摘要
针对特高压直流输电工程试运行期间得到的变电站中性点直流监测数据,提出了数据有效性评估方法:根据变电站中性点直流分布机理,分析了影响因素与变电站偏磁电流分布之间的关联特征,构建了基于神经网络(Artificial Neural Network,ANN)的中性点直流预测模型,并与实测数据进行对比,验证预测模型的有效性;利用蒙特卡洛模拟(Monte Carlo,MC)法对各影响因素进行抽样,模拟各种影响因素不确定组合,作为输入送入已经训练好的神经网络预测模型,构建MC-ANN联合模型,获取影响因素抽样下的大量样本数据,利用数据挖掘技术提取影响因素与中性点直流之间的关系变化规律;针对±800 kV上海庙-山东临沂特高压输电工程测试数据进行有效性评估,结果表明采用文中方法能够快速查找到测试期间中性点发生变化的站点。该方法可为甄别异常监测数据、快速查找异常原因以及锁定接地方式变化站点提供理论参考。
Aiming at the neutral point DC monitoring data of the substation obtained during the trial operation of UHV DC transmission project,the data validity evaluation method is proposed in this paper. Firstly,according to the neutral point DC distribution mechanism of the substation,the influencing factors and the substation bias current distribution are analyzed. Based on the correlation feature,a neutral point DC prediction model based on artificial neural network (ANN) is constructed and compared with the measured data to verify the validity of the prediction model. Then,Monte Carlo (MC)simulation is utilized to sample the influencing factors,simulate the uncertain combination of various influencing factors,input the trained neural network prediction model as input,construct the MC-ANN joint model,obtain a large number of sample data under the influencing factors,and the relationship between the influencing factors of mining technology extraction and the neutral point DC is studied through the data mining technology. Finally,the validity of test data of ± 800 kV Shanghai Temple-Shandong Linyi UHV transmission project is evaluated. The results demonstrate that the method can quickly find the site where the neutral point has changed during test period. This method can provide a theoretical reference for screening abnormal monitoring data,quickly find the cause of the anomaly,and lock the grounding change site.
作者
孙冰
何常根
孙谊媊
王开科
赵普志
吴伟丽
Sun Bing;He Changgen;Sun Yiqian;Wang Kaike;Zhao Puzhi;Wu Weili(Electric Power Science Research Institute of State Grid Xinjiang Electric Power Co.,Ltd.,Urumqi 830000,China;State Grid Xinjiang Electric Power Co.,Ltd.,Urumqi 830000,China;School of Electrical and Control Engineering,Xi'an University of Science and Technology,Xi’an 710054,China)
出处
《电测与仪表》
北大核心
2020年第15期88-94,共7页
Electrical Measurement & Instrumentation
基金
国网新疆电力公司电力科学研究院直流偏磁资助项目(5230DK17000K)
新疆维吾尔自治区科技厅面上基金(2017D01C417)。
关键词
中性点直流
监测数据
影响因素
神经网络
蒙特卡洛模拟
neutral point DC
monitoring data
influence factors
neural network
Monte Carlo simulation
