海上风电场智能维检平台方案设计

杨源; 杨源

doi:10.16516/j.ceec.2024-022

[1]

国家能源局. 电力设备预防性试验规程: DL/T 596-2021 [S]. 北京: 中国电力出版社, 2021.

National Energy Administration. Preventive test code for electric power equipment: DL/T 596-2021 [S]. Beijing: China electric power press, 2021.

[2]

国家能源局. 变电站机器人巡检系统通用技术条件: DL/T 1610-2016 [S]. 北京: 中国电力出版社, 2016.

National Energy Administration. General technical specification for the robot inspection system in substation: DL/T 1610-2016 [S]. Beijing: China electric power press, 2016.

[3]

国家能源局. 风电场无人机巡检作业技术规范: NB/T 10594-2021 [S]. 北京: 中国电力出版社, 2021.

National Energy Administration. Specification of unmanned aerial vehicle inspection for wind farm: NB/T 10594-2021 [S]. Beijing: China electric power press, 2021.

[4]

国家能源局. 油浸式变压器(电抗器)状态检修导则: DL/T 1684-2017 [S]. 北京: 中国电力出版社, 2017.

National Energy Administration. Guide for condition based maintenance strategy of oil-immersed power transformers (reactors): DL/T 1684-2017 [S]. Beijing: China electric power press, 2017.

[5]

国家能源局. 油浸式变压器(电抗器)状态评价导则: DL/T 1685-2017 [S]. 北京: 中国电力出版社, 2017.

National Energy Administration. Guide for condition evaluation of oil-immersed power transformers (reactors): DL/T 1685-2017 [S]. Beijing: China electric power press, 2017.

[6]

潍坊供电公司. 变电设备标准化巡视作业指导书 [M]. 北京: 中国电力出版社, 2012.

State Grid Shandong Weifang Power Supply Company. Substation equipment standardization inspection operation guide [M]. Beijing: China electric power press, 2012.

[7]

徐进, 孙静, 牛倩. 基于主动预防策略的风电场智能运维管理解决方案 [J]. 水力发电, 2020, 46(3): 104-107. DOI: 10.3969/j.issn.0559-9342.2020.03.023.

XU J, SUN J, NIU Q. Solution on intelligent operation and maintenance of wind farm based on active prevention strategy [J]. Water power, 2020, 46(3): 104-107. DOI: 10.3969/j.issn.0559-9342.2020.03.023.

[8]

杨源, 汪少勇, 谭江平, 等. 海上风电场智慧运维管理系统 [J]. 南方能源建设, 2021, 8(1): 74-79. DOI: 10.16516/j.gedi.issn2095-8676.2021.01.011.

YANG Y, WANG S Y, TAN J P, et al. The intelligent operation and maintenance management system for offshore wind farms [J]. Southern energy construction, 2021, 8(1): 74-79. DOI: 10.16516/j.gedi.issn2095-8676.2021.01.011.

[9]

刘昊. 变电检修中心管理平台的设计与实现 [D]. 济南: 山东大学, 2015.

LIU H. The design and implementation of substation maintenance center management platform [D]. Ji'nan: Shandong University, 2015.

[10]

宋明阳, 瞿晟珉, 秦少茜,等. 基于故障风险水平的海上风电场机会维护策略 [J]. 电力工程技术, 2023, 42(9): 117-129. DOI: 10.12158/j.2096-3203.2023.06.013.

SONG M Y, QU S M, QIN S X, et al. Offshore wind farm opportunity maintenance strategy based on failure risk level [J]. Electric power engineering technology, 2023, 42(9): 117-129. DOI: 10.12158/j.2096-3203.2023.06.013.

[11]

辛治铖, 汪隆君, 刘沈全. 基于迁移学习的海上风电机组轴承早期故障预警策略 [J]. 可再生能源, 2024, 42(7): 915-922. DOI: 10.13941/j.cnki.21-1469/tk.2024.07.012.

XIN Z C, Wang L J, LIU S Q. Early fault warning strategy for offshore wind turbine bearings based on transfer learning [J]. Renewable energy resources, 2024, 42(7): 915-922. DOI: 10.13941/j.cnki.21-1469/tk.2024.07.012.

[12]

王昊, 刘璐洁, 陈龙, 等. 考虑故障关联不确定性的海上风电机组预防性维护策略优化方法 [J]. 上海电力大学学报, 2024, 40(4): 331-339.

WANG H, LIU L J, CHEN L, et al. Preventive maintenance decision-making for offshore wind turbine considering failure correlation uncertainty [J]. Journal of Shanghai University of Electric Power, 2024, 40(4): 331-339.

[13]

郭微. 变电运维集中管控平台的设计与实现 [D]. 北京: 华北电力大学(北京), 2017.

GUO W. Substation operations concentrated control platform's design and implementation [D]. Beijing: North China Electric Power University (Beijing), 2017.

[14]

张本. 基于云平台的风电机组智能运维系统设计与实现 [D]. 北京: 北京邮电大学, 2021. DOI: 10.26969/d.cnki.gbydu.2021.002717.

ZHANG B. Intelligent maintenance system design of wind turbine based on cloud platform [D]. Beijing: Beijing University of Posts and Telecommunications, 2021. DOI: 10.26969/d.cnki.gbydu.2021.002717.

[15]

王玉红. 变电站智能运检管控系统的研究与实现 [D]. 合肥: 合肥工业大学, 2020.

WANG Y H. Research and implementation of intelligent management and control system for operation and inspection of substation [D]. Hefei: Hefei University of Technology, 2020.

[16]

胡皓翔. 智能运维技术在220 kV环澳站的应用研究 [D]. 广州: 华南理工大学, 2021. DOI: 10.27151/d.cnki.ghnlu.2021.003516.

HU H X. Application of intelligent operations in 220 kV Huanao substation [D]. Guangzhou: South China University of Technology, 2021. DOI: 10.27151/d.cnki.ghnlu.2021.003516.

[17]

张志宏, 施永吉, 黄建平, 等. 深远海域风电场智慧运维管理系统的探索与研究 [J]. 太阳能, 2018(6): 49-53, 25. DOI: 10.3969/j.issn.1003-0417.2018.06.011.

ZHANG Z H, SHI Y J, HUANG J P, et al. Exploration and research on the intelligent operation and maintenance management system of deep sea wind farm [J]. Solar energy, 2018(6): 49-53, 25. DOI: 10.3969/j.issn.1003-0417.2018.06.011.

[18]

邓清闯, 费怀胜, 李朝锋. 风电场智能运维管控系统方案设计 [J]. 机械与电子, 2019, 37(1): 71-74, 80. DOI: 10.3969/j.issn.1001-2257.2019.01.015.

DENG Q C, FEI H S, LI C F. Design of intelligent operation and maintenance control system for wind farms [J]. Machinery & electronics, 2019, 37(1): 71-74, 80. DOI: 10.3969/j.issn.1001-2257.2019.01.015.

[19]

周登科, 程龙, 张亚平, 等. 海上升压站机器人智能巡检系统应用分析 [J]. 南方能源建设, 2025, 12(1): 116-126. DOI: 10.16516/j.ceec.2024-088.

ZHOU D K, CHENG L, ZHANG Y P, et al. Application analysis of intelligent robot inspection system at offshore step-up substation [J]. Southern energy construction, 2025, 12(1): 116-126. DOI: 10.16516/j.ceec.2024-088.

[20]

王博妮, 王锋, 葛行成, 等. 海上风电气象服务关键技术及应用分析 [J]. 南方能源建设, 2025, 12(1): 65-74. DOI: 10.16516/j.ceec.2024-126.

WANG B N, WANG F, GE H C, et al. Analysis of key technologies and applications of meteorological service for offshore wind power [J]. Southern energy construction, 2025, 12(1): 65-74. DOI: 10.16516/j.ceec.2024-126.

[21]

李铜林,曾甫龙. 基于 5G 技术的海上风电通信系统研究 [J]. 南方能源建设, 2024, 11(2): 51-58. DOI: 10.16516/j.ceec.2024.2.05.

LI T L, ZENG F L. Research on offshore wind power communication system based on 5G technology [J]. Southern energy construction, 2024, 11(2): 51-58. DOI: 10.16516/j.ceec.2024.2.05.

[22]

葛志超,谷艺林, 黄山, 等. 基于人工智能技术的变电站联合巡检系统应用研究 [J]. 电力大数据, 2023, 26(11): 89-96. DOI: 10.19317/j.cnki.1008-083x.2023.11.010.

GE Z C, GU Y L, HUANG S, et al. Application research of substation joint inspection system based on AI technology [J]. Power systems and big data, 2023, 26(11): 89-96. DOI: 10.19317/j.cnki.1008-083x.2023.11.010.