配电网运行异常数据识别方法

doi:10.11930/j.issn.1004-9649.202302051

中国电力 ›› 2023, Vol. 56 ›› Issue (9): 134-139.DOI: 10.11930/j.issn.1004-9649.202302051

• 配电网规划与优化运行 • 上一篇下一篇

配电网运行异常数据识别方法

林昱奂¹, 郝方舟², 李柏新¹, 黄波¹

1. 广东电网有限责任公司广州番禺供电局，广东广州 511400;
2. 广州供电局有限公司，广东广州 510006

收稿日期:2023-02-14 修回日期:2023-08-09 发布日期:2023-09-20
作者简介:林昱奂(1986-),男,工程师,从事配网运行管理、配网规划研究,E-mail:18988826878@139.com
基金资助:
广东电网有限责任公司广州番禺供电局科技项目（082700KK52200001）；国家自然科学基金资助项目（51877061）。

Method for Identifying Abnormal Data in Distribution Network Operation

LIN Yuhuan¹, HAO Fangzhou², LI Baixin¹, HUANG Bo¹

1. Guangzhou Panyu Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou 511400, China;
2. Guangzhou Power Supply Bureau Co., Ltd., Guangzhou 510006, China

Received:2023-02-14 Revised:2023-08-09 Published:2023-09-20
Supported by:
This work is supported by Science and Technology Project of Guangzhou Panyu Power Supply Bureau of Guangdong Power Grid Co., Ltd. (No.082700KK52200001), National Natural Science Foundation of China (No.51877061).

摘要/Abstract

摘要： 为提高配电网运行异常数据的识别准确率，提出结合k均值改进萤火虫算法的配电网运行异常数据识别方法。在配电网信息系统中采集配电网运行数据，并实施数据清洗、合并、特征化处理等操作。利用结合k均值改进萤火虫算法的优化聚类获取样本特征曲线及样本分类，得到带通矩阵来判定异常数据点，完成配电网运行异常数据识别。结果表明：所提方法对不同的用电设备状态组合的运行异常数据识别准确率更高。

关键词: 配电网运行异常数据, 异常识别, 萤火虫算法, 优化聚类

Abstract: In order to improve the recognition accuracy of abnormal data of distribution network operation, a novel method for identifying abnormal data of distribution network operation is proposed based on k-means improved firefly algorithm. Firstly, the distribution network operation data is collected in the distribution network information system, and such treatments as data cleansing, consolidation and characterization are performed. Then, the optimal clustering of the improved firefly algorithm based on k-means is used to obtain the sample characteristic curves and sample classification, and the band-pass matrix is obtained to determine the abnormal data points, so the identification of abnormal data of distribution network operation is completed. The results show that the proposed method has a higher accuracy in identifying abnormal operating data for different combinations of electrical equipment states.

Key words: abnormal data of distribution network operation, abnormal recognition, firefly algorithm, optimize clustering

林昱奂, 郝方舟, 李柏新, 黄波. 配电网运行异常数据识别方法[J]. 中国电力, 2023, 56(9): 134-139.

LIN Yuhuan, HAO Fangzhou, LI Baixin, HUANG Bo. Method for Identifying Abnormal Data in Distribution Network Operation[J]. Electric Power, 2023, 56(9): 134-139.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202302051

https://www.electricpower.com.cn/CN/Y2023/V56/I9/134

参考文献

[1] 岳园园, 王主丁, 王辉, 等. 考虑设备调节成本的主动配电网日前优化调度[J]. 中国电力, 2023, 56(8): 136–142
YUE Yuanyuan, WANG Zhuding, WANG Hui, et al. Day-ahead optimal dispatch for active distribution network considering action cost of devices[J]. Electric Power, 2023, 56(8): 136–142
[2] 严宇平, 洪雨天, 陈守明, 等. 基于参数估计的配电网载波通信异常信号识别方法[J]. 电测与仪表, 2022, 59(10): 123–129
YAN Yuping, HONG Yutian, CHEN Shouming, et al. Identification method of abnormal signal of carrier communication in distribution network based on parameter estimation[J]. Electrical Measurement & Instrumentation, 2022, 59(10): 123–129
[3] 傅世元, 高欣, 张浩, 等. 基于元学习动态选择集成的电力调度数据异常检测方法[J]. 电网技术, 2022, 46(8): 3248–3261
FU Shiyuan, GAO Xin, ZHANG Hao, et al. Anomaly detection for power dispatching data based on meta-learning dynamic ensemble selection[J]. Power System Technology, 2022, 46(8): 3248–3261
[4] 郝颖, 冬雷, 王丽婕, 等. 基于数学形态学去噪的光伏发电限电异常数据识别算法[J]. 中国电机工程学报, 2022, 42(21): 7843–7855
HAO Ying, DONG Lei, WANG Lijie, et al. An algorithm for identifying abnormal data of photovoltaic power cut-off based on mathematical morphology denoising[J]. Proceedings of the CSEE, 2022, 42(21): 7843–7855
[5] 付晓, 张子闻, 邓冰妍. 基于策略融合的电网运行异常值检测方法[J]. 光学与光电技术, 2022, 20(4): 160–165
FU Xiao, ZHANG Ziwen, DENG Bingyan. A strategy fusion-based method for detecting grid operation anomalies[J]. Optics & Optoelectronic Technology, 2022, 20(4): 160–165
[6] 张晗, 杨继斌, 张继业, 等. 基于多种群萤火虫算法的车载燃料电池直流微电网能量管理优化[J]. 中国电机工程学报, 2021, 41(3): 833–846
ZHANG Han, YANG Jibin, ZHANG Jiye, et al. Multiple-population firefly algorithm-based energy management strategy for vehicle-mounted fuel cell DC microgrid[J]. Proceedings of the CSEE, 2021, 41(3): 833–846
[7] 张稳, 盛万兴, 杜松怀, 等. 基于海量数据的配电网运行分析系统架构与技术实现[J]. 电力系统自动化, 2020, 44(3): 147–153
ZHANG Wen, SHENG Wanxing, DU Songhuai, et al. Architecture and technology implementation of massive data based distribution network operation analysis system[J]. Automation of Electric Power Systems, 2020, 44(3): 147–153
[8] 李蕊, 陈孝文, 许家伟, 等. 智能配电网运行数据可视化监控方法研究[J]. 自动化与仪器仪表, 2020(11): 223–226
LI Rui, CHEN Xiaowen, XU Jiawei, et al. Research on visual monitoring method of intelligent distribution network operation data[J]. Automation & Instrumentation, 2020(11): 223–226
[9] 李光军, 袁丹. 配电网统一采集和监控平台的设计[J]. 中国电业(技术版), 2015(7): 38–41
[10] 贾巍, 雷才嘉, 方兵华, 等. 供电分区场景下基于数据驱动的负荷密度综合评估及预测方法[J]. 中国电力, 2023, 56(6): 71–81
JIA Wei, LEI Caijia, FANG Binghua, et al. A comprehensive evaluation and prediction method for load density based on big data under power supply partition scenarios[J]. Electric Power, 2023, 56(6): 71–81
[11] 陈瑞兴, 尹洪苓, 安东升, 等. 大数据技术在配电网全时序运行效率分析中的应用[J]. 供用电, 2021, 38(3): 22–30
CHEN Ruixing, YIN Hongling, AN Dongsheng, et al. Application of big data technology in efficiency analysis of distribution network in full sequence operation[J]. Distribution & Utilization, 2021, 38(3): 22–30
[12] 赵云龙, 傅文进, 李沛. 基于大数据的配电网运行风险智能预警方法[J]. 自动化应用, 2022(1): 116–118, 122
ZHAO Yunlong, FU Wenjin, LI Pei. Intelligent early warning method of distribution network operation risk based on big data[J]. Automation Application, 2022(1): 116–118, 122
[13] 郑重, 苗世洪, 李超, 等. 面向微型能源互联网接入的交直流配电网协同优化调度策略[J]. 电工技术学报, 2022, 37(1): 192–207
ZHENG Zhong, MIAO Shihong, LI Chao, et al. Coordinated optimal dispatching strategy of AC/DC distribution network for the integration of micro energy Internet[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 192–207
[14] 程江洲, 熊双菊, 张赟宁, 等. 基于博弈集对云的中压配电网运行状态评估与预测[J]. 水电能源科学, 2020, 38(7): 207–210, 206
CHENG Jiangzhou, XIONG Shuangju, ZHANG Yunning, et al. Evaluation and prediction of operation status of medium voltage distribution network based on game set pair cloud[J]. Water Resources and Power, 2020, 38(7): 207–210, 206
[15] 康宁, 张颖, 任桂田, 等. 考虑PMU量测信息的配电网运行状态分析方法[J]. 电力系统及其自动化学报, 2020, 32(2): 92–97
KANG Ning, ZHANG Ying, REN Guitian, et al. Analysis method for distribution network operation state with PMU measurement information[J]. Proceedings of the CSU-EPSA, 2020, 32(2): 92–97
[16] 张今, 耿光超, 江全元, 等. 含柔性多状态开关的配电网多目标随机运行优化方法[J]. 高电压技术, 2019, 45(10): 3140–3148
ZHANG Jin, GENG Guangchao, JIANG Quanyuan, et al. Multi-objective stochastic operation optimization for distribution network with flexible multi-state switches[J]. High Voltage Engineering, 2019, 45(10): 3140–3148
[17] 罗宁, 陈露东, 卢嗣斌, 等. 基于聚类分析和改进灰色关联的配电网运行可靠性评价[J]. 武汉大学学报(工学版), 2020, 53(7): 636–642
LUO Ning, CHEN Ludong, LU Sibin, et al. Reliability evaluation of distribution network operation based on cluster analysis and improved grey relation[J]. Engineering Journal of Wuhan University, 2020, 53(7): 636–642
[18] 刘浩武, 王守相, 刘琪, 等. 考虑DG渗透率的交直流混合配电网运行优化方法[J]. 供用电, 2020, 37(5): 18–23
LIU Haowu, WANG Shouxiang, LIU Qi, et al. Operation optimization method of AC/DC distribution network considering DG penetration rate[J]. Distribution & Utilization, 2020, 37(5): 18–23
[19] 陈旸, 刘竹杰, 李美依, 等. 基于多源数据的主动配电网运行评估指标与方法研究[J]. 电气自动化, 2020, 42(2): 40–43
CHEN Yang, LIU Zhujie, LI Meiyi, et al. Research on operational assessment indexes and methods for active distribution networks based on multi-source data[J]. Electrical Automation, 2020, 42(2): 40–43
[20] 白浩, 于力, 姜臻, 等. 基于四端E-SOP的有源配电网运行效率优化[J]. 电力系统及其自动化学报, 2020, 32(3): 89–98
BAI Hao, YU Li, JIANG Zhen, et al. Operating efficiency optimization for active distribution network based on four-terminal E-SOPs[J]. Proceedings of the CSU-EPSA, 2020, 32(3): 89–98
[21] 程江洲, 聂玮瑶, 张赟宁, 等. 基于FP-network关联规则挖掘算法的配电网薄弱点分析研究[J]. 电测与仪表, 2021, 58(3): 47–53
CHENG Jiangzhou, NIE Weiyao, ZHANG Yunning, et al. Analysis and research on weak point of distribution network based on FP-network association rule mining algorithm[J]. Electrical Measurement & Instrumentation, 2021, 58(3): 47–53
[22] 唐捷, 蔡永智, 周来, 等. 基于数据驱动的低压配电网线户关系识别方法[J]. 电力系统自动化, 2020, 44(11): 127–134
TANG Jie, CAI Yongzhi, ZHOU Lai, et al. Data-driven based identification method of feeder-consumer connectivity in low-voltage distribution network[J]. Automation of Electric Power Systems, 2020, 44(11): 127–134
[23] 丁忠安, 高琛, 蒋敏敏, 等. 基于专家样本库和最小二乘支持向量机的配电网线损率预测模型[J]. 水电能源科学, 2020, 38(3): 195–198, 194
DING Zhongan, GAO Chen, JIANG Minmin, et al. Prediction model of line loss rate in distribution systems based on expert sample base and LS-SVM[J]. Water Resources and Power, 2020, 38(3): 195–198, 194
[24] 宋晓林, 黄璐涵, 贺云隆, 等. 基于智能电能表采集数据的台户关系识别新方法[J]. 电测与仪表, 2020, 57(23): 135–140
SONG Xiaolin, HUANG Luhan, HE Yunlong, et al. Novel identification method of station-area relationship based on data acquisition by smart meter[J]. Electrical Measurement & Instrumentation, 2020, 57(23): 135–140
[25] 黄悦华, 邹子豪, 张赟宁, 等. 基于FP-Growth算法的配电网薄弱点分析研究[J]. 电测与仪表, 2020, 57(17): 79–84, 135
HUANG Yuehua, ZOU Zihao, ZHANG Yunning, et al. Research on weak point analysis of distribution network based on FP-Growth algorithm[J]. Electrical Measurement & Instrumentation, 2020, 57(17): 79–84, 135

配电网运行异常数据识别方法

Method for Identifying Abnormal Data in Distribution Network Operation

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 6

编辑推荐

Metrics

[1]	李娟, 汪家铭, 许苏迪, 姜云龙, 杨雄. 基于量子萤火虫算法的配电网故障恢复策略[J]. 中国电力, 2023, 56(12): 191-198.
[2]	陈晓娟, 贾敏, 李明晨. 基于线圈偏移的无线电能传输系统参数优化方法[J]. 中国电力, 2021, 54(7): 125-131.
[3]	范海虹. 基于萤火虫算法的短期电力负荷预测方法[J]. 中国电力, 2021, 54(3): 141-148.
[4]	宋宗耘, 牛东晓, 肖鑫利, 朱琳. 基于改进萤火虫算法优化SVM的变电工程造价预测[J]. 中国电力, 2017, 50(3): 168-173.
[5]	黄新波, 宋桐, 王娅娜, 李文君子. 基于IGSO优化LM网络的变压器故障诊断方法[J]. 中国电力, 2014, 47(9): 60-65.
[6]	洪筱，丁晓群，杨海东，黄恒硕. 基于IGSO计及谐波电压畸变的无功优化[J]. 中国电力, 2013, 46(2): 82-86.

模态框（Modal）标题

配电网运行异常数据识别方法

Method for Identifying Abnormal Data in Distribution Network Operation

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 6

编辑推荐

Metrics