主动配电网电能质量信号高效压缩

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Abstract In order to reduce the communication and storage costs, the massive power quality (PQ) data obtained from the active distribution network needs to be compressed efficiently with low loss. Although the PQ signal can be efficiently compressed by the similarity of different cycle, it is sensitive to noise and difficult to be applied under high-noise environments. Aim to improve the noise immunity, a PQ signal compression method based on one class support vector machine (OCSVM) and normalized distance (ND) was proposed. Firstly, PQ signals with high noise were simulated to extract features. The feature set composed with 1/4 periodic ND and signal-noise-ratio (SNR) of PQ signals was used to train the OCSVM. The characters of OCSVM including bound on error (v) and the width of RBF Kernel function (c) were optimized to improve the disturbance detection ability. Then, the SNR of PQ signals were estimated to noise estimation method. The periodic compression of PQ signals with high SNR were carried out by using ND threshold from the adjacent 2 cycle corresponding to the 1/4 cycle signal. Otherwise, the OCSVM was adopted to determine whether a new disturbance occurs in the signals with low SNR and carry out data compression. Experiments using simulation and real PQ signals in distribution network show that the new method can effectively compress the PQ signals in different noise environments.

Key words： Power quality signal data compression normalized distance one class support vector machine

Received: 02 September 2015 Published: 18 August 2016

PACS:

TM714.3

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	Articles by authors
	Huang Nantian
	Peng Hua
	Xu Dianguo
	Cai Guowei
	Yang Shangqun

Cite this article:

Huang Nantian,Peng Hua,Xu Dianguo等. High Efficiency Power Quality Data Compression in Active Distribution Network[J]. Transactions of China Electrotechnical Society, 2015, 30(增刊2): 181-188.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2015/V30/I增刊2/181

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