基于SVM的电压扰动识别研究
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摘要
针对电力系统中出现的电压暂升(暂降、暂断)以及谐波、电压脉冲和电压闪变这六种常见电压扰动进行建模,将特征量输入到SVM进行电能质量扰动多类分类。首先采用相应小波包分解算法将电能质量中某一频段内的信号分解到特定频段上,其次在这些特定频段上提取特征向量,最后针对该特征向量构造相应支持向量机分类器。结果表明,在较复杂的电能质量扰动情况中,支持向量机分类器仍能实现对信号的精确分类,对电能质量监测具有很好的应用价值。
The six common voltage disturbances such as voltage sag( temporary break) and harmonics,voltage pulse and voltage flicker appear in the power system are modeled,and the feature quantity is input into the SVM to classify the power quality disturbance. Firstly,the corresponding wavelet packet decomposition algorithm is used to decompose the signal in a certain frequency band of power quality into a specific frequency band,and simulate the complex interference signal in the actual power grid. Feature vectors are then extracted on these particular frequency bands. Finally,construct the corresponding support vector for the feature vector. The results show that the support vector machine classifier can still accurately classify the signal in more complex power quality disturbances,which has good application value for power quality monitoring.
The six common voltage disturbances such as voltage sag( temporary break) and harmonics,voltage pulse and voltage flicker appear in the power system are modeled,and the feature quantity is input into the SVM to classify the power quality disturbance. Firstly,the corresponding wavelet packet decomposition algorithm is used to decompose the signal in a certain frequency band of power quality into a specific frequency band,and simulate the complex interference signal in the actual power grid. Feature vectors are then extracted on these particular frequency bands. Finally,construct the corresponding support vector for the feature vector. The results show that the support vector machine classifier can still accurately classify the signal in more complex power quality disturbances,which has good application value for power quality monitoring.
引文
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[2]徐永海,肖湘宁,杨以涵,等.基于dq变换和ANN的电能质量扰动辨识[J].电力系统自动化,2001,25(14):24-28.
[3]祖军,郝润科,杨光,等.智能电网中电能质量监测系统研究与设计[J].信息技术,2013,37(2):179-182.
[4]周传泽,周传泽,吴汉熙,等.低压电网电能质量检测与无功补偿系统的设计[J].信息技术,2015,39(5):202-205.
[5]Jongin S,Seungryong K,Liu L,et al. Fast illumination-robust foreground detection using hierarchical distribution map for real-time video surveillance system[J]. Expert Systems with Applications,2016,66(1):32-41.
[6]Math H J,Bollen,Irene Y H Gu. Signal processing of power quality disturbances[M]. IEEE Press Series on Power Engineering,2006.
[7] Bellavista P,Cardone G,Corradi A,et al. Convergence of manet and wsn in iot urban scenarios[J]. IEEE Sensors Journal,2013,13(10):3558-3567.
[8]Ahila R,Sadasivam V. Stransform based extreme learning machine for power system disturbances classification[J]. The Institution of Engineers,2013,94(3):179-191.
[9]秦英林,田立军,常学飞.基于小波变换能量分布和神经网络的电能质量扰动分类[J].电力自动化设备,2009,29(7):64-67.
[10]张文涛,王成山.基于改进贝叶斯分类法的电能质量扰动分类方法[J].电网技术,2007,31(7):22-25.
[11]宋晓芳,陈劲操.基于支持向量机的动态电能质量扰动分类方法[J].电力自动化设备,2006,26(4):39-42.
[12]程志友,梁栋,王年,等.一种快速暂态电能质量扰动定位方法[J].电力系统自动化,2007,31(27):73-76.
[13]丁世飞,齐丙娟,谭红艳.支持向量机理论与算法研究综述[J].电子科技大学学报,2011,40(1):2-10.