| 引用本文: | 张诗熠,王怀远,李 剑,卢国强.基于失稳模式自适应捕捉的暂态稳定评估方法[J].电力系统保护与控制,2024,52(18):35-44.[点击复制] |
| ZHANG Shiyi,WANG Huaiyuan,LI Jian,LU Guoqiang.Transient stability assessment method based on adaptive capture of instability patterns[J].Power System Protection and Control,2024,52(18):35-44[点击复制] |
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| 摘要: |
| 在电力系统暂态稳定评估(transient stability assessment, TSA)问题中，普通机器学习算法数据挖掘能力的有限性阻碍了TSA模型评估精度的进一步提高。针对此问题，以电力系统数据的不同失稳模式为切入点，提出了基于失稳模式自适应捕捉的TSA方法，构建了由多个子评估模型和一个失稳模式判别模型组成的自适应判稳组合模型。首先，根据失稳模式的不同对原始数据集进行分类，分别训练多个针对不同失稳模式的子评估模型。然后，利用失稳模式判别模型输出的权重值对子评估模型进行集成，自适应完成对输入数据失稳模式的捕捉。最后，在IEEE39节点系统和华东电网系统中进行测试验证。仿真结果表明，所提方法在降低不稳定样本漏报率的同时进一步提高了模型评估精度，验证了该方法的有效性。 |
| 关键词: 深度学习 暂态稳定评估 失稳模式 集成学习 自适应评估 |
| DOI：10.19783/j.cnki.pspc.240132 |
| 投稿时间：2024-01-28修订日期：2024-06-04 |
| 基金项目:福建省自然科学基金项目资助(2022J01113)；国网青海省电力公司科技项目资助(522800230001) |
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| Transient stability assessment method based on adaptive capture of instability patterns |
| ZHANG Shiyi1,WANG Huaiyuan1,LI Jian2,LU Guoqiang2 |
| (1. Key Laboratory of New Energy Generation and Power Conversion (Fuzhou University), Fuzhou 350108, China;
2. State Grid Qinghai Electric Power Company, Xining 810001, China) |
| Abstract: |
| In the transient stability assessment (TSA) problem of a power system, the limited data mining ability of common machine learning algorithms prevents further improvement of TSA model evaluation accuracy. To solve this problem, taking different instability patterns of power system data as the entry point, a TSA method based on adaptive capture of instability patterns is proposed. An adaptive stability judgment combination model is constructed, one which is composed of multiple sub-evaluation models and an instability pattern discrimination model. First, the original data set is classified according to the different instability patterns, and several sub-evaluation models are trained for different instability patterns. Then, the weight value of the instability pattern discrimination model output is used to integrate the sub-evaluation model, and the instability pattern of the input data is captured adaptively. Finally, a case study is performed on the IEEE39-bus system and the East China power grid system. Simulation results show that the proposed method can reduce the negative rate of unstable samples and further improve model evaluation accuracy, which verifies the effectiveness of the proposed method. |
| Key words: deep learning transient stability assessment instability pattern ensemble learning adaptive evaluation |
