基于电流谐波特征的矿用电缆劣化监测与故障诊断

卢润戈; 徐涛; 周卓蓓; 李茂; 黄潮灿

doi:10.13272/j.issn.1671-251x.18144

基于电流谐波特征的矿用电缆劣化监测与故障诊断

doi: 10.13272/j.issn.1671-251x.18144

卢润戈^{1, 2,},
徐涛^{1, 2},
周卓蓓^{1, 2},
李茂^{1, 2},
黄潮灿^{1, 2}

1.
中国南方电网有限责任公司电缆系统安全与运维保障重点实验室，广东广州　510623
2.
广东电网有限责任公司广州供电局，广东广州　510013

基金项目: 中国南方电网有限责任公司科技项目（GZHKJXM20200011）。

详细信息

作者简介:
卢润戈（1991—），男，广东广州人，工程师，硕士，研究方向为电力系统优化运行与控制等，E-mail：lurunge@foxmail.com

中图分类号: TD61
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出版历程
- 收稿日期: 2023-07-27
- 修回日期: 2023-10-12
- 网络出版日期: 2023-10-24

Degradation monitoring and fault diagnosis of mining cables based on current harmonic features

LU Runge^{1, 2
,},
XU Tao^{1, 2},
ZHOU Zhuobei^{1, 2},
LI Mao^{1, 2},
HUANG Chaocan^{1, 2}

1.
Key Laboratory of Cable System Safety and Operation and Maintenance, China Southern Power Grid Company Limited, Guangzhou 510623, China
2.
Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou 510013, China

摘要

摘要: 矿用电缆受煤矿恶劣环境影响，容易发生绝缘劣化、护套受损等情况，传统的矿用电缆检测多采用低压脉冲法、局放法等离线诊断方式，操作复杂，准确度低，难以满足现代煤矿生产需求。而现有基于谐波的电缆故障诊断方法存在检测装置笨重、检测精确低、难以在煤矿应用等问题。针对上述问题，提出一种基于电流谐波特征的矿用电缆劣化监测与故障诊断方法。提取电缆中高次谐波含量信息作为故障特征向量，对特征向量进行归一化处理后导入极限梯度提升树（XGBoost）模型，结合已知电缆故障劣化度数据，形成训练样本集，训练XGBoost模型，最后通过构建的XGBoost模型对电缆劣化度进行实时监测和故障诊断。仿真结果表明：针对电缆不同部位提取的高次谐波向量的相对能量有明显不同，表明提取的高次谐波向量可表征电缆不同部位的运行状态；XGBoost模型的拟合优度参数R²高达 0.93，且误差较小。案例分析结果验证了基于电流谐波特征的矿用电缆劣化监测与故障诊断方法可对矿用电缆运行状态及劣化故障进行实时、准确的监测和诊断。
- 矿用电缆 /
- 故障诊断 /
- 劣化监测 /
- 电流谐波特征 /
- 极限梯度提升树 /
- XGBoost模型
Abstract: Mining cables are affected by the harsh environment of coal mines, and are prone to insulation degradation and sheath damage. The traditional offline diagnostic methods such as low-voltage pulse method and partial discharge method are often used for detecting mining cables. The methods are complex to operate and have low accuracy, making it difficult to meet the needs of modern coal mine production. However, the existing harmonic based cable fault diagnosis methods have problems such as bulky detection devices, low detection precision, and difficulty in application in coal mines. In order to solve the above problems, a degradation monitoring and diagnosing method of mining cables based on current harmonic features is proposed. The method extracts high-order harmonic content information in cables as fault feature vectors, normalize the feature vectors, and then import them into extreme gradient boost tree (XGBoost) model. Combined with known cable fault degradation value data, a training sample set is formed to train the XGBoost model. Finally, the method uses the constructed XGBoost model to monitor and diagnose cable degradation in real-time. The simulation results show that the relative energy of the extracted high-order harmonic vectors from different parts of the cable is significantly different. The extracted high-order harmonic vectors can characterize the operating status of different parts of the cable. The goodness of fit parameter R² of the XGBoost model is as high as 0.93, and the error is small. The case analysis results verify that the degradation monitoring and fault diagnosis method of mining cables based on current harmonic features can provide real-time and accurate monitoring and diagnosis of the operation status and degradation faults of mining cables.
- mining cables /
- fault diagnosis /
- degradation monitoring /
- current harmonic features /
- extreme gradient boost tree /
- XGBoost model

HTML全文

图 1 电力电缆中的磁场与电流

Figure 1. Magnetic field and current in cable

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图 2 XGBoost模型构建流程

Figure 2. XGBoost model construction process

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图 3 谐波信号采集电路结构

Figure 3. Structure of harmonic signal acquisition circuit

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图 4 电缆故障诊断流程

Figure 4. Cable fault diagnosis process

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图 5 电缆谐波向量能量谱

Figure 5. Energy spectrum of cable harmonic vector

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图 6 绝缘体劣化度预测结果

Figure 6. Prediction results of insulation degradation degree

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图 7 屏蔽层劣化度预测结果

Figure 7. Prediction results of shielding layer degradation

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图 8 保护层劣化度预测结果

Figure 8. Prediction results of degradation degree of protective layer

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图 9 电缆接头劣化度预测结果

Figure 9. Prediction results of cable joint deterioration

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图 10 电缆主体部运行状态实时数据

Figure 10. Real time data of the status of main body of the cable

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图 11 诊断报告

Figure 11. Diagnose report

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图 12 故障电缆

Figure 12. Faulty cable

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表 1 矿用电缆劣化状态与高次谐波的关系

Table 1. Relationship between mining power cable degradation state and higher harmonics

电力电缆部位	劣化类型		第一主成分谐波次数（贡献率）	其他主成分谐波次数（贡献率）	累计故障贡献率/%
主体部	绝缘体劣化	初期劣化型	3（41%），5（41%）	4（6%），2（6%）	94
		机械性损伤	2（55%）	4（16%），3（9%），5（6%）	86
		电气性损伤	5（59%）	3（20%），4（8%），2（6%）	93
		自然劣化型	5（52%）	3（28%），4（7%），2（6%）	93
	屏蔽层劣化		3（25%）	5（24%），2（23%），4（18%）	90
	保护层劣化		2（39%）	4（29%），3（10%），5（7%）	85
连接部	发热		7（53%）	10（15%），9（11%），8（7%），6（5%）	91
	污损		8（35%）	7（29%），9（13%），10（11%），6（7%）	95
	龟裂		9（33%）	8（25%），7（21%），10（8%），6（5%）	92
	变形		10（30%）	7（23%），8（17%），9（15%），6（6%）	91

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表 2 部分主体部样本数据

Table 2. Part of the main body sample data

序号	H₂	H₃	H₄	H₅	劣化度
序号	H₂	H₃	H₄	H₅	绝缘体	屏蔽层	保护层
1	1.8	2.3	1.5	4.9	36.8	61.2	52.2
2	2.4	2.1	1.4	5.3	37.8	54.1	47.7
3	3.8	1.7	1.8	0.9	76.8	31.6	46.2
4	3.4	2.1	1.4	2.4	63.0	49.9	49.9
5	2.0	2.1	1.6	4.9	39.3	58.5	56.4
6	2.9	1.3	1.2	2.3	75.0	43.8	67.6
7	3.0	4.4	1.2	4.2	78.4	84.1	71.2
8	3.0	6.0	1.0	2.1	78.2	95.7	54.0
9	2.8	1.0	1.5	2.5	19.7	16.0	26.1
10	2.8	1.5	1.0	0.5	69.0	41.4	48.7
11	3.0	5.3	0.9	1.9	84.0	94.0	55.7
12	3.0	1.4	1.7	5.9	49.7	42.9	70.2
13	2.8	1.4	1.2	1.6	57.8	39.5	47.9
14	2.4	1.1	1.1	1.9	44.6	38.1	46.8
15	2.9	5.7	0.9	1.5	78.1	95.4	50.6

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表 3 部分连接部样本数据

Table 3. Part of the connection part sample data

序号	H₇	H₈	H₉	H₁₀	电缆接头劣化度
1	1.2	0.4	0.4	0.4	82.6
2	1.5	0.6	0.5	0.5	81.3
3	1.2	0.4	0.5	0.7	78.8
4	0.6	0.5	0.4	0.3	47.7
5	0.7	0.5	0.4	0.4	46.2
6	0.5	0.4	0.3	0.2	49.9
7	0.7	0.4	0.3	0.2	56.4
8	0.5	0.5	0.5	0.5	67.6
9	0.8	0.4	0.4	0.4	71.2
10	0.6	0.5	0.4	0.4	54.0
11	0.5	0.4	0.4	0.2	46.1
12	0.6	0.4	0.4	0.2	46.7
13	0.6	0.4	0.4	0.3	55.7
14	0.8	0.6	0.5	0.3	70.2
15	0.7	0.4	0.4	0.3	47.9

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表 4 电缆主体部和连接部预测精度评估参数

Table 4. Prediction accuracy evaluation parameters for cable main body and connection parts

电缆	R²	${\rm{MSE}}$	${\rm{MRSE}}$	${\rm{MAPE}}$
绝缘层	0.9354	0.001824	0.0422	0.0670
屏蔽层	0.9295	0.000798	0.0282	0.0468
保护层	0.9385	0.001736	0.0412	0.0607
电缆接头	0.9510	0.000959	0.0310	0.0286

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表 5 部分高次谐波含有率

Table 5. Part of the high-order harmonic content

序号	H₂	H₃	H₄	H₅	H₇	H₈	H₉	H₁₀	时间
1	1.5	1.1	1.2	0.9	0.5	0.4	0.4	0.3	2021−05−18
2	1.4	1.3	1.4	1.1	0.5	0.4	0.3	0.4	2021−05−18
3	1.6	1.1	1.5	0.9	0.7	0.6	0.4	0.3	2021−05−18
4	1.5	1.2	1.4	1.3	0.6	0.3	0.3	0.2	2021−05−18
5	3.8	1.5	1.5	1.2	0.8	0.5	0.2	0.1	2021−05−19
6	2.4	1.4	1.4	1.3	0.6	0.5	0.4	0.3	2021−05−19
7	3.8	1.8	1.8	1.2	0.7	0.4	0.3	0.2	2021−05−19
8	3.4	2.1	1.4	1.0	0.9	0.5	0.5	0.3	2021−05−19
9	3.3	2.1	1.6	1.1	0.6	0.3	0.4	0.2	2021−05−19

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