基于集合经验模态分解的增强核岭回归配电系统状态估计

doi:10.11930/j.issn.1004-9649.202401110

中国电力 ›› 2024, Vol. 57 ›› Issue (9): 156-168.DOI: 10.11930/j.issn.1004-9649.202401110

• 面向新型电力系统的城市电网关键技术 • 上一篇下一篇

基于集合经验模态分解的增强核岭回归配电系统状态估计

张玉敏¹(), 张涌琛¹, 叶平峰³(), 吉兴全¹, 石春友¹, 蔡富东², 李一宸¹

1. 山东科技大学电气与自动化工程学院，山东青岛　266590
2. 山东信通电子股份有限公司，山东淄博　255088
3. 山东科技大学储能技术学院，山东青岛　266590

收稿日期:2024-01-25 出版日期:2024-09-28 发布日期:2024-09-23
作者简介:张玉敏（1986—），女，博士，副教授，从事电力系统运行与控制研究，E-mail：ymzhang2019@sdust.edu.cn
叶平峰（1988—），男，通信作者，博士，讲师，从事电力系统优化调度和电压稳定分析研究，E-mail：ypfinput@163.com
基金资助:
山东省自然科学基金资助项目（ZR2023QE181，ZR2022ME219，ZR2021QE117）；中国博士后面上资助项目（2023M734092）。

Enhanced Kernel Ridge Regression and Ensemble Empirical Mode Decomposition Based Distribution Network State Estimation

Yumin ZHANG¹(), Yongchen ZHANG¹, Pingfeng YE³(), Xingquan JI¹, Chunyou SHI¹, Fudong CAI², Yichen LI¹

1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
2. Shandong Senter Electronic Co., Ltd., Zibo 255088, China
3. College of Energy Storage Technology, Shandong University of Science and Technology, Qingdao 266590, China

Received:2024-01-25 Online:2024-09-28 Published:2024-09-23
Supported by:
This work is supported by Shandong Province Natural Science Foundation (No.ZR2023QE181, ZR2022ME219, ZR2021QE117) and Chinese Postdoctoral Science Foundation (No.2023M734092).

摘要/Abstract

摘要：

针对配电网量测信息存在强非高斯噪声时会大幅干扰基于深度学习的状态估计模型滤波精度的问题，提出了一种基于集合经验模态分解的增强核岭回归状态估计方法。首先，使用集合经验模态分解筛除量测信息中的多数噪声数据，保障了后续滤波对数据可靠性的要求。然后，通过构建增强核岭回归状态估计模型，建立了量测信息与估计残差之间的映射关系，输入量测信息后可以得到估计结果与估计残差。最后，在标准IEEE 33节点与某市78节点系统上进行数值仿真，结果证明了该方法在强非高斯噪声干扰下具有较高的精确性和鲁棒性。

关键词: 配电系统, 状态估计, 核岭回归, 非高斯噪声, 集合经验模态分解

Abstract:

Addressing the significant interference in the filtering accuracy of deep learning-based state estimation models caused by strong non-Gaussian noise in distribution network measurement information, an enhanced kernel ridge regression state estimation method based on ensemble empirical mode decomposition is proposed. Firstly, the ensemble empirical mode decomposition is utilized to filter out most of the noise data in the measurement information, ensuring the reliability of data for subsequent filtering. Subsequently, an enhanced kernel ridge regression state estimation model is constructed to establish a mapping relationship between measurement information and estimation residuals. By inputting the measurement information, the estimation results and estimation residuals can be obtained. Finally, numerical simulations are conducted on the standard IEEE 33-node system and a city-level 78-node system, demonstrating that the proposed method exhibits high accuracy and robustness under the interference of strong non-Gaussian noise.

Key words: distribution system, state estimation, kernel ridge regression, non-gaussian noise, ensemble empirical mode decomposition

张玉敏, 张涌琛, 叶平峰, 吉兴全, 石春友, 蔡富东, 李一宸. 基于集合经验模态分解的增强核岭回归配电系统状态估计[J]. 中国电力, 2024, 57(9): 156-168.

Yumin ZHANG, Yongchen ZHANG, Pingfeng YE, Xingquan JI, Chunyou SHI, Fudong CAI, Yichen LI. Enhanced Kernel Ridge Regression and Ensemble Empirical Mode Decomposition Based Distribution Network State Estimation[J]. Electric Power, 2024, 57(9): 156-168.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202401110

https://www.electricpower.com.cn/CN/Y2024/V57/I9/156

图/表 17

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分量	拉普拉斯噪声		双峰高斯噪声
分量	有功功率	无功功率	有功功率	无功功率
IMF1	2.48000	2.32000	2.36000	2.19000
IMF2	1.91000	2.09000	1.88000	2.07000
IMF3	1.14000	1.08000	1.03000	1.14000
IMF4	0.63300	0.63800	0.67000	0.69400
IMF5	0.54200	0.51100	0.51500	0.60000
IMF6	0.44200	0.47000	0.46900	0.45400
IMF7	0.12900	0.11900	0.12900	0.13200
IMF8	0.04790	0.04570	0.04690	0.04570
IMF9	0.00457	0.00408	0.00447	0.00449
IMF10	0.00470	0.00463	0.00451	0.00494
IMF11	0.00196	0.00176	0.00194	0.00392

分量	拉普拉斯噪声		双峰高斯噪声
分量	有功功率	无功功率	有功功率	无功功率
IMF1	2.48000	2.32000	2.36000	2.19000
IMF2	1.91000	2.09000	1.88000	2.07000
IMF3	1.14000	1.08000	1.03000	1.14000
IMF4	0.63300	0.63800	0.67000	0.69400
IMF5	0.54200	0.51100	0.51500	0.60000
IMF6	0.44200	0.47000	0.46900	0.45400
IMF7	0.12900	0.11900	0.12900	0.13200
IMF8	0.04790	0.04570	0.04690	0.04570
IMF9	0.00457	0.00408	0.00447	0.00449
IMF10	0.00470	0.00463	0.00451	0.00494
IMF11	0.00196	0.00176	0.00194	0.00392

量测降噪情况	拉普拉斯噪声		双峰高斯噪声
量测降噪情况	AE	RMSE	AE	RMSE
有功降噪前	7.21×10^–3	1.02×10^–2	7.92×10^–3	9.92×10^–3
EEMD降噪后	6.74×10^–3	9.40×10^–3	4.95×10^–3	6.31×10^–3
无功降噪前	9.44×10^–3	9.53×10^–3	8.10×10^–3	1.02×10^–2
EEMD降噪后	6.58×10^–3	6.58×10^–3	5.34×10^–3	6.60×10^–3

量测降噪情况	拉普拉斯噪声		双峰高斯噪声
量测降噪情况	AE	RMSE	AE	RMSE
有功降噪前	7.21×10^–3	1.02×10^–2	7.92×10^–3	9.92×10^–3
EEMD降噪后	6.74×10^–3	9.40×10^–3	4.95×10^–3	6.31×10^–3
无功降噪前	9.44×10^–3	9.53×10^–3	8.10×10^–3	1.02×10^–2
EEMD降噪后	6.58×10^–3	6.58×10^–3	5.34×10^–3	6.60×10^–3

标准差	降噪前		EEMD降噪后
标准差	AE	RMSE	AE	RMSE
0.02	2.91×10^–2	4.14×10^–2	2.56×10^–2	3.34×10^–2
0.03	3.48×10^–2	4.89×10^–2	2.51×10^–2	3.31×10^–2
0.04	3.62×10^–2	5.13×10^–2	2.90×10^–2	3.78×10^–2
0.05	3.80×10^–2	5.33×10^–2	2.05×10^–2	2.67×10^–2

基于集合经验模态分解的增强核岭回归配电系统状态估计

Enhanced Kernel Ridge Regression and Ensemble Empirical Mode Decomposition Based Distribution Network State Estimation

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图/表 17

参考文献 34

相关文章 15

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Metrics

标准差	KRRSE^[33]		EKRRSE
标准差	MAPE	RMSE	MAPE	RMSE
0.01	0.00742	9.59×10^–5	0.00655	8.54×10^–5
0.02	0.01610	2.09×10^–4	0.01230	1.56×10^–4
0.03	0.01540	1.93×10^–4	0.01450	1.78×10^–4
0.04	0.01990	2.53×10^–4	0.01890	2.42×10^–4
0.05	0.02530	3.28×10^–4	0.02490	3.18×10^–4

情景	节点	KRRSE^‎[33]		EKRRSE
情景	节点	MAPE	RMSE	MAPE	RMSE
1	12	0.0347	4.13×10^–4	0.0248	4.00×10^–4
1	32	0.0558	7.26×10^–4	0.0524	6.37×10^–4
2	15	0.0443	4.44×10^–4	0.0367	3.70×10^–4
2	23	0.0227	2.97×10^–4	0.0193	2.46×10^–4

算法	运行时间/s	误差/10^–4
CPF	0.510	0.049
RCPF	0.550	0.046
KRRSE	0.039	0.051
EKRRSE	0.082	0.044

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量测降噪情况	拉普拉斯噪声		双峰高斯噪声
量测降噪情况	AE	RMSE	AE	RMSE
有功降噪前	6.68×10^–3	9.45×10^–3	8.66×10^–3	1.19×10^–2
EEMD降噪后	5.87×10^–3	7.49×10^–3	6.97×10^–3	9.07×10^–3
无功降噪前	7.31×10^–3	1.03×10^–2	8.96×10^–3	1.21×10^–2
EEMD降噪后	6.09×10^–3	7.84×10^–3	7.22×10^–3	9.33×10^–3

量测降噪情况	拉普拉斯噪声		双峰高斯噪声
量测降噪情况	AE	RMSE	AE	RMSE
有功降噪前	6.68×10^–3	9.45×10^–3	8.66×10^–3	1.19×10^–2
EEMD降噪后	5.87×10^–3	7.49×10^–3	6.97×10^–3	9.07×10^–3
无功降噪前	7.31×10^–3	1.03×10^–2	8.96×10^–3	1.21×10^–2
EEMD降噪后	6.09×10^–3	7.84×10^–3	7.22×10^–3	9.33×10^–3

标准差	降噪前		EEMD降噪后
标准差	AE	RMSE	AE	RMSE
0.02	1.44×10^–2	2.08×10^–2	1.03×10^–2	1.40×10^–2
0.03	2.11×10^–2	2.97×10^–2	1.49×10^–2	1.97×10^–2
0.04	2.86×10^–2	4.03×10^–2	2.02×10^–2	2.63×10^–2
0.05	3.59×10^–2	5.13×10^–2	2.56×10^–2	3.35×10^–2

标准差	降噪前		EEMD降噪后
标准差	AE	RMSE	AE	RMSE
0.02	1.44×10^–2	2.08×10^–2	1.03×10^–2	1.40×10^–2
0.03	2.11×10^–2	2.97×10^–2	1.49×10^–2	1.97×10^–2
0.04	2.86×10^–2	4.03×10^–2	2.02×10^–2	2.63×10^–2
0.05	3.59×10^–2	5.13×10^–2	2.56×10^–2	3.35×10^–2

模态框（Modal）标题

基于集合经验模态分解的增强核岭回归配电系统状态估计

Enhanced Kernel Ridge Regression and Ensemble Empirical Mode Decomposition Based Distribution Network State Estimation

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