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基于Powershap特征选择的电力系统暂态稳定评估

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为进一步提高暂态稳定评估(transient stability assessment,TSA)的精准度和可靠性,提出一种基于统计学与Shapley值结合的特征选择方法(Powershap),并建立电力系统TSA模型.首先,根据电力系统运行时的稳态分量构建输入特征集,采用Powershap将数据集分为多个数据子集进行训练,筛选出关键特征集;其次,利用关键特征集训练多个CatBoost模型并进行TSA,生成TSA模型;最后,在新英格兰 10 机 39 节点系统和加入新能源发电的新英格兰 54 机 118 节点系统上进行仿真实验,并给出评估结果.实验得出:在新英格兰10 机39节点系统中采用基于Powershap特征选择的方法进行分类,其准确率能够达到99.79%;在改进的新英格兰54 机118节点系统上,其准确率能够达到99.49%,说明该方法能够有效进行电力系统暂态稳定评估,并且验证了所提TSA模型具有较好的鲁棒性与泛化能力.
Power system transient stability assessment based on Powershap feature selection
To further improve the accuracy and reliability of transient stability assessment(TSA),a feature selection method(Powershap)based on the combination of statistics and Shapley values is proposed,and a power system transient stability assessment model is established.Firstly,the input feature set is constructed based on the steady-state components during the operation of the power system.Powershap is used to divide the dataset into multiple subsets for training,and key feature sets are selected.Then,multiple CatBoost models are trained using key feature sets and transient stability assessments are conduct to generate transient stability assessment models.Finally,simulation experiments are conducted on the New England 10-machine 39-node system and the New England 54-machine 118-node system with the addition of new energy generation,and evaluation results are provided.The experiments show that,in the 10-machine 39-node system in New England,using the Powershap feature selection method for classification can achieve an accuracy of 99.79%.On the improved New England 54-machine 118-node system,its accuracy can reach 99.49%,indicating that the method can effectively perform transient stability assessment of power systems.It is verified that the proposed TSA model has good robustness and generalization ability.

power systemtransient stability assessmentfeature selectionPowershapCatBoost

陈超、余成波、左立昕

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重庆理工大学电气与电子工程学院,重庆 400054

重庆市能源互联网工程技术研究中心,重庆 400054

电力系统 暂态稳定评估 特征选择 Powershap CatBoost

重庆市自然科学基金创新发展联合基金重庆市教委科研基金

2023CCZ0822023CYJH009

2024

热力发电
西安热工研究院有限公司,中国电机工程学会

热力发电

CSTPCD北大核心
影响因子:0.765
ISSN:1002-3364
年,卷(期):2024.53(8)