摘要
针对新能源大规模接入电网,直流输电系统换相失败随机性更强、影响因素更复杂,导致换相失败预测难度加大的问题,提出一种基于深度信念网络的直流输电线路换相失败预测技术。首先,分析换相失败特性,提取出逆变侧交流电压、直流电流、触发角故障数据;其次,利用深度信念网络能够少样本无监督特征学习高维数据的优势,将故障数据进行归一化处理作为深度信念网络的输入数据,构建直流输电线路换相失败预测模型;最后,经过Softmax分类器输出换相失败标签,实现换相失败预测。搭建PSCAD/EMTDC直流输电模型进行验证,实验结果表明,所提方法对换相失败预测准确率较高,相比于常见的卷积神经网络、极限学习机,分别提升了10.6、8.5百分点,验证了该文方法的有效性。
In response to the large-scale integration of new energy into the power grid,the randomness of commutation failure in DC transmission systems becomes stronger,and the influencing factors become more complex,leading to increased difficulty in predicting commutation failure.A DC transmission line commutation failure prediction technology based on deep belief networks is proposed.Firstly,the characteristics of commutation failure are analyzed,and the AC voltage,DC current,and trigger angle fault data on the inverter side are extracted.Secondly,by utilizing the advantage of deep belief networks in learning high-dimensional data with few unsupervised features,the fault data is normalized as input data for deep belief networks,and a DC transmission line commutation failure prediction model is constructed.Finally,the Softmax classifier outputs a commutation failure label to achieve commutation failure prediction.A PSCAD/EMTDC DC transmission model is built for verification,and the experimental results show that the proposed method has high accuracy in predicting commutation failure.Compared with common convolutional neural networks and extreme learning machines,the proposed method has improved by 10.6%and 8.5%,respectively,verifying its effectiveness.
作者
毛玉宾
于琳琳
丁骁
孟高军
陈姝彧
MAO Yubin;YU Linlin;DING Xiao;MENG Gaojun;CHEN Shuyu(State Grid Henan Electric Power Economic and Technological Research Institute,Zhengzhou 450000,China;School of Electric Power Engineering,Nanjing Institute of Technology,Nanjing 211167,China;State Key Laboratory of Advanced Power Transmission Technology(State Grid Smart Grid Research Institute Co.,Ltd.),Beijing 102200,China)
出处
《中国测试》
北大核心
2025年第8期155-162,共8页
China Measurement & Test
基金
江苏省重点研发计划(BE2021094)。
作者简介
毛玉宾(1972-),男,高级工程师,硕士,研究方向为电力系统规划与优化运行。
