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| Localization and Identification Research of Harmonic Disturbance in Power Distribution System |
| He Julong1, Wang Genping1, 2, Liu Dan1, Tang Youming1 |
1. Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education, Xiangtan University, Xiangtan, Hunan 411105;;
2. Shenzhen Polytechnic, Shenzhen, Guangdong 518055 |
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Abstract According to the nonstationarity, mutability and short duration of harmonic disturbance in power distribution system, it is difficult to localize and identify harmonic disturbance with high speed and accuracy. In order to improve localization and identification results of harmonic disturbance, a new method is proposed based on lifting wavelet and improved BP neural network. At first, the Euclidean decomposition principle is used to obtain db4 wavelet lifting scheme. Then, harmonic disturbance signal is decomposed through lifting wavelet analysis, and mutation peak of harmonic disturbance is localized using lifting wavelet modulus maxim. At last, traditional BP algorithm is improved by combining increasing momentum method with self-adaption learning rate method, and improved BP neural network is used to identify harmonic disturbance. The simulation results show that the proposed method can better localize the harmonic disturbances’ time information with high speed and accuracy, and can identify harmonic disturbance with high discrimination ratio.
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Published: 13 December 2016
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| Cite this article: |
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He Julong,Wang Genping,Liu Dan等. Localization and Identification Research of Harmonic Disturbance in Power Distribution System[J]. Electrical Engineering, 2016, 17(12): 25-30.
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| URL: |
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http://dqjs.cesmedia.cn/EN/Y2016/V17/I12/25
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[1] 郝晓弘, 张雪薇, 陈伟, 等. 统一电能质量调节器的新型补偿策略及仿真[J]. 计算机仿真, 2012, 29(6): 300-303.
[2] 金幸, 唐治德, 余小闯, 等. 统一电能质量调节器最优能量控制及仿真[J]. 计算机仿真, 2011, 28(3): 321-324.
[3] 吴兆刚, 李唐兵, 姚建刚, 等. 基于小波和改进神经树的电能质量扰动分类[J]. 电力系统保护与控制, 2014, 42(24): 86-92.
[4] 赵凤展, 杨仁刚. 基于短时傅里叶变换的电压暂降扰动检测[J]. 中国电机工程学报, 2007, 27(10): 28-34.
[5] 徐方维, 杨洪耕, 叶茂清, 等. 基于改进S变换的电能质量扰动分类[J]. 中国电机工程学报, 2012, 32(4): 77-84.
[6] Biswal M, Dash P K. Detection and characterization of multiple power quality disturbances with a fast S-transform and decision tree based classifier[J]. Digital Signal Processing, 2013, 23(4): 1071-1083.
[7] 张淑清, 李盼, 师荣艳. 基于改进S变换的电能质量扰动分类新方法[J]. 仪器仪表学报, 2015, 36(4): 927-934.
[8] 潘从茂, 李凤婷. 基于小波变换的暂态电能质量的检测与识别[J]. 电测与仪表, 2013, 50(11): 69-72.
[9] 熊杰锋, 李群, 袁晓冬, 等. 电力系统谐波和间谐波检测方法综述[J]. 电力系统自动化, 2013, 37(11): 125-133.
[10] 于炳新, 崔涤尘, 许童羽, 等. 小波模极大值扰动电信号检测的关键问题研究[J]. 中国农机化学报, 2016, 37(2): 218-222.
[11] 丁明, 王磊, 毕锐. 基于改进BP神经网络的光伏发电系统输出功率短期预测模型[J]. 电力系统保护与控制, 2012, 40(11): 93-99, 148.
[12] 钱敏平, 龚光鲁. 随机过程论[M]. 北京: 北京大学出版社, 2000.
[13] 张明理, 杨晓亮, 滕云, 等. 基于主成分分析与前向反馈传播神经网络的风电场输出功率预测[J]. 电网技术, 2011, 35(3): 183-187. |
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2016, Vol. 17 
