[1]夏焰坤,郑高萍,黄鹏,等.基于改进龙卷风优化算法的谐波阻抗估计方法[J].郑州大学学报(工学版),2027,48(XX):1-10.[doi:10.13705/j.issn.1671-6833.2027.01.002]
　XIA Yankun,ZHENG Gaoping,HUANG Peng,et al.Harmonic Impedance Estimation Method Based on Improved Tornado Optimization Algorithm[J].Journal of Zhengzhou University (Engineering Science),2027,48(XX):1-10.[doi:10.13705/j.issn.1671-6833.2027.01.002]
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基于改进龙卷风优化算法的谐波阻抗估计方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
48
期数:
2027年XX
页码:
1-10
栏目:
出版日期:
2027-12-10

文章信息/Info

Title:
Harmonic Impedance Estimation Method Based on Improved Tornado Optimization Algorithm
作者:
夏焰坤1, 郑高萍1, 黄鹏2, 张恒1, 周杭1
1. 西华大学 电气与电子信息学院,四川 成都 610039;2. 国网宜宾供电公司,四川 宜宾 644000
Author(s):
XIA Yankun 1 , ZHENG Gaoping1, HUANG Peng 1 , ZHANG Heng 1 , ZHOU Hang1
1. School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China; 2. State Grid Yibin Power Supply Company, Yibin 644000, China
关键词:
系统侧谐波阻抗; 最大信息系数; PELT算法; 深度混合核极限学习机; 龙卷风算法
Keywords:
System-side harmonic impedance;  maximum information coefficient;  PELT algorithm;  deep hybrid kernel extreme learning machine;  Tornado optimizer with Coriolis force
分类号:
TM711 TP18
DOI:
10.13705/j.issn.1671-6833.2027.01.002
文献标志码:
A
摘要:
针对系统侧谐波阻抗在电网运行方式变化及电容器投切等工况下发生突变，且在背景谐波波动较大时估计精度下降的问题，提出一种基于改进龙卷风优化算法优化深度混合核极限学习机（DHKELM）的谐波阻抗估计方法。首先，为削弱背景谐波和异常值的影响，采用最大信息系数（MIC）筛选谐波电压和谐波电流幅值强相关性数据；其次，使用PELT算法检测出的阻抗粗估值突变点对谐波数据样本进行划分。最后，对每段谐波数据分别使用DHKELM估计得到系统侧谐波阻抗。此外，为提高模型预测精度，提出了一种基于多策略混沌和Nelder‑Mead单纯形法改进的龙卷风优化算法 ITOC 对 DHKELM 模型的隐含层节点数、核参数和权重等进行优化。基于诺顿等效模型及IEEE13节点系统开展仿真，并结合实测数据进行案例分析。结果表明：在用户侧谐波阻抗非大于系统侧的情况下，诺顿仿真中所提方法在不同背景谐波波动系数下阻抗估计的误差均较小；在IEEE13节点系统仿真中，幅值和相位估计的RMSE分别为0.002 Ω和0.058°；在实例分析中，幅值和相位的RMSE分别为0.03 Ω和0.02°，表现出较高的估计精度与稳定性。
Abstract:
To address the issue of sudden changes in system‑side harmonic impedance under operating conditions such as changes in power grid operating modes and capacitor switching, as well as the decline in estimation accuracy when background harmonic fluctuations are significant, a harmonic impedance estimation method based on the deep hybrid kernel extreme learning machine (DHKELM) optimized by improved tornado optimization algorithm was proposed. Firstly, to mitigate the effects of background harmonics and outliers, the maximum information coefficient (MIC) was used to filter data with strong correlations between harmonic voltage and harmonic current amplitudes. Secondly, the harmonic data samples were segmented based on the abrupt change points in the coarse impedance estimates detected by the PELT algorithm. Finally, the system‑side harmonic impedance was estimated for each segment of harmonic data using the DHKELM. Furthermore, to improve the model’s prediction accuracy, a tornado optimization algorithm (ITOC) enhanced by multi‑strategy chaos and the Nelder‑Mead simplex method was proposed to optimize the number of hidden layer nodes, kernel parameters, and weights of the DHKELM model. Simulations were conducted using the Norton equivalent model and an IEEE 13‑node system, and case studies were performed in conjunction with measured data. The results showed that, when the harmonic impedance on the customer‑side was no greater than that on the system‑side, the errors in impedance estimation under different background harmonic coefficients in the Norton simulation were all relatively small. In the IEEE 13‑node system simulation, the RMSE for amplitude and phase angle estimates were 0.002 Ω and 0.058°, respectively. In the case study analysis, the RMSE for amplitude and phase angle were 0.03 Ω and 0.02°, respectively, demonstrating high estimation accuracy and stability.

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更新日期/Last Update: 2026-07-24