[1] 郑楷洪, 杨劲锋, 王鑫, 等. 用电量数据的可视化研究综述[J]. 电力系统保护与控制, 2022, 50(9): 179–187 ZHENG Kaihong, YANG Jinfeng, WANG Xin, et al. Overview of visualization research on electricity consumption data[J]. Power System Protection and Control, 2022, 50(9): 179–187 [2] 方紫筠, 钱玉良. 含有用户自主选择行为分析的电价套餐设计策略[J]. 智慧电力, 2022, 50(5): 94–99,105 FANG Ziyun, QIAN Yuliang. Design strategy of electricity price package with analysis of user's independent choice behavior[J]. Smart Power, 2022, 50(5): 94–99,105 [3] 白东壮, 田世明, 邹毅豪, 等. 基于FDA的居民用户空调用电行为分类分析方法[J]. 智慧电力, 2022, 50(3): 44–49, 71 BAI Dongzhuang, TIAN Shiming, ZOU Yihao, et al. Classification analysis method of residential air conditioning electricity consumption behavior based on functional data analysis model[J]. Smart Power, 2022, 50(3): 44–49, 71 [4] 李强, 宋宁希, 王剑晓, 等. 基于用户互动能力的优化用电模式与方法[J]. 电网技术, 2016, 40(6): 1818–1824 LI Qiang, SONG Ningxi, WANG Jianxiao, et al. A pattern and method of optimized power utilization based on consumers' interaction capability[J]. Power System Technology, 2016, 40(6): 1818–1824 [5] 张立辉, 戴谷禹, 聂青云, 等. 碳交易机制下计及用电行为的虚拟电厂经济调度模型[J]. 电力系统保护与控制, 2020, 48(24): 154–163 ZHANG Lihui, DAI Guyu, NIE Qingyun, et al. Economic dispatch model of virtual power plant considering electricity consumption under a carbon trading mechanism[J]. Power System Protection and Control, 2020, 48(24): 154–163 [6] 徐晴, 刘建, 田正其, 等. 水、热、气、电四表合一数据采集系统的研究与应用[J]. 计算机测量与控制, 2017, 25(3): 217–221 XU Qing, LIU Jian, TIAN Zhengqi, et al. Research and application on data acquisition system about water, heat, gas, and electricity meters in one[J]. Computer Measurement & Control, 2017, 25(3): 217–221 [7] LIU G Y, YANG J, HAO Y, et al. Big data-informed energy efficiency assessment of China industry sectors based on K-means clustering[J]. Journal of Cleaner Production, 2018, 183: 304–314. [8] 孙毅, 冯云, 崔灿, 等. 基于动态自适应K均值聚类的电力用户负荷编码与行为分析[J]. 电力科学与技术学报, 2017, 32(3): 3–8 SUN Yi, FENG Yun, CUI Can, et al. Power user load code and behavior analysis based on dynamic adaptive k-means clustering[J]. Journal of Electric Power Science and Technology, 2017, 32(3): 3–8 [9] 孙义豪, 王璟, 郭勇, 等. 一种基于配电网评价指标体系和聚类分析的电网区域分类方法[J]. 武汉大学学报(工学版), 2018, 51(9): 817–822,846 SUN Yihao, WANG Jing, GUO Yong, et al. Area classification based on distribution network evaluation index system and cluster analysis[J]. Engineering Journal of Wuhan University, 2018, 51(9): 817–822,846 [10] 董志辉, 林凌雪. 基于改进模糊C均值聚类时段划分的配电网动态重构[J]. 电网技术, 2019, 43(7): 2299–2305 DONG Zhihui, LIN Lingxue. Dynamic reconfiguration of distribution network based on improved fuzzy C-means clustering of time division[J]. Power System Technology, 2019, 43(7): 2299–2305 [11] 石亮缘, 周任军, 张武军, 等. 采用深度学习和多维模糊C均值聚类的负荷分类方法[J]. 电力系统及其自动化学报, 2019, 31(7): 43–50 SHI Liangyuan, ZHOU Renjun, ZHANG Wujun, et al. Load classification method using deep learning and multi-dimensional fuzzy C-means clustering[J]. Proceedings of the CSU-EPSA, 2019, 31(7): 43–50 [12] CHENG X D, SCHERPEN J M A. Clustering approach to model order reduction of power networks with distributed controllers[J]. Advances in Computational Mathematics, 2018, 44(6): 1917–1939. [13] 赵凯, 侯玉强. 基于自组织映射神经网络K-means聚类算法的风电场多机等值建模[J]. 浙江电力, 2019, 38(8): 30–36 ZHAO Kai, HOU Yuqiang. Multi-machine equivalent modeling of wind farms using SOM-based K-means clustering[J]. Zhejiang Electric Power, 2019, 38(8): 30–36 [14] 谭宇航, 张朕滔, 袁玲, 等. 基于计量一体化的供电设备故障在线识别[J]. 控制工程, 2019, 26(6): 1133–1137 TAN Yuhang, ZHANG Zhentao, YUAN Ling, et al. Online failure identification method for power equipment based on metering automation and integration platform[J]. Control Engineering of China, 2019, 26(6): 1133–1137 [15] 张小斐, 魏玲, 王自强, 等. 基于负荷特性的大用户业扩辅助分析方法[J]. 电测与仪表, 2019, 56(12): 44–48,84 ZHANG Xiaofei, WEI Ling, WANG Ziqiang, et al. Auxiliary analysis method of power supply access for large consumers based on load characteristics[J]. Electrical Measurement & Instrumentation, 2019, 56(12): 44–48,84 [16] 潘明明, 田世明, 魏娜, 等. 基于数据划分的工业电力负荷曲线聚类研究[J]. 电气自动化, 2019, 41(4): 24–26,67 PAN Mingming, TIAN Shiming, WEI Na, et al. Research on industrial power load curve clustering based on data division[J]. Electrical Automation, 2019, 41(4): 24–26,67 [17] 钱潮恺, 黄德才. 基于维度频率相异度和强连通融合的混合数据聚类算法[J]. 模式识别与人工智能, 2016, 29(1): 82–89 QIAN Chaokai, HUANG Decai. Clustering algorithm for mixed data based on dimensional frequency dissimilarity and strongly connected fusion[J]. Pattern Recognition and Artificial Intelligence, 2016, 29(1): 82–89 [18] 黄苑华. 分类型数据的聚类算法研究[D]. 广州: 广东工业大学, 2016. HUANG Yuanhua. Studies on clustering algorithms for categorical data[D]. Guangzhou: Guangdong University of Technology, 2016. [19] BARAI (DEB) A, DEY L. Outlier detection and removal algorithm in K-means and hierarchical clustering[J]. World Journal of Computer Application and Technology, 2017, 5(2): 24–29. [20] 原野, 田园. 基于DTW层次聚类算法的电力负荷数据特征研究[J]. 自动化仪表, 2020, 41(12): 96–101 YUAN Ye, TIAN Yuan. Research on characteristics of power load data based on DTW hierarchical clustering algorithm[J]. Process Automation Instrumentation, 2020, 41(12): 96–101 [21] 徐杰彦, 许雯旸, 褚渊, 等. 区域尺度住宅建筑日用电负荷模型构建方法研究[J]. 中国电力, 2020, 53(8): 29–39 XU Jieyan, XU Wenyang, CHU Yuan, et al. Residential electricity load model construction in district scale[J]. Electric Power, 2020, 53(8): 29–39 [22] 苏欣, 田浩, 秦昌龙, 等. 多变量数据聚类最优选择的用电关联分析算法[J]. 电网与清洁能源, 2022, 38(4): 86-94, 103. SU Xin, TIAN Hao, QIN Changlong, et al. Electricity consumption association analysis algorithm for optimal selection of multivariate data clustering[J]. Advances of Power System and Hydroelectric Engineering, 2022, 38(4): 86-94, 103. [23] 杜勉, 易俊, 郭剑波, 等. 神经网络技术在风电机组SCADA数据分析中的应用研究[J]. 电网技术, 2018, 42(7): 2200–2205 DU Mian, YI Jun, GUO Jianbo, et al. Research on the application of neural networks on wind turbine SCADA data analysis[J]. Power System Technology, 2018, 42(7): 2200–2205 [24] 冯伟夏, 孟安波, 何双伯. 基于自组织网络的智能电网配电方案设计[J]. 现代电子技术, 2019, 42(9): 158–162 FENG Weixia, MENG Anbo, HE Shuangbo. Design of smart grid power distribution scheme based on self-organizing network[J]. Modern Electronics Technique, 2019, 42(9): 158–162 [25] 黄新波, 王宁, 朱永灿, 等. 基于RST-SOM的高压断路器故障诊断[J]. 高压电器, 2020, 56(3): 1–8 HUANG Xinbo, WANG Ning, ZHU Yongcan, et al. Fault diagnosis of high-voltage circuit breaker based on RST-SOM[J]. High Voltage Apparatus, 2020, 56(3): 1–8 [26] 黄红涛, 徐婷. 基于SOM聚类的用户信息数据自动挖掘算法研究[J]. 自动化与仪器仪表, 2022(7): 26–30 HUANG Hongtao, XU Ting. Research on automatic mining algorithm of user information data based on SOM clustering[J]. Automation & Instrumentation, 2022(7): 26–30 [27] 王杰, 丁明, 孙磊, 等. 基于改进聚类算法的关键输电断面搜索方法[J]. 中国电力, 2022, 55(6): 86–94 WANG Jie, DING Ming, SUN Lei, et al. Key transmission section search strategy based on improved clustering algorithm[J]. Electric Power, 2022, 55(6): 86–94 [28] 杨建华, 肖达强, 张伟, 等. 基于改进RBFNN的1 000 kV特高压线损预测[J]. 中国电力, 2022, 55(5): 122–127, 142 YANG Jianhua, XIAO Daqiang, ZHANG Wei, et al. Prediction of 1 000 kV UHV line loss based on improved RBFNN[J]. Electric Power, 2022, 55(5): 122–127, 142 [29] 陈卫东, 郭敏, 吴宁, 等. 基于图像匹配的微电网负荷响应分布式电源波动控制方法[J]. 中国电力, 2022, 55(3): 57–63, 73 CHEN Weidong, GUO Min, WU Ning, et al. The control method that load respond to distributed generation fluctuation in microgrid based on image matching[J]. Electric Power, 2022, 55(3): 57–63, 73 [30] 马宗彪, 许素安, 朱少斌, 等. 基于特征加权模糊聚类的电力负荷分类[J]. 中国电力, 2022, 55(6): 25–32 MA Zongbiao, XU Suan, ZHU Shaobin, et al. Power load classification based on feature weighted fuzzy clustering[J]. Electric Power, 2022, 55(6): 25–32 [31] 苏剑涛, 郑书婷, 严干贵, 等. 基于改进FCM聚类算法的风电场等值建模研究[J]. 智慧电力, 2021, 49(10): 68–74 SU Jiantao, ZHENG Shuting, YAN Gangui, et al. Equivalent modeling of wind farm based on improved FCM clustering algorithm[J]. Smart Power, 2021, 49(10): 68–74
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