1 |
谢敬东, 卢浩哲, 陆池鑫, 等. 基于分阶段离群点检测的电力市场异常辨识[J]. 科学技术与工程, 2021, 21(9): 3633-3641.
|
|
XIE J D, LU H Z, LU C X, et al. Identification of abnormal behavior in power market based on phased outlier detection[J]. Science Technology and Engineering, 2021, 21(9): 3633-3641. (in Chinese)
|
2 |
Al-Dhamari A, Sudirman R, Mahmood N H, et al. Online video-based abnormal detection using highly motion techniques and statistical measures[J]. Telkomnika, 2019, 17(4): 2039-2047.
|
3 |
丁小欧, 于晟健, 王沐贤, 等. 基于相关性分析的工业时序数据异常检测[J]. 软件学报, 2020, 31(3): 22.
|
|
DING X O, YU S J, WANG M X, et al. Anomaly Detection on Industrial Time Series Based on Correlation Analysis[J]. Journal of Software, 2020, 31(3): 22. (in Chinese)
|
4 |
LIM J, CHOI J. Web based online real-time outage cost assessment information system of power system[J]. Review of Scientific Instruments, 2012, 37(2): 171-172.
|
5 |
BRAITMAN L E. Confidence intervals extract clinically useful information from data[J]. Annals of Internal Medicine, 1988, 108(2): 296-298.
|
6 |
ZHANG S, VITTAL V. Design of wide-area power system damping controllers resilient to communication failures[J]. IEEE Transactions on Power Systems, 2013, 28(4): 4292-4300.
|
7 |
SHAFAGHI A. Equipment failure rate updating-Bayesian estimation[J]. Journal of Hazardous Materials, 2008, 159(1): 87-91.
|
8 |
SORENSEN P, CUTULULIS N A, VIGUERAS-RODRIGUEZ A, et al. Power fluctuations from large wind farms[J]. IEEE Transactions on Power Systems, 2007, 22(3): 958-965.
|
9 |
XU L, CHOW M Y, TAYLOR L S. Power distribution fault cause identification with imbalanced data using the data mining-based fuzzy classification E-algorithm[J]. IEEE Transactions on Power Systems, 2007, 22(1): 164-171.
|
10 |
BU S, YU F R, CAI Y, et al. When the smart grid meets energy Efficient communications: Green wireless cellular networks powered by the smart grid[J]. IEEE Transactions on Wireless Communications, 2012, 11(8): 3014-3024.
|
11 |
孙毅, 李世豪, 崔灿 等. 基于高斯校函数的电力用户用电数据离群点检测方法[J]. 电网技术, 2018, 42(5): 1595-1606.
|
|
SUN Y, LI S H, CUI C, et al. Improved outlier detection method of power consumer data based on Gaussian kernel function[J]. Power System. Technology., 2018, 42(5): 1595-1606. (in Chinese)
|
12 |
MONEDERO I, BISCARRI F, LEÓN C, et al. Detection of frauds and other non-technical losses in a power utility using Pearson coeffificient, Bayesian networks and decision trees[J]. International Journal of Electrical Power & Energy Systems, 2012, 34(1): 90-98.
|
13 |
WANG Z, Li G, WANG X, et al. Analysis of 10kV non-technical loss detection with data-driven approaches[C]//2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). Chengdu: IEEE, 2019: 4154-4158.
|
14 |
BUZAU M M, Tejedor-Aguilera J, Cruz-Romero P, et al. Hybrid deep neural networks for detection of non-technical losses in electricity smart meters[J]. IEEE Transactions on Power Systems, 2020, 35(2): 1254-1263.
|
15 |
CHAHLA C, SNOUSSI H, MERGHEM L, et al. A deep learning approach for anomaly detection and prediction in power consumption data[J]. Energy Efficiency, 2020, 13(8): 1633-1651.
|
16 |
BARUA A, MUTHIRAYAN D, KHARGONEKAR P P, et al. Hierarchical temporal memory based machine learning for real-time, unsupervised anomaly detection in smart grid: WiP abstract[C]//2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems(ICCPS). Sydney, NSW, Australia: ACM, 2020:188-189.
|
17 |
ROUZBAHANI H M, HADIS KARIMIPOUR H, LEI L. An ensemble deep convolutional neural network model for electricity theft detection in smart grids[C]//2020 IEEE International Conference on Systems, Man, and Cybernetics(SMC). Singapore. Singapore: IEEE, 2020: 3637-3642
|
18 |
LO Y L, HUANG S C, LU C N. Non-technical loss detection using smart distribution network measurement data[C]//IEEE PES Innovative Smart Grid Technologies. Tianjin: IEEE, 2012: 1-5.
|
19 |
张承智, 肖先勇, 郑子萱. 基于实值深度置信网络的用户侧窃电行为检测[J]. 电网技术, 2019, 43(3): 1083-1091.
|
|
ZHANG C Z, XIAO X Y, ZHENG Z X. Electricity theft detection for customers in power utility based on real-valued deep belief network[J]. Power System.Technology, 2019, 43(3): 1083-1091. (in Chinese)
|
20 |
WANG B Q, JIANG T H, ZHOU X, et al. Variance error of multi-classification based anomaly detection for time series data[J]. J. Comput. Methods Sci. Eng. 2021, 21(4): 875-890.
|
21 |
ADIL M, NADEEM, ZIA U, et al. Electricity theft detection using machine learning techniques to secure smart grid[C]//Proceedings of the 14th International Conference on Complex, Intelligent and Software Intensive Systems. Lodz: Springer. 2020: 233-243.
|
22 |
KHALEDIAN E, PANDEY S, KUNDU P, et al. Real-time synchrophasor data anomaly detection and classification using isolation forest, KMeans, and LoOP[J]. IEEE Transactions on Smart Grid, 2020, 12(3): 2378-2388.
|
|
LONG Z. A study of intelligent analysis of abnormal power consumption behavior based on daily load curve[C]//AIAM2020: 2nd International Conference on Artificial Intelligence and Advanced Manufacture. Shanghai: ACM, 2020: 209-216
|
24 |
MISHRA S, KSHIRSAGAR V, DWIVEDULA R, et al. Attention-Based Bi-LSTM for Anomaly Detection on Time-Series Data[C]//Proceedings of the 30th International Conference on Artificial Neural Networks. Bratislava: ENNS, 2021: 129-140.
|
25 |
HOMAYOUNI H, GHOSH S, RAY I, et al. An Autocorrelation-based LSTM-Autoencoder for Anomaly Detection on Time-Series Data[C]// 2020 IEEE International Conference on Big Data. Atlanta, Georgia: IEEE, 2020: 5068-5077.
|
26 |
PASSOS JÚNIOR L A, RAMOS C C OBA, RODRIGUES D, et al. Unsupervised non-technical losses identifification through optimum-path forest[J]. Electric Power Systems Research, 2016, 140(1): 413-423.
|
27 |
CUPER M, LÓDERER M, ROZINAJOVÁ V. Detection of abnormal load consumption in the power grid using clustering and statistical analysis[C]//Intelligent Data Engineering and Automated Learning. Manchester: Springer, 2019: 464-475.
|
28 |
曾惟如, 吴佳, 闫飞. 基于层级实时记忆算法的时间序列异常检测算法[J]. 电子学报, 2018, 46(2): 325-332.
|
|
ZENG W R, WU J, YAN F. time series anomaly detection model based on hierarchical temporal memory[J]. Acta Electronica Sinica, 2018, 46(2): 325-332. (in Chinese)
|
29 |
周伯阳, 郭志民, 王延松, 等. 基于多尺度低秩模型的电力无线接入网异常流量检测方法[J]. 电子学报, 2020, 48(8): 1552-1557.
|
|
ZHOU B Y, GUO Z M, WANG Y S, et al. An anomaly traffic detection method using multi-resolution low rank model for wireless access network of electric power grids[J]. Acta Electronica Sinica, 2020, 48(8): 1552-1557. (in Chinese)
|
30 |
PENG Y L, YANG Y N, XU Y J, et al. Electricity theft detection in ami based on clustering and local outlier factor[J]. IEEE Access,2021, 9(1): 107250-107259.
|