This paper presents a compression algorithm for power quality (PQ) signals, which does not separate the signal into stationary and transient components. The proposed algorithm is composed of a number of different compression techniques which are combined to improve both compression ratio and total computing time. This method is based on a sequence of three phases. The first phase transforms the signal to the frequency domain using the FFT algorithm. In a second phase the signal is approximated by a polynomial approximation algorithm, which is finally compressed by a lossless compression algorithm. The proposed method is characterized by high compression ratio and low computational cost, making its implementation suitable for embedded systems.
This paper presents a compression method for power quality (PQ) signals. This method consists of polynomial approximation algorithm and a lossless compression algorithm The proposed method uses the Deflate algorithm as lossless compression algorithm. The polynomial approximation is intended to decrease the entropy of the signal, thus increasing the compression ratio of the Deflate algorithm. The proposed method is characterized by high compression ratio and low computational cost, making feasible its implementation in embedded systems.
Abstract. This paper analyses and compares different digital signal processing techniques to capture with fidelity the power lines disturbances. The benefits and shortcomings of both Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) techniques are assessed. Their implementation, application and test-cases with recorded power signals are described in the paper. We define the data compression ratio (or efficiency) and the signal reconstruction accuracy after compression as the main criteria for comparing these techniques. In this paper the trade-offs of accuracy in the disturbances representation and compression efficiency were exploited. After systematic comparisons, our experimental results have shown shortcomings of the DWT and PCA techniques not yet addressed in previous works.
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