[1] 王浩然, 冯天天, 崔茗莉, 等. 碳交易政策下绿氢交易市场与电力市场耦合效应分析 [J]. 南方能源建设, 2023, 10(3): 32-46. DOI:  10.16516/j.gedi.issn2095-8676.2023.03.004.

WANG H R, FENG T T, CUI M L, et al. Analysis of coupling effect between green hydrogen trading market and electricity market under carbon trading policy [J]. Southern energy construction, 2023, 10(3): 32-46. DOI:  10.16516/j.gedi.issn2095-8676.2023.03.004.
[2] 王鑫, 吴继承, 朴磊. “双碳”目标下核能发展形势思考 [J]. 核科学与工程, 2022, 42(2): 241-245. DOI:  10.3969/j.issn.0258-0918.2022.02.001.

WANG X, WU J C, PU L. Consideration of the development situation of nuclear power under the goal of carbon peaking and carbon neutraulity [J]. Nuclear science and engineering, 2022, 42(2): 241-245. DOI:  10.3969/j.issn.0258-0918.2022.02.001.
[3] 蔡绍宽. 双碳目标的挑战与电力结构调整趋势展望 [J]. 南方能源建设, 2021, 8(3): 8-17. DOI:  10.16516/j.gedi.issn2095-8676.2021.03.002.

CAI S K. Challenges and prospects for the trends of power structure adjustment under the goal of carbon peak and neutrality [J]. Southern energy construction, 2021, 8(3): 8-17. DOI:  10.16516/j.gedi.issn2095-8676.2021.03.002.
[4] 吴铮, 张悦, 董泽. 基于改进高斯混合模型的热工过程异常值检测 [J]. 系统仿真学报, 2023, 35(5): 1020-1033. DOI:  10.16182/j.issn1004731x.joss.22-0047.

WU Z, ZHANG Y, DONG Z. Outlier detection during thermal processes based on improved gaussian mixture model [J]. Journal of system simulation, 2023, 35(5): 1020-1033. DOI:  10.16182/j.issn1004731x.joss.22-0047.
[5] 崔文浩, 郑胜, 秦雄杰, 等. 基于多尺度时间窗口的核电运行数据关联性分析方法研究 [J]. 南方能源建设, 2023, 10(2): 143-150. DOI:  10.16516/j.gedi.issn2095-8676.2023.02.019.

CUI W H, ZHENG S, QIN X J, et al. Research on correlation analysis method for nuclear power operation data based on multi-scale time window [J]. Southern energy construction, 2023, 10(2): 143-150. DOI:  10.16516/j.gedi.issn2095-8676.2023.02.019.
[6]

YIN S, DING S X, XIE X C, et al. A review on basic data-driven approaches for industrial process monitoring [J]. IEEE Transactions on industrial electronics, 2014, 61(11): 6418-6428. DOI:  10.1109/TIE.2014.2301773.
[7]

JIN X H, FAN J C, CHOW T W S. Fault detection for rolling-element bearings using multivariate statistical process control methods [J]. IEEE transactions on instrumentation and measurement, 2019, 68(9): 3128-3136. DOI:  10.1109/TIM.2018.2872610.
[8]

ZHANG Y W, LI S, HU Z Y. Improved multi-scale kernel principal component analysis and its application for fault detection [J]. Chemical engineering research and design, 2012, 90(9): 1271-1280. DOI:  10.1016/j.cherd.2011.11.015.
[9]

JIANG Q C, YAN X F, HUANG B. Performance-driven distributed PCA process monitoring based on fault-relevant variable selection and bayesian inference [J]. IEEE transactions on industrial electronics, 2016, 63(1): 377-386. DOI:  10.1109/TIE.2015.2466557.
[10]

CHANDOLA V, BANERJEE A, KUMAR V. Anomaly detection: a survey [J]. ACM computing surveys (CSUR), 2009, 41(3): 15. DOI:  10.1145/1541880.1541882.
[11]

WANG H, PENG M J, WESLEY HINES J, et al. A hybrid fault diagnosis methodology with support vector machine and improved particle swarm optimization for nuclear power plants [J]. ISA transactions, 2019, 95: 358-371. DOI:  10.1016/j.isatra.2019.05.016.
[12]

NOMIKOS P. Detection and diagnosis of abnormal batch operations based on multi-way principal component analysis world batch forum, Toronto, May 1996 [J]. ISA transactions, 1996, 35(3): 259-266. DOI:  10.1016/S0019-0578(96)00035-3.
[13]

CHEN S Y, JIN G, MA X Y. Satellite on-orbit anomaly detection method based on a dynamic threshold and causality pruning [J]. IEEE access, 2021, 9: 86751-86758. DOI:  10.1109/ACCESS.2021.3088439.
[14] 卢培, 李小宝, 郑晨旭, 等. 350 MW余热锅炉变工况运行特性分析 [J]. 南方能源建设, 2022, 9(3): 41-49. DOI:  10.16516/j.gedi.issn2095-8676.2022.03.005.

LU P, LI X B, ZHENG C X, et al. Analysis on operation characteristics of 350 MW waste heat boiler under variable working conditions [J]. Southern energy construction, 2022, 9(3): 41-49. DOI:  10.16516/j.gedi.issn2095-8676.2022.03.005.
[15]

SIFFER A, FOUQUE P A, TERMIER A, et al. Anomaly detection in streams with extreme value theory [C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, Canada, August 13-17, 2017. Halifax: ACM, 2017: 1067-1075. DOI:  10.1145/3097983.3098144.
[16]

CHARRAS-GARRIDO M, DEVILLE Y, LEZAUD P. Corrective to the article: extreme value analysis - an introduction Journal de la SFdS Vol. 154 No2, 66-97 [J]. Journal de la société française de statistique, 2017, 158(3): 27-28.
[17]

YU X L, ZHAO Z B, ZHANG X W, et al. Deep-learning-based open set fault diagnosis by extreme value theory [J]. IEEE transactions on industrial informatics, 2022, 18(1): 185-196. DOI:  10.1109/TII.2021.3070324.
[18]

BEIRLANT J, GOEGEBEUR Y, TEUGELS J, et al. Statistics of extremes: theory and applications [M]. Hoboken: Wiley, 2004. DOI:  10.1002/0470012382.
[19]

HUNDMAN K, CONSTANTINOU V, LAPORTE C, et al. Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding [C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK, August 19-23, 2018. London: ACM, 2018: 387-395. DOI:  10.1145/3219819.3219845.
[20]

LUO X Y, ZHENG S, HUANG Y, et al. Molecular clump extraction algorithm based on local density clustering [J]. Research in astronomy and astrophysics, 2021, 22(1): 015003. DOI:  10.1088/1674-4527/ac321d.
[21]

WANG H, PENG M J, YU Y, et al. Fault identification and diagnosis based on KPCA and similarity clustering for nuclear power plants [J]. Annals of nuclear energy, 2021, 150: 107786. DOI:  10.1016/j.anucene.2020.107786.