主动配电网电能质量信号高效压缩

摘要
图/表
参考文献
相关文章 (6)

全文: PDF (20119 KB)
输出: BibTeX | EndNote (RIS)

摘要主动配电网获得的海量电能质量数据需要进行高效、低损压缩,以降低通信与存储压力。电能质量信号可通过周期间相似性测度进行高效压缩,但易受噪声干扰。为提高测度压缩方法在配电网内抗噪能力,提出基于单类支持向量机（OCSVM）与归一化距离测度的电能质量信号压缩方法。首先,通过仿真,获取高噪声环境下故障样本,训练以1/4周期信号归一化距离测度和信号信噪比为输入的OCSVM,并改进OCSVM的误差限v和径向基函数（RBF）核函数宽度参数c,以提高扰动检测能力;之后通过噪声估计方法,估计待压缩的信号信噪比。如信号信噪比较高,则采用相邻两周期内对应1/4周期信号归一化距离测度阈值,进行周期化压缩。否则,采用OCSVM,判定低信噪比信号内是否新发生扰动并开展压缩。仿真与实测配电网电能质量信号证明,新方法能够在不同噪声环境下,有效地压缩电能质量信号。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	黄南天
	彭华
	徐殿国
	蔡国伟
	杨尚群

关键词 ：电能质量信号, 数据压缩, 归一化距离, 单类支持向量机

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

收稿日期: 2015-09-02 出版日期: 2016-08-18

PACS:

TM714.3

基金资助:国家自然科学基金（51307020）和吉林省科技发展计划（20150520114JH）资助项目

作者简介: 黄南天男,1980年生,副教授,硕士生导师,研究方向为电能质量分析与控制、可再生能源预测与规划等。E-mail: huangnantian@126.com（通信作者）;彭华男,1989年生,硕士研究生,研究方向为电能质量信号分析与压缩。E-mail: 479767809@qq.com

引用本文:

黄南天, 彭华, 徐殿国, 蔡国伟, 杨尚群. 主动配电网电能质量信号高效压缩[J]. 电工技术学报, 2015, 30(增刊2): 181-188. Huang Nantian, Peng Hua, Xu Dianguo, Cai Guowei, Yang Shangqun. High Efficiency Power Quality Data Compression in Active Distribution Network. Transactions of China Electrotechnical Society, 2015, 30(增刊2): 181-188.

链接本文:

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

[1] 范明天, 张祖平, 苏傲雪, 等. 主动配电系统可行技术的研究[J]. 中国电机工程学报, 2013, 33(22): 12-18.
Fan Mingtian, Zhang Zuping, Su Aoxue, et al. Enabling technologies for active distribution systems[J]. Proceedings of the CSEE, 2013, 33(22): 12-18.
[2] 沈鑫, 曹敏. 分布式电源并网对于配电网的影响研究[J]. 电工技术学报, 2015, 30(增1): 346-351.
Shen Xin, Cao Min. Research on the influence of distributed power grid for distribution network[J]. Transactions of China Electrotechnical Society, 2015, 30(S1): 346-351.
[3] 崔红芬, 汪春, 叶季蕾, 等. 多接入点分布式光伏发电系统与配电网交互影响研究[J]. 电力系统保护与控制, 2015, 43(10): 91-97.
Cui Hongfen, Wang Chun, Ye Jilei, et al. Research of interaction of distributed PV system with multiple access points and distribution network[J]. Power System Protection and Control, 2015, 43(10): 91-97.
[4] 马钊, 梁惠施, 苏剑. 主动配电系统规划和运行中的重要问题[J]. 电网技术, 2015, 39(6): 1499-1503.
Ma Zhao, Liang Huishi, Su Jian. Important issues in planning and operation of active distribution system[J]. Power System Technology,2015, 39(6): 1499-1503.
[5] 刘东, 陈云辉, 黄玉辉, 等. 主动配电网的分层能量管理与协调控制[J]. 中国电机工程学报, 2014, 34(31): 5500-5506.
Liu Dong, Chen Yunhui, Huang Yuhui, et al. Hierarchical energy management and coordination control of active distribution network[J]. Proceedings of the CSEE, 2014, 34(31): 5500-5506.
[6] 张逸, 林焱, 吴丹岳. 电能质量监测系统研究现状及发展趋势[J]. 电力系统保护与控制, 2015, 43(2): 138-147.
Zhang Yi, Lin Yan, Wu Danyue. Current status and development trend of power quality monitoring system[J]. Power System Protection and Control, 2015, 43(2): 138-147.
[7] 尤毅, 刘东, 钟清, 等. 主动配电网储能系统的多目标优化配置[J]. 电力系统自动化, 2014, 38(18): 46-52.
You Yi, Liu Dong, Zhong Qing, et al. Multi-objective optimal placement of energy storage systems in an active distribution network[J]. Automation of Electric Power Systems, 2014, 38(18): 46-52.
[8] Ray P K, Mohanty S R, Kishor N, et al. Optimal feature and decision tree-based classification of power quality disturbances in distributed generation systems[J]. IEEE Transactions on Sustainable Energy, 2014, 5(1): 200-208.
[9] 黄纯, 杨帅雄, 梁勇超, 等. 电力系统故障录波数据实用压缩方法[J]. 电力自动化设备, 2014, 34(6): 162-167.
Huang Chun, Yang Shuaixiong, Liang Yongchao, et al. Practical data compression method for power system fault records[J]. Electric Power Automation Equipment, 2014, 34(6): 162-167.
[10] 鲁晓帆, 刘志刚, 吴峰. 多核系统的小波包并行算法及其在电力系统数据压缩中的应用[J]. 电力自动化设备, 2013, 33(5): 130-135.
Lu Xiaofan, Liu Zhigang, Wu Feng. Parallel wavelet packet algorithm based on multi-core system and its application in power data compression[J]. Electric Power Automation Equipment, 2013,33(5): 130-135.
[11] 郑伟彦, 吴为麟. 基于二维小波包和 v -SVR的电能质量数据压缩[J]. 电力系统保护与控制, 2010, 38(23): 68-74.
Zheng Weiyan, Wu Weilin. A power quality data compression algorithm based on 2-d wavelet packet and v -support vector regression[J]. Power System Protection and Control, 2010, 38(23): 68-74.
[12] 桂勋. 面向IEEE COMTRADE格式的海量录波数据并行压缩/解压算法[J]. 电力自动化设备, 2013, 33(5): 136-143.
Gui Xun. Parallel compression and decompression algorithm for massive recording data in IEEE COMTRADE format[J]. Electric Power Automation Equipment, 2013, 33(5): 136-143.
[13] 黄南天, 徐殿国, 刘晓胜, 等. 基于模式相似性测度的电能质量数据压缩方法[J]. 电工技术学报, 2011, 26(10): 39-46, 61.
Huang Nantian, Xu Dianguo, Liu Xiaosheng, et al. Power quality data compression based on pattern similarity measurement[J]. Transactions of China Electrotechnical Society, 2011, 26(10): 39-46, 61.
[14] 刘晓胜, 王新库, 黄南天, 等. 基于模式相似度和LZW压缩编码的电能质量数据压缩方法[J]. 电力自动化设备, 2012, 32(3): 53-57.
Liu Xiaosheng, Wang Xinku, Huang Nantian, et al. Power quality data compression based on pattern similarity and LZW compressed encoding[J]. Electric Power Automation Equipment, 2012, 32(3): 53-57.
[15] 刘博, 刘晓胜, 徐殿国. 基于新距离测度的电能质量测量数据压缩算法[J]. 电工技术学报, 2013, 28(9): 129-136.
Liu Bo, Liu Xiaosheng, Xu Dianguo. Power quality measuring data compression based on new distance measurement[J]. Transactions of China Electro- technical Society, 2013, 28(9): 129-136.
[16] Schölkopf B, Platt J C, Shawe-Taylor J, et al. Estimating the support of a high-dimensional distri- bution[J]. Neural Computation, 2001, 13(7): 1443-1471.
[17] Lee C Y, Shen Y X. Optimal feature selection for power-quality disturbances classification[J]. IEEE Transactions on Power Delivery, 2011, 26(4): 2342- 2351.
[18] 易吉良, 彭建春, 罗安, 等. 电能质量信号的改进S变换降噪方法[J]. 仪器仪表学报, 2010, 31(1): 32-37.
Yi Jiliang, Peng Jianchun, Luo An, et al. Power quality signal denoising using modified S- transform[J]. Chinese Journal of Scientific Instru- ment, 2010, 31(1): 32-37.
[19] Radil T, Ramos P M, Janeiro F M, et al. PQ monitoring system for real-time detection and classification of disturbances in a single-phase power system[J]. IEEE Transactions on Instrumentation and Measurement, 2008, 57(8): 1725-1733.