国网新疆电力有限公司电力科学研究院,新疆,乌鲁木齐,830011
网络出版:2025-11-11,
纸质出版:2025-11-11
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祁晓笑, 王利超, 孙冰, 邓小宇, 郭启斌. 宽运行工况下基于增秩条件构造的构网型储能迭代求解参数辨识方法[J]. 湖南电力, 2025, 45(5): 33-40.
QI Xiaoxiao, WANG Lichao, SUN Bing, et al. A Rank-Increasing Iterative Parameter Identification Method for Grid-Forming Energy Storage Under Wide Operation Conditions[J]. 2025, 45(5): 33-40.
祁晓笑, 王利超, 孙冰, 邓小宇, 郭启斌. 宽运行工况下基于增秩条件构造的构网型储能迭代求解参数辨识方法[J]. 湖南电力, 2025, 45(5): 33-40. DOI: 10.3969/j.issn.1008-0198.2025.05.005.
QI Xiaoxiao, WANG Lichao, SUN Bing, et al. A Rank-Increasing Iterative Parameter Identification Method for Grid-Forming Energy Storage Under Wide Operation Conditions[J]. 2025, 45(5): 33-40. DOI: 10.3969/j.issn.1008-0198.2025.05.005.
针对现有时域和频域辨识方法均存在宽运行工况下计算结果波动性加剧、准确性降低的问题
提出基于增秩条件构造的二次型迭代求解参数辨识方法
提高宽运行工况和复杂电网条件下的参数辨识能力。首先
针对现有时域辨识方法对电网运行工况变化适应性不足的问题
将构网储能变流器与电网互动特性进行数学抽象
构建单机和多机形态的闭环辨识模型。其次
针对现有频域辨识方法在迭代计算过程中容易陷入局部最优的问题
提出基于状态方程增秩的二次型迭代求解方法
通过施加增秩条件改善全局寻优性能
提高宽运行工况下辨识结果的一致性。最后
通过仿真验证闭环辨识模型的有效性
证明所提方法能够在不同运行工况和电网场景下得到准确性和一致性更优的辨识结果。
Aiming at the problems of increased volatility and reduced accuracy of calculation results under wide operating conditions in both existing time and frequency domain identification methods
a quadratic iterative parameter identification method based on the construction of increasing rank condition is proposed to improve the parameter identification ability under wide operating conditions and variable power grid conditions. Firstly
in view of the problem of insufficient adaptability of the existing time-domain methods to the change of power grid operation conditions
the interaction characteristics of the grid-forming energy storage converter and the power grid are mathematically abstracted
and a closed-loop identification model is constructed for single-machine and two-machine/multiple-machine forms. Secondly
to address the problem that the existing frequency domain methods tend to fall into local optimization during iterative computation
a quadratic iterative solving method based on increasing the rank of state equations is proposed
which improves the global optimization performance by applying the increasing-rank condition
and improves the consistency of the identification results under a wide range of operating conditions. Finally
the effectiveness of the closed-loop identification model is verified by simulation. Resultsshow that the proposed method can obtain more accurate identification results under variable operating conditions and power grid scenarios.
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