
浏览全部资源
扫码关注微信
中国大唐集团科学技术研究院有限公司华东电力试验研究院,安徽省合肥市 230031
徐搏超(1992—),男,硕士,工程师,主要研究方向:清洁能源发电、大数据技术。E-mail:ncepu_xbc@163.com
收稿日期:2019-12-29,
修回日期:2020-06-26,
网络出版日期:2020-08-24,
纸质出版日期:2020-10-25
移动端阅览
徐搏超.基于参数关联性的电站参数异常点清洗方法[J].电力系统自动化,2020,44(20):142-147.
XU Bochao.Parameter Correlation Based Parameter Abnormal Point Cleaning Method for Power Station[J].Automation of Electric Power Systems,2020,44(20):142-147.
徐搏超.基于参数关联性的电站参数异常点清洗方法[J].电力系统自动化,2020,44(20):142-147. DOI: 10.7500/AEPS20191229003.
XU Bochao.Parameter Correlation Based Parameter Abnormal Point Cleaning Method for Power Station[J].Automation of Electric Power Systems,2020,44(20):142-147. DOI: 10.7500/AEPS20191229003.
针对电站参数虚假数据和异常状态点的区分问题,提出了一种将关联规则、基于密度模式的空间数据聚类(DBSCAN)算法和改进高斯核相关向量机(RVM)相结合的清洗方法。首先,引入关联规则分析参数间的关联性,找出强关联参数组合;然后,利用DBSCAN算法初步检测异常点,给出了结合关联参数的清洗流程,区分了虚假数据和系统异常状态点;最后,使用RVM清洗虚假数据,并通过改进高斯核空间样本点形式降低时间成本。案例结果表明,基于参数关联性的清洗方法能有效提高清洗的准确性和时效性。
Aiming at distinguishing false data and abnormal state points in power plant parameters
a cleaning method based on association rule
density-based spatial clustering of applications with noise (DBSCAN) algorithm and improved Gauss kernel relevance vector machine (RVM) is proposed. Firstly
association rules are introduced to analyze the association among parameters and find out the combination of parameters with strong association. Secondly
the DBSCAN algorithm is used to detect the abnormal point preliminarily
and the cleaning procedure combined with the associated parameters is proposed to distinguish the false data and the system abnormal state points. Finally
RVM is used to clean the false data
and the time cost is reduced by improving the Gaussian kernel space sample point form. Test results show that the cleaning method based on parameter correlation can effectively improve the accuracy and timeliness of cleaning.
刘炳含 , 付忠广 , 王鹏凯 , 等 . 大数据挖据技术在燃煤电站机组能耗分析中的应用研究 [J]. 中国电机工程学报 , 2018 , 38 ( 12 ): 3578 - 3587 .
LIU Binghan , FU Zhongguang , WANG Pengkai , et al . Big data mining technology application in energy consumption analysis of coal-fired power plant units [J]. Proceedings of the CSEE , 2018 , 38 ( 12 ): 3578 - 3587 .
冷喜武 , 陈国平 , 蒋宇 , 等 . 智能电网监控运行大数据应用模型构建方法 [J]. 电力系统自动化 , 2018 , 42 ( 20 ): 121 - 128 .
LENG Xiwu , CHEN Guoping , JIANG Yu , et al . Model construction method of big data application for monitoring and control of smart grid [J]. Automation of Electric Power Systems , 2018 , 42 ( 20 ): 121 - 128 .
胡阳 , 乔依林 . 基于置信等效边界模型的风功率数据清洗方法 [J]. 电力系统自动化 , 2018 , 42 ( 15 ): 18 - 23 .
HU Yang , QIAO Yilin . Wind power data cleaning method based on confidence equivalent boundary model [J]. Automation of Electric Power Systems , 2018 , 42 ( 15 ): 18 - 23 .
曲朝阳 , 张艺竞 , 王永文 , 等 . 基于Spark框架的能源互联网电力能源大数据清洗模型 [J]. 电测与仪表 , 2018 , 55 ( 2 ): 39 - 44 .
QU Zhaoyang , ZHANG Yijing , WANG Yongwen , et al . Big energy data cleaning model for energy internet based on Spark framework [J]. Electrical Measurement & Instrumentation , 2018 , 55 ( 2 ): 39 - 44 .
沈小军 , 付雪姣 , 周冲成 , 等 . 风电机组风速-功率异常运行数据特征及清洗方法 [J]. 电工技术学报 , 2018 , 33 ( 14 ): 3353 - 3361 .
SHEN Xiaojun , FU Xuejiao , ZHOU Chongcheng , et al . Characteristics of outliers in wind speed-power operation data of wind turbines and its cleaning method [J]. Transactions of China Electrotechnical Society , 2018 , 33 ( 14 ): 3353 - 3361 .
代杰杰 , 宋辉 , 杨祎 , 等 . 基于栈式降噪自编码器的输变电设备状态数据清洗方法 [J]. 电力系统自动化 , 2017 , 41 ( 12 ): 224 - 229 .
DAI Jiejie , SONG Hui , YANG Yi , et al . Cleaning method for status data of power transmission and transformation equipment based on stacked denoising autoencoders [J]. Automation of Electric Power Systems , 2017 , 41 ( 12 ): 224 - 229 .
莫文雄 , 许中 , 肖斐 , 等 . 基于随机矩阵理论的电力扰动事件时空关联 [J]. 高电压技术 , 2017 , 43 ( 7 ): 296 - 303 .
MO Wenxiong , XU Zhong , XIAO Fei , et al . Research on temporal-spatial correlation of power disturbance events based on random matrix theory [J]. High Voltage Engineering , 2017 , 43 ( 7 ): 296 - 303 .
谢龙君 , 李黎 , 程勇 , 等 . 融合集对分析和关联规则的变压器故障诊断方法 [J]. 中国电机工程学报 , 2015 , 35 ( 2 ): 277 - 286 .
XIE Longjun , LI Li , CHENG Yong , et al . A fault diagnosis method of power transformers by integrated set pair analysis and association rules [J]. Proceedings of the CSEE , 2015 , 35 ( 2 ): 277 - 286 .
田力 , 向敏 . 基于密度聚类技术的电力系统用电量异常分析算法 [J]. 电力系统自动化 , 2017 , 41 ( 5 ): 64 - 70 .
TIAN Li , XIANG Min . Abnormal power consumption analysis based on density-based spatial clustering of applications with noise in power systems [J]. Automation of Electric Power Systems , 2017 , 41 ( 5 ): 64 - 70 .
孙鹏 , 黄绪勇 , 耿苏杰 , 等 . 基于实时监测和例行试验数据的电力变压器状态动态评估方法 [J]. 电力自动化设备 , 2018 , 38 ( 3 ): 210 - 217 .
SUN Peng , HUANG Xuyong , GENG Sujie , et al . Dynamic assessment of power transformer status based on real-time monitoring and experimental data [J]. Electric Power Automation Equipment , 2018 , 38 ( 3 ): 210 - 217 .
王英博 , 马菁 , 柴佳佳 , 等 . 基于Hadoop平台的改进关联规则挖掘算法 [J]. 计算机工程 , 2016 , 42 ( 10 ): 69 - 74 .
WANG Yingbo , MA Jing , CHAI Jiajia , et al . Improved association rule mining algorithm based on Hadoop platform [J]. Computer Engineering , 2016 , 42 ( 10 ): 69 - 74 .
杨越文 , 侯慧娟 , 杨祎 , 等 . 基于贝叶斯模型的输电线路运行参量关联规则挖掘及预测方法 [J]. 电网技术 , 2017 , 41 ( 11 ): 260 - 266 .
YANG Yuewen , HOU Huijuan , YANG Yi , et al . Association rule mining and prediction method for transmission line operation parameters based on Bayesian model [J]. Power System Technology , 2017 , 41 ( 11 ): 260 - 266 .
王桂兰 , 周国亮 , 赵洪山 , 等 . 大规模用电数据流的快速聚类和异常检测技术 [J]. 电力系统自动化 , 2016 , 40 ( 24 ): 27 - 33 .
WANG Guilan , ZHOU Guoliang , ZHAO Hongshan , et al . Fast clustering and anomaly detection technique for large-scale power data stream [J]. Automation of Electric Power Systems , 2016 , 40 ( 24 ): 27 - 33 .
孙小磊 , 郑华 , 李晖 , 等 . 基于改进DBSCAN算法的变压器不良漏抗参数辨识 [J]. 电力系统自动化 , 2017 , 41 ( 9 ): 96 - 101 .
SUN Xiaolei , ZHENG Hua , LI Hui , et al . Bad data identification for leakage reactance parameters of transformer based on improved DBSCAN algorithm [J]. Automation of Electric Power Systems , 2017 , 41 ( 9 ): 96 - 101 .
李东 , 宋云亭 , 任大江 , 等 . 基于同调分群的解列配置和措施配合研究 [J]. 宁夏电力 , 2016 ( 4 ): 9 - 17 .
LI Dong , SONG Yunting , REN Dajiang , et al . Research on islanding configuration and coordination based on coherency identification [J]. Ningxia Electric Power , 2016 ( 4 ): 9 - 17 .
孙毅 , 李世豪 , 崔灿 , 等 . 基于高斯核函数改进的电力用户用电数据离群点检测方法 [J]. 电网技术 , 2018 , 42 ( 5 ): 1595 - 1604 .
SUN Yi , LI Shihao , CUI Can , et al . Improved outlier detection method of power consumer data based on Gaussian kernel function [J]. Power System Technology , 2018 , 42 ( 5 ): 1595 - 1604 .
王奉涛 , 刘晓飞 , 敦泊森 , 等 . 基于萤火虫优化的核自动编码器在中介轴承故障诊断中的应用 [J]. 机械工程学报 , 2018 , 55 ( 7 ): 58 - 64 .
WANG Fengtao , LIU Xiaofei , DUN Bosen , et al . Application of kernel auto-encoder based on firefly optimization in intershaft bearing fault diagnosis [J]. Journal of Mechanical Engineering , 2018 , 55 ( 7 ): 58 - 64 .
吴坤 , 康建设 , 池阔 . 基于改进多分类算法和相关向量机的电力变压器故障诊断方法 [J]. 高电压技术 , 2016 , 42 ( 9 ): 3011 - 3017 .
WU Kun , KANG Jianshe , CHI Kuo , et al . Fault diagnosis method of power transformers using improved multi-class classification algorithm and relevance vector machine [J]. High Voltage Engineering , 2016 , 42 ( 9 ): 3011 - 3017 .
范磊 , 卫志农 , 李慧杰 , 等 . 基于变分模态分解和蝙蝠算法-相关向量机的短期风速区间预测 [J]. 电力自动化设备 , 2017 , 37 ( 1 ): 93 - 100 .
FAN Lei , WEI Zhinong , LI Huijie , et al . Short-term wind speed interval prediction based on VMD and BA-RVM algorithm [J]. Automation of Electric Power Systems , 2017 , 37 ( 1 ): 93 - 100 .
徐搏超 . 高维空间可分性指标在转子诊断系统优化中的应用 [J]. 中国机械工程 , 2019 , 30 ( 10 ): 1239 - 1243 .
XU Bochao . Applications of separability index of high-dimensional space in rotor diagnosis system optimization [J]. China Mechanical Engineering , 2019 , 30 ( 10 ): 1239 - 1243 .
0
浏览量
0
下载量
5
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621