Abstract：The operational reliability assessment of integrated energy systems can not only realize the real-time perception of the operating status, but also can reasonably predict the short-term operational risks. Using data-driven artificial intelligence technology to solve problems in the energy field is a current research hotspot. In this paper, firstly, the equipment operational reliability modeling methods of integrated energy systems considering time-varying features and uncertainty of supply and demand based on data-driven method were summarized, and the shortcomings of existing research in equipment operational reliability modeling were pointed out. Then, the principles, research status and limitations of current research on data-driven and model-data hybrid driven operational reliability assessment methods were introduced in detail. Finally, aiming at the shortcomings of existing research, the study on the operational reliability assessment of integrated energy systems were summarized and prospected, and the overall idea of hybrid-driven operational reliability modeling and assessment process under the background of artificial intelligence was proposed.
朱继忠, 骆腾燕, 吴皖莉, 李盛林, 董瀚江. 综合能源系统运行可靠性评估评述Ⅱ：数据驱动法与模型-数据混合驱动法[J]. 电工技术学报, 2022, 37(13): 3227-3240.
Zhu Jizhong, Luo Tengyan, Wu Wanli, Li Shenglin, Dong Hanjiang. A Review of Operational Reliability Assessment of Integrated Energy Systems Ⅱ: Data-Driven Method and Model-Data Hybrid Driven Method. Transactions of China Electrotechnical Society, 2022, 37(13): 3227-3240.
[1] 周子冠, 白晓民, 李再华, 等. 采用知识网格技术的智能输电网故障诊断方法[J]. 中国电机工程学报, 2010, 30(4): 8-15. Zhou Ziguan, Bai Xiaomin, Li Zaihua, et al.Novel fault diagnosis approach of smart transmission grid based on knowledge grid technology[J]. Proceeding of the CSEE, 2010, 30(4): 8-15. [2] 段杰, 王秀丽, 侯雨伸. 基于模糊专家系统的输电线路分段冰风荷载等效停运率模型[J]. 电工技术学报, 2016, 31(8): 220-228. Duan Jie, Wang Xiuli, Hou Yushen.Piecewise equivalent model if ice disaster impact on outage rate of transmission lines using fuzzy expert system[J]. Transactions of China Electrotechnical Society, 2016, 31(8): 220-228. [3] Flores N, Quiles E, Egarcia E, et al.Fault diagnosis of electric transmission lines using modular neural networks[J]. IEEE Latin America Transactions, 2016, 14(8): 3663-3668. [4] Ma Deyin, Liang Yanchun, Zhao Xiaoshe, et al.Multi-BP expert system for fault diagnosis of power system[J]. Applications of Artifical Intelligence, 2013, 26(3): 937-944. [5] 孙羽, 王秀丽, 王建学, 等. 电力系统短期可靠性评估综述[J]. 电力系统保护与控制, 2011, 39(8): 143-154. Sun Yu, Wang Xiuli, Wang Jianxue, et al.An overview of the short-term reliability evaluation of power system[J]. Power System Protection and Control, 2011, 39(8): 143-154. [6] 赵霞, 胡潇云, 杨仑, 等. 考虑参数不确定性的区域电-气联合系统的概率-模糊能流评估[J]. 电力自动化设备, 2019, 39(2): 135-142. Zhao Xia, Hu Xiaoyun, Yang Lun, et al.Probabilistic-possibilistic energy flow evaluation of regional electricity-gas integrated system considering parameter uncertainties[J]. Electric Power Automation Equipment, 2019, 39(2): 135-142. [7] Suganthi L, Iniyan S, Samuel A.Applications of fuzzy logic in renewable energy systems - a review[J]. Renewable and Sustainable Energy Review, 2015(48): 585-607. [8] Cao Yang, Wei Wei, Chen Laijun, et al.Supply inadequacy risk evaluation of stand-alone renewable powered heat-electricity energy systems: a data-driven robust approach[J]. IEEE Transactions on Industrial Informatics, 2020, 17(3): 1937-1947. [9] Neil M, Tailor M, Marquez D, et al.Modeling dependable systems using hybrid Bayesian networks[C]//First International Conference in Availability, Reliability and Security, Vienna, Australia, 2006: 819-823. [10] 宁宁剑, 任怡睿, 林济铿, 等. 基于人工智能及信息融合的电力系统故障诊断方法[J]. 电网技术, 2021, 45(8): 2925-2936. Ningjian, Ren Yirui, Lin Jikeng, et al. Power system fault diagnosis method based on artificial intelligence and information fusion[J]. Power System Technology, 2021, 45(8): 2925-2936. [11] 李岩, 滕云, 冷欧阳, 等. 数据驱动的输电线路在线监测装置可靠性评估[J]. 中国电机工程学报, 2018, 38(15): 4410-4419, 4641. Li Yan, Teng Yun, Leng Ouyang, et al.Reliability evaluation of on-line monitoring device over transmission line based on data driven concept[J]. Proceedings of the CSEE, 2018, 38(15): 4410-4419, 4641. [12] 刘雪锋, 王红平, 钟汶兵, 等. 基于数据驱动的输电线路在线监测及可靠性评估[J]. 电气开关, 2021, 59(1): 13-15. Liu Xuefeng, Wang Hongping, Zhong Wenbing, et al.Transmission line online monitoring and reliability estimation based on drive data[J]. Electrical Switch, 2021, 59(1): 13-15. [13] 杨挺, 赵黎媛, 王成山. 人工智能在电力系统及综合能源系统中的应用综述[J]. 电力系统自动化, 2019, 43(1): 2-14. Yang Ting, Zhao Liyuan, Wang Chengshan.Review on application of artificial intelligence in power system and integrated energy system[J]. Automation of Electric Power Systems, 2019, 43(1): 2-14. [14] 何俊涛. 基于录波数据的电力系统故障元件智能诊断方法研究[D]. 济南: 山东大学, 2019. [15] Rossi F, Velazquez D.A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant[J]. Energy, 2015(89): 528-544. [16] Li Wenyuan, Zhou Jiaqi, Xie Kaigui, et al.Power system risk assessment using a hybrid method of fuzzy set and Monte Carlo simulation[J]. IEEE Transactions on Power Systems, 2008, 23(2): 336-343. [17] 王守鹏, 赵冬梅. 电网故障诊断的研究综述与前景展望[J]. 电力系统自动化, 2017, 41(19): 164-175. Wang Shoupeng, Zhao Dongmei.Research review and prospects for power grid fault diagnosis[J]. Automation of Electric Power System, 2017, 41(19): 164-175. [18] 白展, 苗世洪, 孙雁斌, 等. 计及时间约束的改进模糊Petri网故障诊断模型[J]. 电工技术学报, 2016, 31(23): 107-115. Bai Zhan, Miao Shihong, Sun Yanbin, et al.Fault diagnosis model based on improved fuzzy Petri net considering time constraints[J]. Transcations of China Electrotechnical Society, 2016, 31(23): 107-115. [19] 程学珍, 林晓晓,朱春华, 等. 基于时序信息的模糊Petri网电网故障诊断方法[J]. 电工技术学报, 2017, 32(14): 229-237. Cheng Xuezhen, Lin Xiaoxiao, Zhu Chunhua, et al.Power system fault analysis based on hierarchical fuzzy Petri net considering time association character[J]. Transactions of China Electrotechnical Society, 2017, 32(14): 229-237. [20] 夏昌浩, 胡爽, 李伶俐, 等. 基于贝叶斯-粗糙集理论的诊断模型及诊断方法的应用[J]. 三峡大学学报(自然科学版), 2020, 42(5): 82-87. Xia Changhao, Hu Shuang, Li Lingli, et al.Application of diagnosis model and diagnosis method based on Bayesian-Rough set theory[J]. Journal of China Three Gorges University (Natural Sciences), 2020, 42(5): 82-87. [21] He Jun, Yuan Zhijun, Yang Xiaoling, et al.Reliability modeling and evaluation of urban multi-energy systems: a review of the state of the art and future challenges[J]. IEEE Access, 2020(8): 98887-98909. [22] 陈娟伟, 余涛, 许悦, 等. 气电耦合综合能源系统供电可靠性评估解析算法[J]. 电力系统自动化, 2018, 42(24): 59-69. Chen Juanwei, Yu Tao, Xu Yue, et al.Analytic method for power system reliability assessment of electricity-gas coupled energy system[J]. Automation of Electric Power Systems, 2018, 42(24): 59-69. [23] 唐文虎, 牛哲文, 赵柏宁, 等. 数据驱动的人工智能技术在电力设备状态分析中的研究与应用[J]. 高电压技术, 2020, 46(9): 2985-2999. Tang Wenhu, Niu Zhewen, Zhao Boning, et al.Research and application of data-driven artificial intelligence technology for condition analysis of power equipment[J]. High Voltage Engineering, 2020, 46(9): 2985-2999. [24] 黄天恩, 郭庆来, 孙宏斌, 等. 模型-数据混合驱动的电网安全特征选择和知识发现关键技术与工程应用[J]. 电力系统自动化, 2019, 43(1): 95-104. Huang Tianen, Guo Qinglai, Sun Hongbin, et al.Hybrid model and data driven concepts for power system security feature selection and knowledge discovery: key technologies and engineering application[J]. Automation of Electric Power Systems, 2019, 43(1): 95-104. [25] 程乐峰, 余涛, 张孝顺, 等. 机器学习在能源与电力系统领域的应用和展望[J]. 电力系统自动化, 2019, 43(1): 15-31. Cheng Lefeng, Yu Tao, Zhang Xiaoshun, et al.Machine learning for energy and electric power systems: state of the art and prospects[J]. Automation of Electric Power System, 2019, 43(1): 15-31. [26] 李更丰, 黄玉雄, 别朝红, 等. 综合能源系统运行可靠性评估综述及展望[J]. 电力自动化设备, 2019, 39(8): 12-21. Li Gengfeng, Huang Yuxiong, Bie Zhaohong, et al.Review and prospect of operational reliability evaluation of integrated energy system[J]. Electric Power Automation Equipment, 2019, 39(8): 12-21. [27] 朱继忠, 董瀚江, 李盛林, 等. 数据驱动的综合能源系统负荷预测综述[J]. 中国电机工程学报, 2021, 41(23): 7905-7923. Zhu Jizhong, Dong Hanjiang, Li Shenglin, et al.Review of data-driven load forecasting for integrated energy system[J]. Proceedings of th CSEE, 2021, 41(23): 7905-7923 [28] 杨慧敏, 崔江, 张卓然, 等. 基于改进支持向量机的故障诊断方法[J]. 电工技术学报, 2014, 29(增刊1): 164-169. Yang Huimin, Cui Jiang, Zhang Zhuoran, et al.A fault diagnosis method of power electronic circuits[J]. Transcations of China Electrotechnical Society, 2014, 29(S1): 164-169. [29] 李赢, 舒乃秋. 基于模糊聚类和完全二叉树支持向量机的变压器故障诊断[J]. 电工技术学报, 2016, 31(4): 64-70. Li Ying, Shu Naiqiu.Transformer fault diagnosis based on fuzzy clustering and complete binary tree support vector machine[J]. Transcations of China Electrotechnical Society, 2016, 31(4): 64-70. [30] 卫志农, 廖星星, 薛溟枫, 等. 计及相关性及改进交叉熵法的电-热互联综合能源系统风险评估[J]. 电网技术, 2021, 45(4): 1295-1304. Wei Zhinong, Liao Xingxing, Xue Mingfeng, et al.Risk assessment of integrated electricity-heat energy system with improved correlation cross entropy method[J]. Power System Technology, 2021, 45(4): 1295-1304. [31] Khodayar M, Liu Guangyi, Wang Jianhui, et al.Deep learning in power systems research: a review[J]. CSEE Journal of Power and Energy Systems, 2021, 7(2): 209-220. [32] Cao Jun, Fan Zhong.Deep learning-based online small signal stability assessment of power systems with renewable generation[C]//2018 IEEE Smart World, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation, Guangzhou, China, 2018: 216-221. [33] 刘辉海, 赵星宇, 赵洪山, 等. 基于深度自编码网络模型的风电机组齿轮箱故障检测[J]. 电工技术学报, 2017, 32(17): 156-163. Liu Huihai, Zhao Xingyu, Zhao Hongshan, et al.Fault detection of wind turbine gearbox based on deep autoencoder network[J]. Transcations of China Electrotechnical Society, 2017, 32(17): 156-163. [34] Xu Zhong, Mo Wenxiong, Wang Yong, et al.Transformer fault diagnosis based on deep brief sparse autoencoder[C]//2019 Chinese Control Conference (CCC), Guangzhou, China, 2019: 7432-7435. [35] Shi Zhongtuo, Yao Wei, Zeng Lingkang, et al.Convolutional neural network-based power system transient stability assessment and instability mode prediction[J]. Applied Energy, 2020, 263(1): 1-13. [36] 张世栋, 左新斌, 孙勇, 等. 一种配电主设备分布式状态传感器可靠性评估方法[J]. 华北电力大学学报(自然科学版), 2021, 48(1): 33-41. Zhang Shidong, Zuo Xinbin, Sun Yong, et al.Reliability evaluation method for distributed state sensors of distribution main equipment[J]. Journal of North China Electric Power University (Natural Science Edition), 2021, 48(1): 33-41. [37] Yang Weiquan, Zhu Yuanzhen, Liu Yutian.Fast assessment of short-term voltage stability of AC/DC power grid based on CNN[C]//2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Macau, China, 2019: 1-4. [38] Zhang Senlin, Wang Yixing, Liu Meiqin, et al.Data-based line trip fault prediction in power systems using LSTM networks and SVM[J]. IEEE Access, 2018(6): 7675-7686. [39] 余涛, 刘靖, 胡细兵. 基于分布式多步回溯Q(λ)学习的复杂电网最优潮流算法[J]. 电工技术学报, 2012, 27(4): 185-192. Yu Tao, Liu Jing, Hu Xibing, et al.Optimal power flow for complex power grid using distributed multi-step backtrack Q(λ) learning[J]. Transcations of China Electrotechnical Society, 2012, 27(4): 185-192. [40] Wang Zhisheng, Chen Ying, Liu Feng, et al.Power system security under false data injection attacks with exploitation and exploration based on reinforcement learning[J]. IEEE Access, 2018(6): 48785-48796. [41] Kosuru R, Chen Pengcheng, Liu Shichao.A reinforcement learning based power system stabilizer for a grid connected wind energy conversion system[C]//2020 IEEE Electric Power and Energy Conference (EPEC), Edmonton, AB, Canada, 2020: 1-5. [42] Teimourzadeh H, Moradzadeh A, Shoaran M, et al.High impedance single-phase faults diagnosis in transmission lines via deep reinforcement learning of transfer Functions[J]. IEEE Access, 2021(9): 15796-15809. [43] Yan Ziming, Xu Yan.Real-time optimal power flow: a lagrangian based deep reinforcement learning approach[J]. IEEE Transactions on Power Systems, 2020, 35(4): 3270-3273. [44] Ren Junyu, Chen Jinfu, Li Benyu, et al.A method for power system transient stability assessment based on transfer learning[C]//2020 IEEE Power & Energy Society General Meeting (PESGM), Montreal, QC, Canada, 2020: 1-5. [45] Ren Chao, Xu Yan.Transfer learning-based power system online dynamic security assessment: using one model to assess many unlearned faults[J]. IEEE Transactions on Power Systems, 2020, 35(1): 821-824. [46] Urgun D, Singh C.Composite system reliability analysis using deep learning enhanced by transfer learning[C]//2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Liege, Belgium, 2020: 1-6. [47] 汤奕, 崔晗, 李峰, 等. 人工智能在电力系统暂态问题中的应用综述[J]. 中国电机工程学报, 2019, 39(1): 2-13, 315. Tang Yi, Cui Han, Li Feng, et al.Review on artificial intelligence in power system transient stability analysis[J]. Proceedings of the CSEE, 2019, 39(1): 2-13, 315. [48] Wang Qi, Li Feng, Tang Yi, et al.Integrating model-driven and data-driven methods for power system frequency stability assessment and control[J]. IEEE Transactions on Power Systems, 2019, 34: 4557-4568. [49] Benjio Y, Lodi A, Prouvost A.Machine learning for combinatorial optimization: a methodological tour d’horizon[J]. European Journal of Operational Research, 2021(290): 405-421. [50] Rocco S, Moreno J.System reliability evaluation using Monte Carlo & support vector machine[C]//49th Annual Reliability and Maintainability Symposium (RAMS), Tampa, Florida, 2003: 482-486. [51] Li Wenyuan, Zhou Jiaqi, Xie Kaigui, et al.Power system risk assessment using a hybrid method of fuzzy set and monte carlo simulation[J]. IEEE Transactions on Power Systems, 2008, 23(2): 336-343. [52] 余娟, 马梦楠, 郭林, 等. 含电转气的电-气互联系统可靠性评估[J]. 中国电机工程学报, 2018, 38(3): 708-715. Yu Juan, Ma Mengnan, Guo Lin, et al.Reliability evaluation of integrated electrical and natural-gas system with power-to-gas[J]. Proceedings of the CSEE, 2018, 38(3): 708-715. [53] 靳尧飞, 应雨龙, 李靖超, 等. 基于模型与数据混合驱动的燃气轮机气路故障诊断方法[J]. 热力发电, 2021, 50(9): 66-71, 93. Jin Yaofei, Ying Yulong, Li Jingchao, et al.A gas turbine gas path fault diagnosis method based on model and data hybrid driven[J]. Thermal Power Generation, 2021, 50(9): 66-71, 93. [54] 宋晓通, 徐文悦, 周京华, 等. 面向微电网时序多态不确定性的选择性数据驱动[J]. 高电压技术, 2020, 46(7): 2370-2380. Song Xiaotong, Xu Wenyue, Zhou Jinghua, et al.Selective data-driven algorithm proposed for reliability evaluation of microgrid with sequential multi-state uncertainties[J]. High Voltage Engineering, 2020, 46(7): 2370-2380. [55] 黎静华, 骆怡辰, 杨舒惠, 等. 可再生能源电力不确定性预测方法综述[J]. 高电压技术, 2021, 47(4): 1144-1157. Li Jinghua, Luo Yichen, Yang Shuhui, et al.Review of uncertainty forecasting methods for renewable energy power[J]. High Voltage Engineering, 2021, 47(4): 1144-1157. [56] Han Shuang, Zhang Luna, Liu Yongqian, et al.Quantitative evaluation method for the complementarity of wind-solar-hydro power and optimization of wind-solar ratio[J]. Applied Energy, 2019, 236(15): 973-984. [57] 任惠, 王根旺, 唐焕新, 等. 基于数据驱动的输电网节点停电风险评估指标研究[J]. 电力系统及其自动化学报, 2021, 33(6): 51-59. Ren Hui, Wang Genwang, Tang Huanxin, et al.Study based on the nodal outage risk index of transmission system through data-driven techniques[J]. Proceedings of the CSU-EPSA, 2021, 33(6): 51-59. [58] 陈彬彬, 孙宏斌, 陈瑜玮, 等. 综合能源系统分析的统一能路理论(一): 气路[J]. 中国电机工程学报, 2020, 40(2): 436-444. Chen Binbin, Sun Hongbin, Chen Yuwei, et al.Energy circuit theory of integrated energy system analysis(I): gaseous circuit[J]. Proceedings of the CSEE, 2020, 40(2): 436-444. [59] 罗毅, 李昱龙. 基于熵权法和灰色关联分析法的输电网规划方案综合决策[J]. 电网技术, 2013, 37(1): 77-81. Luo Yi, Li Yulong.Comprehensive decision-making of transmission network planning based om entropy weight and grey relational analysis[J]. Power System Technology, 2013, 7(1): 77-81. [60] 董福贵, 张也, 尚美美. 分布式能源系统多指标综合评价研究[J]. 中国电机工程学报, 2016, 36(12): 3214-3223. Dong Fugui, Zhang Ye, Shang Meimei.Multi-criteria comprehensive evaluation of distributed energy system[J]. Proceedings of the CSEE, 2016, 36(12): 3214-3223. [61] 郭艳飞, 任雪桂, 鞠力, 等. 基于层次分析法的综合能源系统能效评估方法研究及应用[J]. 电力科学与技术学报, 2018, 33(4): 121-128. Guo Yanfei, Ren Xuegui, Ju Li, et al.The comprehensive efficiency evaluation method for integrated energy system based on AHP[J]. Journal of Electric Power Science and Technology, 2018, 33(4): 121-128. [62] 吴强, 程林, 黄河, 等. 基于层次分析法的能源互联网综合能效评估方法[J]. 电气应用, 2017, 36(17): 62-68. Wu Qiang, Cheng Lin, Huang He, et al.Comprehensive energy efficiency evaluation method of energy Internet based on analytic hierarchy process[J]. Electrical Application, 2017, 36(17): 62-68. [63] Saaty T.Decision making-the analytic hierarchy and network processes(AHP/ANP)[J]. Journal of Systems Science and Systems Engineering, 2004(13): 1-35. [64] 杨志超, 张成龙, 葛乐, 等. 基于熵权法的绝缘子污闪状态模糊综合评价[J]. 电力自动化设备, 2014, 34(4): 90-94. Yang Zhichao, Zhang Chenglong, Ge Le, et al.Comprehensive fuzzy evaluation based on entropy weight method for insulator flashover pollution[J]. Electric Power Automation Equipment, 2014, 34(4): 90-94. [65] 穆永铮, 鲁宗相, 乔颖, 等. 基于多算子层次分析模糊评价的电网安全与效益综合评价指标体系[J]. 电网技术, 2015, 39(1): 23-28. Mu Yongzheng, Lu Zongxiang, Qiao Ying, et al.A comprehensive evaluation index system of power grid security and benefit based on multi-operator fuzzy hierarchy evaluation method[J]. Power System Technology, 2015, 39(1): 23-28. [66] 马丽叶, 卢志刚, 胡华伟. 基于区间数的城市配电网经济运行模糊综合评价[J]. 电工技术学报, 2012, 27(8): 163-171. Ma Liye, Lu Zhigang, Hu Huawei.A fuzzy comprehensive evaluation method for economic operation of urban distribution network based on interval number[J]. Transactions of China Electrotechnical Society, 2012, 27(8): 163-171. [67] 陈柏森, 廖清芬, 刘涤尘, 等. 区域综合能源系统的综合评估指标与方法[J]. 电力系统自动化, 2018, 42(4): 174-182. Chen Baisen, Liao Qingfen, Liu Dichen, et al.Comprehensive evaluation indices and methods for regional integrated energy system[J]. Automation of Electric Power Systems, 2018, 42(4): 174-182. [68] 张涛, 朱彤, 高乃平, 等. 分布式冷热电能源系统优化设计及多指标综合评价方法的研究[J]. 中国电机工程学报, 2015, 35(14): 3706-3713. Zhang Tao, Zhu Tong, Gao Naiping, et al.Optimization design and multi-criteria comprehensive evaluation method of combined cooling heating and power system[J]. Proceedings of the CSEE, 2015, 35(14): 3706-3713. [69] 李慧玲, 芦新波, 刘大川, 等. 基于AHP-TOPSIS的电力能效项目综合评价[J]. 现代电力, 2014, 31(4): 88-94. Li Huiling, Lu Xinbo, Liu Dachuan, et al.A comprehensive evaluation method for power energy efficiency project based on AHP-TOPSIS[J]. Modern Electric Power, 2014, 31(4): 88-94. [70] 陈琪华, 何育恒, 曾永忠, 等. TOPSIS法在垃圾焚烧发电锅炉蒸汽空气预热器综合评价中的应用[J]. 中国电机工程学报, 2020, 40(4): 1274-1281, 1418. Chen Qihua, He Yuheng, Zeng Yongzhong, et al.Application of TOPSIS method in the comprehensive evaluation of eteam-air preheater on waste incineration boiler[J]. Proceedings of the CSEE, 2020, 40(4): 1274-1281, 1418. [71] 葛磊蛟, 张东, 王旭东, 等. 智能电网示范工程项目评估的模糊层次分析法[J]. 电力系统及其自动化学报, 2017, 29(9): 54-60. Ge Leijiao, Zhang Dong, Wang Xudong, et al.Fuzzy AHP method for the assessment on smart grid demonstration project[J]. Proceedings of the CSU-EPSA, 2017, 29(9): 54-60.
2022, Vol. 37 
