Abstract
To reduce the stress of data transmission and storage for power quality (PQ) in smart distribution systems and help PQ analysis, a multichannel data compression based on iterative PCA (principal component analysis) algorithm is introduced. The proposed method uses PCA to reduce the redundancy of data to achieve the purpose of compressing data. In order to improve the calculating speed, an iterative method is proposed to compute the principal components of the covariance matrix. The correctness and feasibility of the proposed method are verified by field PQ data tests. Compared with discrete wavelet transform (DWT) method, the proposed method has good performance on compression ratio and reconstruction accuracy.
Highlights
As the growing demand for smart distribution systems, more and more power quality (PQ) monitors are especially needed for the power systems with distributed power sources and impulsive and sensitive loads [1] [2] [3]
To reduce the stress of data transmission and storage for power quality (PQ) in smart distribution systems and help PQ analysis, a multichannel data compression based on iterative Principal Component Analysis (PCA) algorithm is introduced
Since periodical PQ signal is compressed by Fourier transform (FT), and the signal compressed by wavelet transform (WT) is with much less low frequency components, the quantization methods have much higher compression ratio (CR) than traditional methods
Summary
As the growing demand for smart distribution systems, more and more power quality (PQ) monitors are especially needed for the power systems with distributed power sources and impulsive and sensitive loads [1] [2] [3]. A popular PQD data compression is the quantization method [11] [12]. These methods separate fundamental component and disturbance components in power signals. Since periodical PQ signal is compressed by FT, and the signal compressed by WT is with much less low frequency components, the quantization methods have much higher CR than traditional methods Another interesting PQ data compression method is developed based on singular value decomposition (SVD) [13]. This paper presents a method based on iterative principal component analysis (IPCA) algorithm for PQ data compression in smart distribution systems, which can compress multichannel data simultaneously. The PCA algorithm is introduced in Part 2; Part 3 presents the proposed method; Part 4 provides field PQ data to test and compare the proposed method with related works; Part 5 summarizes the whole work
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