方浩男, 王颖, 张欣然, 陆超, 张建新, 邱建. 考虑负荷内部扰动影响的改进类噪声负荷辨识方法[J]. 电网技术, 2025, 49(3): 1227-1235. DOI: 10.13335/j.1000-3673.pst.2024.1710
引用本文: 方浩男, 王颖, 张欣然, 陆超, 张建新, 邱建. 考虑负荷内部扰动影响的改进类噪声负荷辨识方法[J]. 电网技术, 2025, 49(3): 1227-1235. DOI: 10.13335/j.1000-3673.pst.2024.1710
FANG Haonan, WANG Ying, ZHANG Xinran, LU Chao, ZHANG Jianxin, QIU Jian. Improved Method of Ambient Signal Load Identification Considered the Impacts of Load Internal Disturbances[J]. Power System Technology, 2025, 49(3): 1227-1235. DOI: 10.13335/j.1000-3673.pst.2024.1710
Citation: FANG Haonan, WANG Ying, ZHANG Xinran, LU Chao, ZHANG Jianxin, QIU Jian. Improved Method of Ambient Signal Load Identification Considered the Impacts of Load Internal Disturbances[J]. Power System Technology, 2025, 49(3): 1227-1235. DOI: 10.13335/j.1000-3673.pst.2024.1710

考虑负荷内部扰动影响的改进类噪声负荷辨识方法

Improved Method of Ambient Signal Load Identification Considered the Impacts of Load Internal Disturbances

  • 摘要: 负荷时变性是制约负荷建模发展的难点之一,而基于类噪声的负荷建模方法为克服该难题提供了新的解决思路。但是,类噪声和传统大扰动性质不同,负荷随机波动在类噪声中作为重要扰动源难以忽略,导致了实测类噪声数据和典型负荷模型间频域性质的差异,也影响了基于类噪声负荷建模方法的效果。因此,该文研究内部扰动存在对负荷节点响应的影响,以解释频域差异的可能原因,同时分析类噪声激励下不同频段的内部扰动对负荷响应的影响强弱,并提出一种基于滤波的负荷辨识改进方法。最后,基于仿真验证了负荷特性分析和改进方法的有效性。结果表明,滤波能提取有效负荷响应,改善类噪声负荷辨识效果。

     

    Abstract: The load time variability constrains the development of the load modeling, but ambient signal based load modeling provides a new solution to overcome this problem. However, the load random fluctuation cannot be ignored in the ambient signal environment and leads to the difference between measured data and the typical load model, which also affects the results of ambient signal-based load identification. Therefore, the impacts of internal disturbances in different frequency bands on load response properties under ambient signal excitation are analyzed in this paper to explain the reasons of differences between measured data and the typical load model. Then, an identification method considered filtering is proposed. Finally, the load properties analysis and improved method are verified based on simulation, the results indicate that the filtering can extract effective load responses and improve effect of ambient signal-based load modeling.

     

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