| 基于深度学习的多输入特征融合的暂态电压稳定性评估方法 |
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| 作者姓名： | 潘晓杰 徐友平 朱成亮 罗红梅 黄杰明 商小峰 |
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| 作者单位： | 国家电网公司华中分部公司;国网浙江省电力有限公司嘉兴供电公司;中国电力科学研究院有限公司;国网浙江桐庐县供电有限公司 |
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| 基金项目： | 国家自然科学基金项目(51777142&51907096);青海省自然科学基金项目(2019-ZJ-950Q)。 |
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| 摘 要： | 暂态电压稳定性评估是电力系统稳定性评估中的难点和重点。提出一种基于深度学习、考虑多输入特征集的暂态电压稳定性评估方法,首先建立包含故障前、故障发生时刻、故障切除时刻的多输入故障集;然后基于深度学习建立卷积神经网络并离线训练PMU数据,最终达到快速准确评估暂态电压稳定性的目的。仿真结果表明,提出的评估方法与现有的神经网络、最小二乘支持向量机方法相比,在准确率、评估速度2方面有着较大提升。
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| 关 键 词： | 特征集 卷积神经网络 稳定性评估 |
Transient Voltage Stability Evaluation Method Based on Multi-Input Feature Fusion of Deep Learning |
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| Authors: | PAN Xiaojie XU Youping ZHU Chengliang LUO Hongmei HUANG Jieming SHANG Xiaofeng |
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| Affiliation: | (Central China Branch of State Grid Corporation of China,Wuhan 430077,Hubei,China;Jiaxing Power Supply Company,State Grid Zhejiang Electric Power Co.,Ltd.,Jiaxing 314000,Zhejiang,China;China Electric Power Research Institute,Beijing 100192,China;State Grid Tonglu Power Supply Company,Tonglu 311500,Zhejiang,China) |
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| Abstract: | The stability assessment of transient voltage is the emphasis in the stability assessment of the power system,and it is also difficult.This paper proposes a method based on deep learning to consider transient voltage stability evaluation considering multiple input feature sets.First,a multi-input fault set including pre-fault,fault occurrence time and fault resection time is established.Second,the convolutional neural network based on deep learning is used to train PMU data offline,so as to achieve fast and accurate evaluation of transient voltage stability.The simulation results show that the proposed evaluation method,compared with the existing neural network and least squares support vector machine method,has improved both accuracy and evaluation speed. |
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| Keywords: | feature set convolutional neural network stability evaluation |
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