| 基于XGBoost和ASPSO的电力系统暂态稳定预防控制方法 |
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| 引用本文：刘颂凯1，2,袁铭洋1，2,杨超1，2,阮肇华3,张磊1，2,刘旭4.基于XGBoost和ASPSO的电力系统暂态稳定预防控制方法[J].电网与清洁能源,2023,39(10):9~18 |
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| 基金项目:湖北省自然科学基金（2022CFB825）；国家自然科学基金项目（62233006，52007103）；电力系统智能运行与安全防御宜昌市重点实验室（三峡大学）开放基金（2020DLXY06）；梯级水电站运行与控制湖北省重点实验室（三峡大学）开放基金（2019KJX11）。 |
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| 中文摘要:该文提出了一种极限梯度提升（extreme gradient boosting，XGBoost）和基于蚁群算法的自适应参数粒子群优化算法（particle swarm optimization algorithm of self-adaptive parameter based on ant colony algorithm，ASPSO）相结合的方法，以实现电力系统暂态稳定预防控制。首先，通过XGBoost模型快速学习系统运行特征与暂态稳定性间的映射关系，给出特征重要性排序，提供一定的模型可解释性。其次，将训练好的XGBoost嵌入暂态稳定约束最优潮流模型作为暂态稳定约束。之后，利用ASPSO算法迭代求解，保证系统暂态稳定的同时，考虑发电机有功出力调整最小化，制定相应预防控制策略。最后，在PSS/E提供的IEEE 39节点系统中进行算例仿真，证明了所提方法的有效性。 |
| 中文关键词:暂态稳定 预防控制 XGBoost ASPSO |
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| A Transient Stability Preventive Control Method of Power Systems Based on XGBoost and ASPSO |
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| Abstract:This paper proposes a method which combines extreme gradient boosting （XGBoost） with particle swarm optimization algorithm of self-adaptive parameter based on ant colony algorithm （ASPSO） to achieve the transient stability preventive control of power systems. First， the mapping relationship between system operation features and transient stability is quickly learned by the XGBoost model， and the importance ranking of features is given to provide certain model interpretability. Second， the trained XGBoost is embedded into the transient stability constrained optimal power flow model as transient stability constraints. Furthermore， the ASPSO algorithm is used for iterative solution to ensure the transient stability of the system while considering the minimization of the generator active output adjustment， and formulate the corresponding preventive control strategy. Finally， a case study on IEEE 39-bus system provided by PSS/E is performed to demonstrate the effectiveness of the proposed method. |
| keywords:transient stability preventive control XGBoost ASPSO |
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