基于SVM级联决策树的复合电能质量扰动识别*

doi:10.11985/2023.02.014

摘要/Abstract

摘要：

针对双重及以上复合电能质量扰动特征重叠和识别困难的问题，提出了一种基于支持向量机(Support vector machine, SVM)级联决策树的电能质量复合扰动分类识别方法。首先，基于S变换提取的扰动初始特征，针对电压暂降、谐波、电压中断等7种单一扰动构建多个二分类SVM；然后通过SVM的二分类统计学习进行扰动蕴含特征的重构，以降低复合扰动的特征重叠；最后利用新构建的复合扰动特征，结合CART决策树的二分离散特征处理，实现SVM级联决策树的复合电能质量扰动多分类识别。仿真试验表明，所提方法可有效降低复合扰动下特征重叠程度，有较强的抗噪性，能准确识别包括双重及多重复合的21种电能质量扰动。

关键词: 电能质量, 复合扰动, 级联分类器, 支持向量机, 决策树

Abstract:

Aiming at the problem of features overlapping and difficult recognition of complex power quality(PQ) disturbance, a PQ disturbance recognition method is proposed based on support vector machine(SVM) cascaded decision tree. Firstly, based on the initial characteristics of the disturbance extracted by the S-transform, 7 two-class SVMs are constructed for 7 single PQ disturbances such as voltage sag, harmonics, and voltage interruption. Then, in order to reduce the feature overlap of the complex disturbance, two-class statistical machine learning of the SVMs are used to reconstruct the features of disturbance. Finally, the constructed features are used to realize multi-class recognition of the complex PQ disturbance by the SVM cascade decision tree, combining with the two discrete feature processing of the CART decision tree. Simulation results show that the proposed method can effectively reduce the degree of feature overlap for composite PQ disturbances with strong noise immunity, and accurately identify 21 types of PQ disturbances including double and multiple complex disturbances.

Key words: Power quality, complex disturbances, cascade classifier, support vector machine, decision tree

中图分类号:

TM714

陈诺, 吕干云, 叶加星. 基于SVM级联决策树的复合电能质量扰动识别^*[J]. 电气工程学报, 2023, 18(2): 149-156.

CHEN Nuo, LÜ Ganyun, YE Jiaxing. Recognition of Complex PQ Disturbances Based on SVM Cascaded Decision Tree[J]. Journal of Electrical Engineering, 2023, 18(2): 149-156.

图/表 10

表1

表2

电能质量扰动信号模型"

类型号	信号类型	表达式	参数
T1	电压暂降	$v (t) = 1 − a u t 1 − u t 2 cos (ω t)$	$0.1 ≤ a ≤ 0.9$ , $0.1 ≤ a ≤ 0.9$
T2	电压暂升	$v (t) = 1 + a u t 1 − u t 2 cos (ω t)$	$0.1 ≤ a ≤ 0.9$ , $0.1 ≤ a ≤ 0.9$
T3	谐波	$v (t) = cos (ω t) + α 3 cos (3 ω t) + α 5 cos (5 ω t) + α 7 cos (7 ω t)$	$f = 50 Hz$ , $f = 50 Hz$
T4	电压中断	$v (t) = 1 − a u t 1 − u t 2 cos (ω t)$	$0.9 ≤ a ≤ 1$ , $0.9 ≤ a ≤ 1$
T5	闪变	$v (t) = 1 + α cos (β ω t) cos (ω t)$	$0.1 ≤ α ≤ 0.2$ , $0.1 ≤ α ≤ 0.2$
T6	振荡暂态	$v (t) = cos (ω t) + a exp − (t − t 1) / τ ⋅ cos ω n (t − t 1) ⋅ u (t 1) − u (t 2)$	$0.1 ≤ a ≤ 0.8$ , $0.1 ≤ a ≤ 0.8$ , $0.1 ≤ a ≤ 0.8$ , $0.1 ≤ a ≤ 0.8$
T7	脉冲暂态	$v (t) = cos (ω t) + a u (t 1) − u (t 2)$	$1 ≤ a ≤ 10$ , $1 ≤ a ≤ 10$

表2

图1

图2

图3

图4

图5

表3

表4

表5

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类型号	信号类型	类型号	信号类型
T1	电压暂降	T12	电压暂升+振荡暂态
T2	电压暂升	T13	闪变+振荡暂态
T3	谐波	T14	谐波+振荡暂态
T4	电压中断	T15	电压暂降+脉冲暂态
T5	闪变	T16	电压暂升+脉冲暂态
T6	振荡暂态	T17	谐波+脉冲暂态
T7	脉冲暂态	T18	电压暂降+振荡暂态+谐波
T8	电压暂降+谐波	T19	电压暂升+振荡暂态+谐波
T9	电压中断+谐波	T20	闪变+脉冲暂态+谐波
T10	闪变+谐波	T21	电压暂降+振荡暂态+ 脉冲暂态+谐波
T11	电压暂降+振荡暂态

扰动类型		分类准确率(%)
扰动类型		20 dB	30 dB	40 dB
T1	电压暂降	98.2	100.0	100.0
T2	电压暂升	95.7	97.3	99.5
T3	谐波	96.3	99.3	100.0
T4	电压中断	96.6	99.2	100.0
T5	闪变	96.3	98.6	100.0
T6	暂态振荡	96.8	99.8	100.0
T7	脉冲暂态	96.0	97.5	98.7
T8	电压暂降+谐波	95.1	97.7	98.5
T9	电压中断+谐波	95.6	97.8	99.2
T10	闪变+谐波	95.8	99.4	100.0
T11	电压暂降+振荡暂态	96.4	97.6	98.3
T12	电压暂升+脉冲暂态	97.0	97.9	98.2
T13	闪变+振荡暂态	96.7	97.0	100.0
T14	谐波+振荡暂态	95.8	98.3	100.0
T15	电压暂降+脉冲暂态	96.8	98.6	99.6
T16	电压暂升+脉冲暂态	95.1	97.6	98.3
T17	谐波+脉冲暂态	96.1	97.7	98.7
T18	电压暂降+振荡暂态+谐波	97.0	97.4	97.9
T19	电压暂升+振荡暂态+谐波	95.5	97.2	97.5
T20	闪变+脉冲暂态+谐波	96.1	96.8	97.2
T21	电压暂降+振荡暂态 +脉冲暂态+谐波	94.2	96.3	96.7
	平均准确率(%)	96.2	98.1	99.0

分类方法	不同噪声强度下的分类准确率(%)
分类方法	20 dB	30 dB	40 dB
SVM	94.6	95.2	95.7
决策树	94.2	94.5	94.8
量化神经网络^[12]	95.9	96.9	98.0
SVM级联决策树	96.2	98.1	99.0

分类方法	训练样本	测试样本	分类准确率(%)
决策树	840	210	89.2
	1 680	420	93.5
	3 360	840	94.5
SVM级联决策树	840	210	95.3
	1 680	420	97.9
	3 360	840	98.1