Abstract：A fault in any one string of insulators may cause a power network accident, so insulator online monitoring is of vital importance. Sampling frequency of leakage currents must be high enough to find abnormalities in insulators, yielding large amount of data, hence the data compression of leakage currents is of great significance. An adaptive version of SPIHT is proposed, which can partition sets of wavelet coefficients according to their significance, thus effectively reduces the number of judgments on whether the coefficients in one set are all 0, making itself well suited for coding signals with high-level noise such as leakage currents. In view of the high noise level and periodic redundancy of the leakage currents, adaptive SPIHT and DPCM are utilized for the coding of wavelet detail and approximation coefficients, respectively. The compression efficiency can be further improved if context-based adaptive binary arithmetic coding is employed. The proposed algorithm is evaluated on testing data of insulator leakage currents, showing compression performance significantly better than SPIHT. Compared with existing two-dimensional algorithms for data compression in power systems, it can do compression after data sampling within just one AC power working cycle, so it is more suitable for the real time or online situations.
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2011, Vol. 26 
