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

doi:10.11930/j.issn.1004-9649.202401110

Abstract

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

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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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202401110

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

Figures/Tables 17

References 34

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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