周涛,姚嘉辰,倪俊,徐超,徐妍
ZHOU Tao,YAO Jiachen,NI Jun,XU Chao,XU Yan

• 1:南京理工大学自动化学院
• 2:国网无锡供电公司
• 3:江苏方天电力技术有限公司

摘要(Abstract)：

传统SIC（逐步惯性控制）与电网频率解耦，限制了风电机组对系统频率的实时感知能力，可能引发严重的SFD（频率二次跌落）问题。为此，提出一种基于改进模糊自适应控制的风电机组逐步惯性最优控制策略。首先，通过模糊自适应控制建立了频率状态量、转速变化量与控制策略之间的联系，使风电机组能够根据系统频率波动和自身转速状态动态调整输出功率。接着，引入了PSO（粒子群优化）算法对模糊自适应控制的关键参数进行优化，以提升控制性能。进一步，利用DNN（深度神经网络）的数据处理与在线决策能力，实现对关键参数的特征提取和学习。仿真结果表明，所提策略能够有效避免SFD，平滑风电机组的状态切换轨迹，实现最优频率轨迹，在不同场景下均表现出良好的适应性。
Conventional stepwise inertial control(SIC) is decoupled from grid frequency, which limits the real-time frequency perception of wind turbines and may lead to severe secondary frequency drop(SFD). To address this issue, this paper proposes an optimal stepwise inertial control strategy for wind turbines based on improved fuzzy adaptive control. First, fuzzy adaptive control is used to establish the relationship between frequency states, rotor speed variations, and control strategies, enabling wind turbines to dynamically adjust output power according to system frequency fluctuations and their rotor speed conditions. Then, particle swarm optimization(PSO) is applied to optimize key parameters of the fuzzy adaptive control, thereby enhancing control performance. Furthermore, a deep neural network(DNN) is leveraged for feature extraction and learning of key parameters, taking advantage of its data processing and online decision-making capabilities. Simulation results demonstrate that the proposed strategy effectively prevents SFD, smooths the state transition of wind turbines, and achieves the optimal frequency trajectory, showing good adaptability under different scenarios.

关键词(KeyWords)： 新型电力系统;逐步惯性控制;频率二次跌落;模糊自适应控制;深度神经网络;最优频率轨迹
modern power system;SIC;SFD;fuzzy adaptive control;DNN;optimal frequency trajectory

Abstract:

Keywords:

基金项目(Foundation): 江苏省自然科学基金青年项目（BK20241481）;; 中央高校基本科研业务费专项资金（30922010709）

作者(Author): 周涛,姚嘉辰,倪俊,徐超,徐妍
ZHOU Tao,YAO Jiachen,NI Jun,XU Chao,XU Yan

DOI: 10.19585/j.zjdl.202511004

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