationary and nonlinear data, in this paper Hilbert-Huang transorm is adopted to analyze power quality data.Based on analysis of wavelet transform and its applications in power quality data conpression, a power quality compression algorithm based on HHT and PCA is proposed in this paper in order to get better compression effect. First, decompose power quality data using EMD algorithm to get IMFs, fault signal information including start-stop time, frequency and amplitude is obtained by analyzing IMFs using HHT transform. Then, cut out original signal according to the start-end time to separate normal and fault signals. Reconstruct normal and fault singals into two-dimensional signal. At last, compress the constructed data using improved PCA algorithm. Compressed data can keep characteristics of the fault signal according to experimental results. After analysis of compression results, the proposed algorithm is effective compared with classical wavelet algorithm.