| 引用本文: | 刘 伟,胡歆岳,路敬祎.基于改进CNNA的含风电电力系统暂态稳定评估[J].电力系统保护与控制,2026,54(03):156-166.[点击复制] |
| LIU Wei,HU Xinyue,LU Jingyi.Transient stability assessment of power systems with wind turbines based on an improved CNNA model[J].Power System Protection and Control,2026,54(03):156-166[点击复制] |
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| 摘要: |
| 随着风电渗透率的不断提高，电力系统的功率波动性和不确定性显著加剧，传统暂态稳定评估方法在精度和效率方面均面临严峻挑战。为此提出一种改进一维注意力卷积神经网络(CNN-Attention, CNNA)的暂态稳定评估模型。首先，充分发挥CNNA的特征提取与重点时序捕捉能力，引入残差块以改善模型的梯度消失问题，并结合维纳滤波技术抑制样本噪声干扰。其次，基于功角稳定与电压稳定联合判据设计暂态稳定评估流程，并建立相应的评价指标体系。最后，通过PSASP软件仿真实验对IEEE39和IEEE118含风力发电机组节点系统进行了算例验证。结果表明所提方法在不同噪声、风机工况下均保持较高的准确率，能有效降低“误判稳定”风险，对失稳状态具备较高的识别能力。 |
| 关键词: 含风电电力系统 暂态稳定评估 残差块 维纳滤波 |
| DOI：10.19783/j.cnki.pspc.256114 |
| 投稿时间：2025-03-01修订日期：2025-09-07 |
| 基金项目:国家自然科学基金项目资助(62473096) |
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| Transient stability assessment of power systems with wind turbines based on an improved CNNA model |
| LIU Wei1,2,HU Xinyue1,LU Jingyi2 |
| (1. School of Electrical and Information Engineering, Northeast Petroleum University, Daqing 163318, China;
2. NEPU Sanya Offshore Oil & Gas Research Insititute, Sanya 572000, China) |
| Abstract: |
| With the continuous increase in wind power penetration, the power fluctuation and uncertainty of power systems have significantly intensified, posing severe challenges to traditional transient stability assessment methods in terms of both accuracy and efficiency. To address this issue, a transient stability assessment model based on an improved one-dimensional convolutional neural network with attention (CNN-Attention, CNNA) is proposed. First, by fully exploiting the feature extraction capability and key temporal sequence focusing ability of the CNNA, residual blocks are introduced to alleviate the gradient vanishing problem, and Wiener filtering is incorporated to suppress noise interference in the samples. Second, a transient stability assessment framework is designed based on a combined criterion of rotor angle stability and voltage stability, and a corresponding evaluation index system is established. Finally, simulation studies are carried out using PSASP software on the IEEE 39-bus and IEEE 118-bus systems with wind turbine generator. The results show that the proposed method maintains high accuracy under different noise levels and wind turbine operating conditions, effectively reduces the risk of “false stable” misjudgment, and exhibits strong capability in identifying unstable states. |
| Key words: power systems with wind power transient stability assessment residual block Wiener filtering |
