Power Quality Characteristics Identification of Industrial Users Based on Data Correlation Analysis
Zhang Yi1, Li Ke1, Shao Zhenguo1, Lin Nan1, Yu Junhong2
1. College of Electrical Engineering and Automation Fuzhou University Fuzhou 350108 China; 2. Fuzhou Power Supply Company of State Grid Fujian Electric Power Company Fuzhou 350009 China
Abstract：The type and number of interference source users at the point of common coupling (PCC) are increasing. This makes the index and spatial-temporal characteristics of power quality (PQ) disturbance more complex. However, it is difficult for the PQ monitoring devices to monitor all terminal users of each feeder. Recently, some methods were presented to identify the PQ characteristics of multiple users, but most of them failed to accommodate long-term PQ phenomena and uncertain user operating conditions. To address these issues, this paper proposes a industrial users PQ characteristics identification method. Using derivative dynamic time warping (DDTW) to calculate the correlation of the existing multi-source data, it accurately identifies the PQ characteristics for industrial users. Firstly, the over-limit and fluctuation periods of PQ monitoring index data are defined as disturbance periods. According to national standard limits and probability distribution, the over-limit and fluctuation values of PQ time series and their periods are extracted. Secondly, the screening rules of key harmonic indicators are set up to obtain the disturbance time series data of key harmonic indicators, voltage deviation and negative sequence voltage imbalance, so that the characteristics identification is more targeted. Thirdly, the user power data during the disturbance period are extracted, and the correlation between them and the monitoring indicators are calculated by using DDTW. Finally, the power quality characteristics of users are identified according to the correlation degree of different indicators. The simulation results show that the proposed method can select the user groups most related to PQ problem, which is consistent with the simulation settings. In addition, compared with the traditional DTW algorithm, this shows that DDTW overcomes ill-conditioned matching of time series data, so as to effectively filter out the data with strong correlation in time series.To compare with the identification method based on mutual information, the power quality monitoring data with an interval of 3 minutes is averaged every 5 as a group, which is aligned with the power data of 15 minutes, and then the maximum mutual information decision method is used to identify the user power quality characteristics. The result shows that the maximum mutual information method obtains more types of characteristics and some errors. The following conclusions can be drawn from the simulation and real measurement analysis: (1) Through the national standard limits and probability distribution method, it effectively determines key indicators for long time scale series. (2) The DDTW algorithm is used to match the power quality index of the monitoring point and the power time series data of the user side. In this way, it is more practical to select the representative indicators that are greatly affected by the power fluctuation. (3) The proposed method only needs long-term statistical data in the existing relevant systems. It is convenient to realize online identification of user power quality characteristics.
张逸, 李渴, 邵振国, 林楠, 余俊宏. 基于数据关联性分析的工业用户电能质量特征识别[J]. 电工技术学报, 2023, 38(13): 3512-3526.
Zhang Yi, Li Ke, Shao Zhenguo, Lin Nan, Yu Junhong. Power Quality Characteristics Identification of Industrial Users Based on Data Correlation Analysis. Transactions of China Electrotechnical Society, 2023, 38(13): 3512-3526.
[1] 严静, 邵振国. 电能质量谐波监测与评估综述[J]. 电气技术, 2020, 21(7): 1-7. Yan Jing, Shao Zhenguo.Summary of harmonic monitoring and evaluation[J]. Electrical Engineering, 2020, 21(7): 1-7. [2] 吴建章, 梅飞, 郑建勇, 等. 基于改进经验小波变换和XGBoost的电能质量复合扰动分类[J]. 电工技术学报, 2022, 37(1): 232-243, 253. Wu Jianzhang, Mei Fei, Zheng Jianyong, et al.Recognition of multiple power quality disturbances based on modified empirical wavelet transform and XGBoost[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 232-243, 253. [3] 熊敏, 杨洪耕. 基于改进协方差特性的永磁直驱风电场谐波发射水平评估[J]. 电工技术学报, 2020, 35(3): 603-611 Xiong Min, Yang Honggeng.Assessment method of D-PMSG wind farm harmonic emission level based on the improved covariance characteristic[J]. Transactions of China Electrotechnical Society, 2020, 35(3): 603-611 [4] 王鹤, 李石强, 于华楠, 等. 基于分布式压缩感知和边缘计算的配电网电能质量数据压缩存储方法[J]. 电工技术学报, 2020, 35(21): 4553-4564. Wang He, Li Shiqiang, Yu Huanan, et al.Compression acquisition method for power quality data of distribution network based on distributed compressed sensing and edge computing[J]. Transactions of China Electrotechnical Society, 2020, 35(21): 4553-4564. [5] 杨挺, 李扬, 何周泽, 等. 基于矩阵填充的泛在电力物联网电能质量数据修复算法[J]. 电力系统自动化, 2020, 44(2): 13-21. Yang Ting, Li Yang, He Zhouze, et al.Matrix completion theory based recovery algorithm for power quality data in ubiquitous power Internet of Things[J]. Automation of Electric Power Systems, 2020, 44(2): 13-21. [6] 张逸, 林才华, 邵振国, 等. 基于电子连续性方程的新型交流电弧炉通用模型[J]. 中国电机工程学报, 2021, 41(21): 7425-7433. Zhang Yi, Lin Caihua, Shao Zhenguo, et al.A novel general model of AC electric arc furnace based on electron continuity equation[J]. Proceedings of the CSEE, 2021, 41(21): 7425-7433. [7] 肖湘宁, 廖坤玉, 唐松浩, 等. 配电网电力电子化的发展和超高次谐波新问题[J]. 电工技术学报, 2018, 33(4): 707-720. Xiao Xiangning, Liao Kunyu, Tang Songhao, et al.Development of power-electronized distribution grids and the new supraharmonics issues[J]. Transactions of China Electrotechnical Society, 2018, 33(4): 707-720. [8] 陈海涛. 电能质量监测海量数据分析研究[D]. 广州: 华南理工大学, 2013. [9] 钟庆, 刘峰, 王钢, 等. 电能质量监测数据中间距离法聚类分析[J]. 电力系统及其自动化学报, 2016, 28(8): 69-73. Zhong Qing, Liu Feng, Wang Gang, et al.Middle distance clustering of power quality monitor data[J]. Proceedings of the CSU-EPSA, 2016, 28(8): 69-73. [10] 李长松, 刘凯, 肖先勇, 等. 基于条件互信息特征选择法和Adaboost算法的电能质量复合扰动分类[J]. 高电压技术, 2019, 45(2): 579-585. Li Changsong, Liu Kai, Xiao Xianyong, et al.Classification of multiple power quality disturbances based on conditional mutual information feature selection method and Adaboost algorithm[J]. High Voltage Engineering, 2019, 45(2): 579-585. [11] 于浩, 贾清泉, 李珍国, 等. 基于时间序列模式匹配的电能质量区域化治理[J]. 中国电机工程学报, 2019, 39(13): 3788-3798. Yu Hao, Jia Qingquan, Li Zhenguo, et al.Regionalization control for power quality based on time series pattern matching[J]. Proceedings of the CSEE, 2019, 39(13): 3788-3798. [12] 孙海东. 电能质量时间序列关联分析及数据驱动治理策略研究[D]. 秦皇岛: 燕山大学, 2018. [13] 石磊磊, 贾清泉, 孙海东, 等. 基于数据驱动的电能质量分区治理策略[J]. 中国电机工程学报, 2019, 39(4): 992-1001. Shi Leilei, Jia Qingquan, Sun Haidong, et al.Regional abatement strategy for power quality based on data driven[J]. Proceedings of the CSEE, 2019, 39(4): 992-1001. [14] 汪颖, 喻梦洁, 卢宏, 等. 基于最大互信息的干扰源类型识别及电能质量需求画像技术[J]. 电力系统自动化, 2022, 46(9): 171-181. Wang Ying, Yu Mengjie, Lu Hong, et al.Interference source type identification and power quality demand portrait technology based on maximum mutual information[J]. Automation of Electric Power Systems, 2022, 46(9): 171-181. [15] 秦梦雅. 基于区间-仿射算法的有源配电网电能质量综合评估[D]. 北京: 华北电力大学, 2020. [16] 张逸, 王攸然, 刘航, 等. 基于监测数据相关性分析的用户谐波责任划分方法[J]. 电力系统自动化, 2020, 44(2): 189-197. Zhang Yi, Wang Youran, Liu Hang, et al.Determination method of user harmonic responsibility based on correlation analysis of monitoring data[J]. Automation of Electric Power Systems, 2020, 44(2): 189-197. [17] 张逸, 姚文旭, 王康, 等. 考虑时序趋势分析的周期性谐波异常识别[J]. 电网技术, 2021, 45(3): 1117-1124. Zhang Yi, Yao Wenxu, Wang Kang, et al.Periodic harmonic anomaly recognition considering time series trend analysis[J]. Power System Technology, 2021, 45(3): 1117-1124. [18] Cooke T A, Howe W R.Dynamic statistical process control limits for power quality trend data[C]//2018 18th International Conference on Harmonics and Quality of Power (ICHQP), Ljubljana, Slovenia, 2008: 1-5. [19] 国家技术监督局. GB/T 14549—1993电能质量公用电网谐波[S]. 北京: 中国标准出版社, 1994. [20] 国家质量监督检验检疫总局, 中国国家标准化管理委员会. GB/T 12325—2008电能质量供电电压偏差[S]. 北京: 中国标准出版社, 2009. [21] 李云峰, 高云鹏, 蔡星月, 等. 自适应辛几何模态分解和短时能量差分因子在电能质量扰动检测中的应用[J]. 电工技术学报, 2022, 37(17): 4390-4400. Li Yunfeng, Gao Yunpeng, Cai Xingyue, et al.Application of adaptive symplectic geometry modal decomposition and short-time energy difference factor in power quality disturbance detection[J]. Transactions of China Electrotechnical Society, 2022, 37(17): 4390-4400. [22] 肖贤贵, 李开成, 蔡得龙, 等. 一种电能质量扰动信号的联合去噪算法[J]. 电工技术学报, 2021, 36(21): 4418-4428. Xiao Xiangui, Li Kaicheng, Cai Delong, et al.A combined de-noising method for power quality disturbances events[J]. Transactions of China Electrotechnical Society, 2021, 36(21): 4418-4428. [23] 国家质量监督检验检疫总局, 中国国家标准化管理委员会. GB/T 15543—2008电能质量三相电压不平衡[S]. 北京: 中国标准出版社, 2009. [24] 国家质量监督检验检疫总局. GB 12326—2000电能质量电压波动和闪变[S]. 北京: 中国标准出版社, 2004. [25] 国家质量监督检验检疫总局, 中国国家标准化管理委员会. GB/T 24337—2009电能质量公用电网间谐波[S]. 北京: 中国标准出版社, 2010. [26] 邝昊云, 温和. 基于泰勒-傅里叶变换的电压闪变测量方法[J]. 电工技术学报, 2020, 35(22): 4798-4806. Kuang Haoyun, Wen He.Voltage flicker measurement based on Taylor-Fourier transform[J]. Transactions of China Electrotechnical Society, 2020, 35(22): 4798-4806. [27] 冯丹丹, 王同勋. 一种电能质量扰动事件特征提取方法及系统: CN108053095A[P].2018-05-18. [28] 张逸, 李渴, 邵振国, 等. 基于标准文件的电能质量领域本体构建方法[J]. 电力系统自动化, 2020, 44(17): 102-111. Zhang Yi, Li Ke, Shao Zhenguo, et al.Standard files based ontology construction method in power quality domain[J]. Automation of Electric Power Systems, 2020, 44(17): 102-111. [29] 刘苏, 黄纯, 侯帅帅, 等. 基于DDTW距离与DBSCAN算法的户变关系识别方法[J]. 电力系统自动化, 2021, 45(18): 71-77. Liu Su, Huang Chun, Hou Shuaishuai, et al.Identification method for household-transformer relationship based on derivative dynamic time warping distance and density-based spatial clustering of application with noise algorithm[J]. Automation of Electric Power Systems, 2021, 45(18): 71-77. [30] Górecki T, Łuczak M.Using derivatives in time series classification[J]. Data Mining and Knowledge Discovery, 2013, 26(2): 310-331. [31] 国家电网公司. Q/GDW 1354—2013智能电能表功能规范[S]. 2013. [32] 杨朝赟, 夏圣峰, 江南, 等. 基于相关性分析和长短时记忆网络的稳态电压质量指标预测[J]. 电力建设, 2021, 42(4): 9-16. Yang Chaoyun, Xia Shengfeng, Jiang Nan, et al.Prediction of steady-state indices of voltage quality based on correlation analysis and long short-term memory network[J]. Electric Power Construction, 2021, 42(4): 9-16. [33] 朱明星, 张毅恒, 张华赢, 等. 考虑指标特性的区域电网电能质量评估方法[J]. 电力系统及其自动化学报, 2022, 34(8): 150-158. Zhu Mingxing, Zhang Yiheng, Zhang Huaying, et al.Regional power grid quality evaluation method considering index characteristics[J]. Proceedings of the CSU-EPSA, 2022, 34(8): 150-158.
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