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| 引用本文: | 廖峥,熊小伏,李新,等.基于BP神经网络的输电线路舞动预警方法[J].电力系统保护与控制,2017,45(19):154-161.[点击复制] |
| LIAO Zheng, XIONG Xiaofu, LI Xin, WANG Jian, LI Zhe and LIU Shanfeng.An early warning method of transmission line galloping based on BP neural network[J].Power System Protection and Control,2017,45(19):154-161[点击复制] |
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| 基于BP神经网络的输电线路舞动预警方法 |
| 廖峥,熊小伏,李新,王建,李哲,刘善峰 |
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| (输配电装备及系统安全与新技术国家重点实验室重庆大学，重庆 400044 ;国网重庆市电力公司电力科学研究院，重庆 401123;国网河南省电力公司电力科学研究院，河南 郑州 450052) |
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| 摘要: |
| 为了保证在覆冰舞动环境下输电线路的正常运维，将输电线舞动预警问题归结为有监督机器学习方法下的分类预测问题，提出了一种基于BP神经网络的舞动预警方法。通过分析影响舞动的外界气象因素，构建了以风速、风向与线路的夹角、相对湿度以及温度为输入特征量的BP神经网络学习算法，判断是否达到易舞气象条件预测输电线的舞动情况，并采用评价指标评估其预警性能，以便进行模型改进。采用河南电网舞动相关历史数据进行算例分析，验证了所提方法的有效性和实用性。输出的预警结果可为电网运维人员合理制定调度决策提供支持，保证电网安全迎峰度冬。 |
| 关键词: 架空输电线 舞动 预警 机器学习方法 BP神经网络 |
| DOI：10.7667/PSPC161612 |
| 投稿时间：2016-09-26修订日期：2016-12-19 |
| 基金项目:国家电网公司重大基础前瞻科技项目(SG20141187) |
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| An early warning method of transmission line galloping based on BP neural network |
| LIAO Zheng,XIONG Xiaofu,LI Xin,WANG Jian,LI Zhe,LIU Shanfeng |
| (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China;Electric Power Research Institute of State Grid Chongqing Electric Power Company, Chongqing 401123, China;Electric Power Research Institute of State Grid Henan Electric Power Company, Zhengzhou 450052, China) |
| Abstract: |
| In order to guarantee the normal operation and maintenance of the transmission line under the icing and galloping conditions, a novel early warning method of transmission line galloping based on BP neural network is proposed through treating it as a problem of classification and prediction under supervised machine learning. Through analyzing the external meteorological factors that influencing galloping, a BP neural network learning algorithm is established by taking wind, inducted angle of wind direction and line, relative humidity, and ambient temperature as input vectors. The galloping probability is predicted by judging whether the prone-galloping weather conditions are satisfied utilizing the proposed method, and its prediction performance is assessed through several test indexes with the purpose of improvement. A case study is presented by adopting historical galloping data of Henan power grid, and the result shows that the proposed method is effective and practical, which can provide support for power system operation staffs to make reasonable decisions as well as ensure the power grid securely tiding over the peak-load during winter. This work is supported by Major and Basic Project of State Grid Foresight Science and Technology (No. SG20141187). |
| Key words: overhead transmission line galloping early warning machine learning BP neural network |
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