基于人工神经网络的线损计算及窃电分析
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摘要
在电力工业快速发展和电力市场化改革如火如荼进行的背景下,电力企业对于电力经济性的要求越来越高,网损尤其是配电网电力损耗成为了亟待解决的难题。以机器学习为切入点,通过数据驱动的方式,基于不同潮流对线损结果的差异影响,利用神经网络构建相关线损模型,实现了线损理论计算以及窃电位置判断。相关算例证明了神经网络模型基础下线损计算和窃电分析的准确性和可靠性。
Due to the rapid development of the power industry and the reform of the power market,power companies put higher requirement on power economy. Network losses reduction,especially in the distribution network,has become an urgent problem that need to be solved. Using machine learning as an entry point and through data-driven method,based on different impact on line loss result of different flow,line loss model is built with artificial neural network( ANN),which realizes the theoretical calculation of line loss and electricity theft judgment. Through the relevant cases,the accuracy and reliability of line loss prediction and electricity theft analysis based on ANN model are proved.
Due to the rapid development of the power industry and the reform of the power market,power companies put higher requirement on power economy. Network losses reduction,especially in the distribution network,has become an urgent problem that need to be solved. Using machine learning as an entry point and through data-driven method,based on different impact on line loss result of different flow,line loss model is built with artificial neural network( ANN),which realizes the theoretical calculation of line loss and electricity theft judgment. Through the relevant cases,the accuracy and reliability of line loss prediction and electricity theft analysis based on ANN model are proved.
引文
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[20]黄豪彩,黄宜坚,杨冠鲁.基于LM算法的神经网络系统辨识[J].组合机床与自动化加工技术,2003(2):6-8.HUANG Haocai,HUANG Yijian,YANG Guanlu. Neural network system identification based on levenberg-marquardt algorithm[J]. Modular Machine Tool&Automatic Manufacturing Technique,2003(2):6-8.
[2]张小玲.浅淡低压台区线损管理[J].电子技术应用,2014(s1):75-76.ZHANG Xiaoling. Discussion of power losses management low voltage[J]. Application of Electronic Technique,2014(s1):75-76.
[3]李滨,罗发,黄柳军,等.地市级电网多维度线损管理对标评价体系构建[J].电力系统及其自动化学报,2018,30(6):23-30.LI Bing,LUO Fa,HUANG Liujun,et al. Construction of multi-dimensional benchmarking evaluation system for line loss management of the power grid of city-level[J]. Proceedings of the CSU-EPSA,2018,30(6):23-30.
[4]王全兴,李思韬.基于采集系统的反窃电技术分析及防范措施[J].电测与仪表,2016,53(7):78-83.WANG Quanxing,LI Sitao. Technology analysis and preventive measures of electric larceny prevention technology based on electric energy data acquisition system[J]. Electrical Measurement&In-strumentation,2016,53(7):78-83.
[5]陈文瑛,陈雁,邱林,等.应用大数据技术的反窃电分析[J].电子测量与仪器学报,2016,30(10):1558-1567.CHEN Wenying,CHEN Yan,QIU Lin,et al. Analysis of antistealing electric power based on big data technology[J]. Journal of Electronic Measurement and Instrumentation,2016,30(10):1558-1567.
[6]张长水,杨强.机器学习及其应用[M].北京:清华大学出版社,2013.
[7]陈凯,朱钰.机器学习及其相关算法综述[J].统计与信息论坛,2007,22(5):105-112.CHEN Kai,ZHU Yu. A summary of machine learning and related algorithms[J]. Statistics&Information Forum,2007,22(5):105-112.
[8]徐学良.人工神经网络的发展及现状[J].微电子学,2017,47(2):239-242.XU Xueliang. The development and status of artificial neural network[J]. Microelectronics,2017,47(2):239-242.
[9]周飞燕,金林鹏,董军.卷积神经网络研究综述[J].计算机学报,2017,40(6):1229-1251.ZHOU Feiyan,JIN Linpeng,DONG Jun. Review of convolutional neural network[J]. Chinese Journal of Computers,2017,40(6):1229-1251.
[10]梁天新,杨小平,王良,等.记忆神经网络的研究与发展[J].软件学报,2017,28(11):2905-2924.LIANG Tianxin, YANG Xiaoping, WANG Liang, et al.Review on research and development of memory neural networks[J]. Journal of Software,2017,28(11):2905-2924.
[11]张明,张树群,雷兆宜.改进的萤火虫算法在神经网络中的应用[J].计算机工程与应用,2017,53(5):159-163.ZHANG Ming,ZHANG Shuqun,LEI Zhaoyi. Application of improved firefly algorithm in neural network[J]. Computer Engineering and Applications,2017,53(5):159-163.
[12]郭欣,王蕾,宣伯凯,等.基于有监督Kohonen神经网络的步态识别[J].自动化学报,2017,43(3):430-438.GUO Xin,WANG Lei,XUE Boka,et al. Gait recognition based on supervised Kohonen neural network[J]. Acta Automatica Sinica,2017,43(3):430-438.
[13] WAN C,XU Z,PINSON P,et al. Optimal prediction intervals of wind power generation[J]. IEEE Transactions on Power Systems,2014,29(3):1166-1174.
[14] WAN C,NIU M,SONG Y,et al. Pareto optimal prediction intervals of electricity price[J]. IEEE Transactions on Power Systems,2017,32(1):817-819.
[15]张勤,周步祥,林楠,等.基于灰色模型与神经网络组合的线损率预测[J].电力系统及其自动化学报,2013,25(5):162-166.ZHANG Qin,ZHOU Buxiang,LIN Nan,et al. Line loss rate forecasting based on combination of grey model and neural network[J]. Proceedings of the CSU-EPSA,2013,25(5):162-166.
[16]张方舟,郝庆辉,周勃,等.遗传算法的RBF神经网络在线损计算中的应用[J].计算机技术与发展,2014(6):192-195.ZHANG Fangzhou,HAO Qinghui,ZHOU Bo,et al. Application of RBF neural network of genetic algorithm in calculation of line losses[J]. Computer Technology and Development,2014(6):192-195.
[17]李亚,刘丽平,李柏青,等.基于改进k-Means聚类和BP神经网络的台区线损率计算方法[J].中国电机工程学报,2016,36(17):4543-4551.LI Ya,LIU Liping,LI Boqing,et al. Calculation of line loss rate in transformer district based on improved k-means clustering algorithm[J]. Computer Technology and Development,2016,36(17):4543-4551.
[18]曹峥,杨镜非,刘晓娜. BP神经网络在反窃电系统中的研究与应用[J].水电能源科学,2011,29(9):199-202.CAO Zheng,YANG Jingfei,LIU Xiaona. Study and application of preventing system from stealing Power based on bp neural network[J]. Water Resources and Power,2011,29(9):199-202.
[19]王庆宁,张东辉,孙香德,等.基于GA-BP神经网络的反窃电系统研究与应用[J].电测与仪表,2018,55(11):35-40.WANG Qingning,ZHANG Donghui,SUN Xiangde,et al. Research and application of electricity anti-stealing system based on GA-BP neural network[J]. Electrical Measurement&Instrumentation,2018,55(11):35-40.
[20]黄豪彩,黄宜坚,杨冠鲁.基于LM算法的神经网络系统辨识[J].组合机床与自动化加工技术,2003(2):6-8.HUANG Haocai,HUANG Yijian,YANG Guanlu. Neural network system identification based on levenberg-marquardt algorithm[J]. Modular Machine Tool&Automatic Manufacturing Technique,2003(2):6-8.