基于CEEMDAN与NRBO-XGBoost的含光伏配电网漏电故障辨识

doi:10.11930/j.issn.1004-9649.202501003

摘要/Abstract

摘要：

针对在含光伏电源的配电网中，现有剩余电流保护装置难以实现漏电故障的准确识别，存在误动、拒动的问题，提出一种基于完全自适应噪声集合经验模态分解（complete ensemble empirical mode decomposition with adaptive noise，CEEMDAN）与牛顿拉夫逊优化算法（Newton-Raphson-based optimizer，NRBO）优化梯度提升决策树（eXtreme Gradient Boosting，XGBoost）的含光伏配电网漏电故障辨识模型。首先，采用CEEMDAN对含光伏配电网的漏电信号进行分解；然后，提取分解后各模态分量的能量熵构建漏电故障特征集；最后，将能量熵特征输入到NRBO-XGBoost识别模型，实现对含光伏配电网不同漏电状态的辨识。通过仿真数据对所提方法进行验证，结果表明：与其他模型相比，所提方法具有更高的辨识精度。

关键词: 光伏电源, 配电网, 牛顿拉夫逊法, 梯度提升决策树, 漏电故障识别

Abstract:

To address the problem that existing residual current protection devices are difficult to accurately identify leakage faults in the PV-integrated distribution networks, a leakage fault identification model for photovoltaic-integrated distribution networks based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Newton-Raphson-based optimizer-eXtreme Gradient Boosting (NRBO-XGBoost) is presented. Firstly, the CEEMDAN is used to decompose different leakage signals of the PV-integrated distribution networks. Then, the energy entropy of each decomposed modal component is extracted to construct the leakage fault feature set. Finally, the energy entropy features are input into the NRBO-XGBoost model to achieve the recognition of different leakage states of PV-integrated distribution networks. The effectiveness of the proposed method is verified by the simulation data. The results show that compared with other models, the proposed method has the highest recognition accuracy.

Key words: photovoltaic power supply, distribution network, Newton-Raphson-based optimizer, extreme gradient boosting, leakage fault identification

刘晗, 刘金东, 李贺, 李彦立, 于起媛, 赵远, 耿亚男. 基于CEEMDAN与NRBO-XGBoost的含光伏配电网漏电故障辨识[J]. 中国电力, 2025, 58(9): 23-32.

LIU Han, LIU Jindong, LI He, LI Yanli, YU Qiyuan, ZHAO Yuan, GENG Yanan. Leakage Fault Identification of PV-Integrated Distribution Networks Based on CEEMDAN and NRBO-XGBoost[J]. Electric Power, 2025, 58(9): 23-32.

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https://www.electricpower.com.cn/CN/Y2025/V58/I9/23

图/表 13

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模态分量	能量熵
模态分量	线路正常漏电	光伏漏电	三相不平衡漏电	生物体触电
IMF1	3.1×10^–7	8.3×10^–7	0.0017	0.0327
IMF2	7.9×10^–8	2.3×10^–7	0.0065	0.0092
IMF3	8.7×10^–7	3.4×10^–6	0.0186	0.0976
IMF4	7.7×10^–7	0.0061	0.0175	0.0923
IMF5	7.3×10^–7	0.0258	0.0170	0.0896
IMF6	7.2×10^–7	0.0037	0.0168	0.0878
IMF7	7.1×10^–7	0.0014	0.0195	0.0889
IMF8	6.2×10^–7	0.0027	0.0185	0.0878
IMF9	6.5×10^–7	0.0128	0.0181	0.1026
IMF10	8.4×10^–7	0.0048	0.0149	0.0998
IMF11	2.4×10^–5	0.0443	0.0268	0.0786
IMF12	0.0113	0.0008	0.0674	0.0706
IMF13	0.0151	0.0578	0.0379	0.1170
IMF14			0.2987	0.4561
IMF15			1.0029	10.2028

模态分量	能量熵
模态分量	线路正常漏电	光伏漏电	三相不平衡漏电	生物体触电
IMF1	3.1×10^–7	8.3×10^–7	0.0017	0.0327
IMF2	7.9×10^–8	2.3×10^–7	0.0065	0.0092
IMF3	8.7×10^–7	3.4×10^–6	0.0186	0.0976
IMF4	7.7×10^–7	0.0061	0.0175	0.0923
IMF5	7.3×10^–7	0.0258	0.0170	0.0896
IMF6	7.2×10^–7	0.0037	0.0168	0.0878
IMF7	7.1×10^–7	0.0014	0.0195	0.0889
IMF8	6.2×10^–7	0.0027	0.0185	0.0878
IMF9	6.5×10^–7	0.0128	0.0181	0.1026
IMF10	8.4×10^–7	0.0048	0.0149	0.0998
IMF11	2.4×10^–5	0.0443	0.0268	0.0786
IMF12	0.0113	0.0008	0.0674	0.0706
IMF13	0.0151	0.0578	0.0379	0.1170
IMF14			0.2987	0.4561
IMF15			1.0029	10.2028

算法	参数名称	参数值
NRBO	种群规模	20
	最大迭代次数	50
	搜索空间维度	3
	避免陷进决定因素	0.6
XGBoost	决策树深度	[1, 15]
	决策树数量	[10, 500]
	学习率	[0.0001, 0.3]
	子节点最小权重	1

算法	参数名称	参数值
NRBO	种群规模	20
	最大迭代次数	50
	搜索空间维度	3
	避免陷进决定因素	0.6
XGBoost	决策树深度	[1, 15]
	决策树数量	[10, 500]
	学习率	[0.0001, 0.3]
	子节点最小权重	1

模型	评价指标	状态1	状态2	状态3	状态4
XGBoost	准确率/%	92.5
	精确度/%	96.7	85.3	89.3	100.0
	召回率/%	96.7	96.7	83.3	93.3
	F1分数/%	96.7	90.6	86.2	96.5

NRBO- XGBoost	准确率/%	99.2
	精确度/%	100.0	100.0	96.8	100.0
	召回率/%	100.0	100.0	100.0	96.7
	F1分数/%	100.0	100.0	98.4	98.3