-
摘要: 電力系統預測主要包括負荷預測、出力預測以及健康狀態預測等。通過負荷預測，可以優化電力生產規劃，從而更好地實現電能的精細化分配；通過出力預測，可以有效提升新能源電力消納能力，實現電能的充分及合理利用；通過電力設備健康狀態預測，可以及時發現設備運行隱患，從而進一步保障電力系統平穩安全運行。深度學習憑借其卓越的特征分析和預測能力，被廣泛應用于電力系統運行及維護。本文首先歸納介紹了電力系統預測深度學習模型的特點、適用場景；其次，梳理了深度學習在面向民用及工業場景負荷預測、光伏及風電出力預測、機械及非機械設備健康狀態預測中的應用前沿；最后，對深度學習在電力系統預測中所面臨的關鍵問題、發展趨勢進行了總結和展望。Abstract: Power system is one of the largest and complex artificial engineering in the modern society. With the development of intelligence, digitization and long-distance technology, a large number of multi-source, multi-state and heterogeneous operational data have emerged. As a new trend direction of machine learning, deep learning has shown potential in data feature extraction and pattern recognition. Because of its excellent ability in data analysis and prediction, it is widely used in power system, which has a significant impact on optimizing power production planning, improving power production efficiency and energy utilization, and ensuring the smooth operation of the system influence. Based on massive quantities of data and by means of deep learning, it can better fit the nonlinear relationship between the factors affecting the subsequent operational state of the system, so as to further improve the prediction accuracy. Power system prediction includes load forecasting, new energy power prediction and state-of-health prediction. Power production planning can be optimized using load forecasting; thus, electrical energy can be finely dispatched. The capacity of new energy power consumption is improved through power prediction to reasonably use electrical energy. Potential equipment hazards can be timely found using power equipment health state prediction, thereby ensuring safe and smooth operation. First, in this paper, the characteristics and applicable scenarios of typical deep learning models are introduced, among them, deep belief network and stacked auto encoder belong to stack structure, so the structure is flexible and easy to expand, which is suitable for the modeling and feature extraction of unrelated data type; convolutional neural network shares convolution kernel internally to reduce the number of network parameters and is good at processing high-dimensional data type; recurrent neural network has feedforward and feedback connections, so it is suitable for processing sequence data with pre and post dependence. Second, the application frontiers of predictive power systems based on deep learning are reviewed, which include civil and industrial scenarios, photovoltaic and wind power, mechanical and non-mechanical equipment health state prediction. Finally, facing the challenges of power system in energy efficient allocation, high proportion of new energy power consumption, highly stable operation of power equipment and so on, the key problems and future development trends are presented.
-
Key words:
- power system /
- deep learning /
- load forecasting /
- new energy power prediction /
- health state prediction
-
圖 1 基于雙向LSTM-GRU的居民區用電負荷預測
Figure 1. Residential area load forecasting based on bidirectional LSTM-GRU
圖 2 基于CNN和頻域分解的光伏出力預測
Figure 2. Photovoltaic power forecasting based on CNN and frequency-domain decomposition
圖 3 基于DBN和奇異譜分析的風電出力預測
Figure 3. Wind power forecasting based on DBN and singular spectrum analysis
圖 4 基于SDA的發電機軸承健康階段劃分及健康狀態預測
Figure 4. Health stage segment and health state prediction of generator bearing based on SDA
表 1 典型的深度學習預測模型對比
Table 1. Comparison of typical deep learning prediction models
Model Main feature Apply data type Typical application scenario DBN Unsupervised learning
No need for large number of label data, and the training difficulty is lowSequence data without correlation before and after
(Time series data)Load forecasting, power prediction, equipment health state prediction CNN Supervised learning
Random initial value, sample data without preprocessingSequence data without correlation before and after,
(Multidimensional data)Load forecasting under multi energy spatiotemporal coupling, power prediction considering spatiotemporal correlation, health state forecasting RNN Supervised learning
Both feedforward and feedback connections are includedSequence data correlated before and after Load forecasting and power prediction under the scenario of severe power fluctuation SAE Unsupervised learning
Asymmetric connection, simple structure, easy to expandSequence data without correlation before and after Power prediction, equipment health state prediction 
下載: 導出CSV
www.77susu.com<span id="fpn9h"><noframes id="fpn9h"><span id="fpn9h"></span> <span id="fpn9h"><noframes id="fpn9h"> <th id="fpn9h"></th> <strike id="fpn9h"><noframes id="fpn9h"><strike id="fpn9h"></strike> <th id="fpn9h"><noframes id="fpn9h"> <span id="fpn9h"><video id="fpn9h"></video></span> <ruby id="fpn9h"></ruby> <strike id="fpn9h"><noframes id="fpn9h"><span id="fpn9h"></span> -
參考文獻
[1] Goodfellow I, Bengio Y, Courville A. Deep Learning. Cambridge: The MIT Press, 2016 [2] Wang L, Tao R, Hu H L, et al. Effective wind power prediction using novel deep learning network: Stacked independently recurrent autoencoder. Renew Energy, 2021, 164: 642 doi: 10.1016/j.renene.2020.09.108 [3] Ahmad T, Chen H X. Deep learning for multi-scale smart energy forecasting. Energy, 2019, 175: 98 doi: 10.1016/j.energy.2019.03.080 [4] Chen J F, Zhu Q M, Li H Y, et al. Learning heterogeneous features jointly: A deep end-to-end framework for multi-step short-term wind power prediction. IEEE Trans Sustain Energy, 2020, 11(3): 1761 doi: 10.1109/TSTE.2019.2940590 [5] Wang G M, Qiao J F, Guan L N, et al. Review and prospect on deep belief network. Acta Autom Sin, 2021, 47(1): 35 doi: 10.16383/j.aas.c190102王功明, 喬俊飛, 關麗娜, 等. 深度信念網絡研究現狀與展望. 自動化學報, 2021, 47(1):35 doi: 10.16383/j.aas.c190102 [6] Li H Z, Fu G, Sun W Q. Short-term wind power forecasting method based on deep recurrent belief network. Autom Electr Power Syst, 2021, 45(15): 85 doi: 10.7500/AEPS20201016005李宏仲, 付國, 孫偉卿. 基于深度遞歸信念網絡的風電功率短期預測方法. 電力系統自動化, 2021, 45(15):85 doi: 10.7500/AEPS20201016005 [7] Chang G W, Lu H J. Integrating gray data preprocessor and deep belief network for day-ahead PV power output forecast. IEEE Trans Sustain Energy, 2020, 11(1): 185 doi: 10.1109/TSTE.2018.2888548 [8] Cheng Q, Pei X B, Fang Z, et al. Short-term load forecasting method for low voltage users based on deep belief neural network // 2020 IEEE Sustainable Power and Energy Conference. Chengdu, 2020: 2564 [9] Gu Y H, Liu S, He L F, et al. Research on failure prediction using DBN and LSTM neural network // 2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE). Nara, 2018: 1705 [10] Ganguly B, Chaudhuri S, Biswas S, et al. Wavelet kernel-based convolutional neural network for localization of partial discharge sources within a power apparatus. IEEE Trans Ind Inform, 2021, 17(3): 1831 [11] Wang C, Kou P. Wind speed forecasts of multiple wind turbines in a wind farm based on integration model built by convolutional neural network and simple recurrent unit. Trans China Electrotech Soc, 2020, 35(13): 2723王晨, 寇鵬. 基于卷積神經網絡和簡單循環單元集成模型的風電場內多風機風速預測. 電工技術學報, 2020, 35(13):2723 [12] Gao W, Guo M F, Xu L B. Recognition method of internal overvoltage type for distribution network via improved CWD-CNN. Electr Mach Control, 2020, 24(8): 131高偉, 郭謀發, 許立彬. 基于改進CWD-CNN的配電網內部過電壓類型識別方法. 電機與控制學報, 2020, 24(8):131 [13] Li R, Sun F, Ding X, et al. Ultra short-term load forecasting for user-level integrated energy system considering multi-energy spatio-temporal coupling. Power Syst Technol, 2020, 44(11): 4121栗然, 孫帆, 丁星, 等. 考慮多能時空耦合的用戶級綜合能源系統超短期負荷預測方法. 電網技術, 2020, 44(11):4121 [14] Xu Z, Zhang G, Wang H M, et al. Error compensation of collaborative robot dynamics based on deep recurrent neural network. Chin J Eng, 2021, 43(7): 995徐征, 張弓, 汪火明, 等. 基于深度循環神經網絡的協作機器人動力學誤差補償. 工程科學學報, 2021, 43(7):995 [15] Yang C, Jiang W X, Guo Z W. Time series data classification based on dual path CNN-RNN cascade network. IEEE Access, 2019, 7: 155304 doi: 10.1109/ACCESS.2019.2949287 [16] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9(8): 1735 doi: 10.1162/neco.1997.9.8.1735 [17] Xia M, Shao H D, Ma X D, et al. A stacked GRU-RNN-based approach for predicting renewable energy and electricity load for smart grid operation. IEEE Trans Ind Inform, 2021, 17(10): 7050 doi: 10.1109/TII.2021.3056867 [18] Tan M, Yuan S P, Li S H, et al. Ultra-short-term industrial power demand forecasting using LSTM based hybrid ensemble learning. IEEE Trans Power Syst, 2020, 35(4): 2937 doi: 10.1109/TPWRS.2019.2963109 [19] Chen M R, Zeng G Q, Lu K D, et al. A two-layer nonlinear combination method for short-term wind speed prediction based on ELM, ENN, and LSTM. IEEE Internet Things J, 2019, 6(4): 6997 doi: 10.1109/JIOT.2019.2913176 [20] Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets. Neural Comput, 2006, 18(7): 1527 doi: 10.1162/neco.2006.18.7.1527 [21] Liu R M, Feng S Y, Cai Y M, et al. State assessment and fault prediction method of distribution terminal based on SDAE and hierarchical Bayesian // 2019 IEEE Sustainable Power and Energy Conference. Beijing, 2019: 2783 [22] Fu Y W, Chai H, Zhen Z, et al. Sky image prediction model based on convolutional auto-encoder for minutely solar PV power forecasting. IEEE Trans Ind Appl, 2021, 57(4): 3272 doi: 10.1109/TIA.2021.3072025 [23] Zhu J C, Yang Z L, Chang Y, et al. A novel LSTM based deep learning approach for multi-time scale electric vehicles charging load prediction // 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). Chengdu, 2019: 3531 [24] Xiao B, Zhou C, Mu G. Review and prospect of the spatial load forecasting methods. Proc CSEE, 2013, 33(25): 78 doi: 10.13334/j.0258-8013.pcsee.2013.25.026肖白, 周潮, 穆鋼. 空間電力負荷預測方法綜述與展望. 中國電機工程學報, 2013, 33(25):78 doi: 10.13334/j.0258-8013.pcsee.2013.25.026 [25] Yang Z Y, Liu J Y, Liu Y B, et al. Transformer load forecasting based on adaptive deep belief network. Proc CSEE, 2019, 39(14): 4049楊智宇, 劉俊勇, 劉友波, 等. 基于自適應深度信念網絡的變電站負荷預測. 中國電機工程學報, 2019, 39(14):4049 [26] Shi J Q, Tan T, Guo J, et al. Multi-task learning based on deep architecture for various types of load forecasting in regional energy system integration. Power Syst Technol, 2018, 42(3): 698 doi: 10.13335/j.1000-3673.pst.2017.2368史佳琪, 譚濤, 郭經, 等. 基于深度結構多任務學習的園區型綜合能源系統多元負荷預測. 電網技術, 2018, 42(3):698 doi: 10.13335/j.1000-3673.pst.2017.2368 [27] Tan M, Liu Y, Meng B M, et al. Multinodal forecasting of industrial power load using participation factor and ensemble learning // 2020 IEEE 4th Conference on Energy Internet and Energy System Integration. Wuhan, 2020: 745 [28] Zhao Y, Wang H M, Kang L, et al. Temporal convolution network-based short-term electrical load forecasting. Trans China Electrotech Soc, 2022, 37(5): 1242趙洋, 王瀚墨, 康麗, 等. 基于時間卷積網絡的短期電力負荷預測. 電工技術學報, 2022, 37(5):1242 [29] Xu J H, Wang X W, Yang J J. Short-term load density prediction based on CNN-QRLightGBM. Power Syst Technol, 2020, 44(9): 3409許佳輝, 王向文, 楊俊杰. 基于CNN-QRLightGBM的短期負荷概率密度預測. 電網技術, 2020, 44(9):3409 [30] Cheng L L, Zang H X, Xu Y, et al. Probabilistic residential load forecasting based on micrometeorological data and customer consumption pattern. IEEE Trans Power Syst, 2021, 36(4): 3762 doi: 10.1109/TPWRS.2021.3051684 [31] Wei A, Mao D J, Han W L, et al. Short-term load forecasting based on EMD and long short-term memory neural networks. J Eng Therm Energy Power, 2020, 35(4): 203 doi: 10.16146/j.cnki.rndlgc.2020.04.028魏驁, 茅大鈞, 韓萬里, 等. 基于EMD和長短期記憶網絡的短期電力負荷預測研究. 熱能動力工程, 2020, 35(4):203 doi: 10.16146/j.cnki.rndlgc.2020.04.028 [32] Wang Z P, Zhao B, Ji W J, et al. Short-term load forecasting method based on GRU-NN model. Autom Electr Power Syst, 2019, 43(5): 53王增平, 趙兵, 紀維佳, 等. 基于GRU-NN模型的短期負荷預測方法. 電力系統自動化, 2019, 43(5):53 [33] Wang Z P, Zhao B, Jia X, et al. Short-term power load forecasting method based on difference decomposition and error compensation. Power Syst Technol, 2021, 45(7): 2560王增平, 趙兵, 賈欣, 等. 基于差分分解和誤差補償的短期電力負荷預測方法. 電網技術, 2021, 45(7):2560 [34] Chen H W, Wang S X, Wang S M, et al. Aggregated load forecasting method based on gated recurrent unit networks and model fusion. Autom Electr Power Syst, 2019, 43(1): 65陳海文, 王守相, 王紹敏, 等. 基于門控循環單元網絡與模型融合的負荷聚合體預測方法. 電力系統自動化, 2019, 43(1):65 [35] Ma Y, Zhang Q, Ding J J, et al. Short term load forecasting based on iForest-LSTM // IEEE 14th Conference on Industrial Electronics and Applications. Xi’an, 2019: 2278 [36] Eskandari H, Imani M, Moghaddam M P. Convolutional and recurrent neural network based model for short-term load forecasting. Electr Power Syst Res, 2021, 195: 107173 doi: 10.1016/j.jpgr.2021.107173 [37] Sun M Y, Zhang T Q, Wang Y, et al. Using Bayesian deep learning to capture uncertainty for residential net load forecasting. IEEE Trans Power Syst, 2020, 35(1): 188 doi: 10.1109/TPWRS.2019.2924294 [38] Salakhutdinov R, Hinton G. Using deep belief nets to learn covariance kernels for Gaussian processes // Proceedings of the 20th International Conference on Neural Information Processing Systems. New York, 2007: 1249 [39] Peng W, Xu L W, Li C D, et al. Stacked autoencoders and extreme learning machine based hybrid model for electrical load prediction. J Intell Fuzzy Syst, 2019, 37(4): 5403 doi: 10.3233/JIFS-190548 [40] Gal Y, Ghahramani Z. Dropout as a bayesian approximation: Representing model uncertainty in deep learning // Proceedings of the 33rd International Conference on International Conference on Machine Learning. New York, 2016: 1050 [41] Mitra P, Vittal V, Pourbeik P, et al. Load sensitivity studies in power systems with non-smooth load behavior. IEEE Trans Power Syst, 2017, 32(1): 705 doi: 10.1109/TPWRS.2016.2554398 [42] Liu Y, Zhang H, Zhang A M. Improved load forecasting method based on load characteristics under demand-side response. Power Syst Prot Control, 2018, 46(13): 126 doi: 10.7667/PSPC170167劉云, 張杭, 張愛民. 需求側響應下基于負荷特性的改進短期負荷預測方法. 電力系統保護與控制, 2018, 46(13):126 doi: 10.7667/PSPC170167 [43] Kong X Y, Li C, Zheng F, et al. Improved deep belief network for short-term load forecasting considering demand-side management. IEEE Trans Power Syst, 2020, 35(2): 1531 doi: 10.1109/TPWRS.2019.2943972 [44] Tan X Y, Liu F, Ma J J, et al. Ultra-short-term pv power forecasting model based on dbn and t-s time-varying weight combination. Acta Energiae Solaris Sin, 2021, 42(10): 42 doi: 10.19912/j.0254-0096.tynxb.2019-1029譚小鈺, 劉芳, 馬俊杰, 等. 基于DBN與T-S時變權重組合的光伏功率超短期預測模型. 太陽能學報, 2021, 42(10):42 doi: 10.19912/j.0254-0096.tynxb.2019-1029 [45] Lai C W, Li J H, Chen B, et al. Review of photovoltaic power output prediction technology. Trans China Electrotech Soc, 2019, 34(6): 1201 doi: 10.19595/j.cnki.1000-6753.tces.180326(賴昌偉, 黎靜華, 陳博, 等. 光伏發電出力預測技術研究綜述. 電工技術學報, 2019, 34(6):1201 doi: 10.19595/j.cnki.1000-6753.tces.180326 [46] Du J L, Liu Y Y, Liu Z J. Study of precipitation forecast based on deep belief networks. Algorithms, 2018, 11(9): 132 doi: 10.3390/a11090132 [47] Zhang C Y, Chen C L P, Gan M, et al. Predictive deep boltzmann machine for multiperiod wind speed forecasting. IEEE Trans Sustain Energy, 2015, 6(4): 1416 doi: 10.1109/TSTE.2015.2434387 [48] Khodayar M, Wang J H. Spatio-temporal graph deep neural network for short-term wind speed forecasting. IEEE Trans Sustain Energy, 2019, 10(2): 670 doi: 10.1109/TSTE.2018.2844102 [49] Zhang Y C, Le J, Liao X B, et al. A novel combination forecasting model for wind power integrating least square support vector machine, deep belief network, singular spectrum analysis and locality-sensitive hashing. Energy, 2019, 168: 558 doi: 10.1016/j.energy.2018.11.128 [50] Miao C X, Li H, Wang X, et al. Data-driven and deep-learning-based ultra-short-term wind power prediction. Autom Electr Power Syst, 2021, 45(14): 22 doi: 10.7500/AEPS20201127004苗長新, 李昊, 王霞, 等. 基于數據驅動和深度學習的超短期風電功率預測. 電力系統自動化, 2021, 45(14):22 doi: 10.7500/AEPS20201127004 [51] Zang H X, Cheng L L, Ding T, et al. A hybrid method for short-term photovoltaic power forecasting based on deep convolutional neural network. IET Gener Transm Distribution, 2018, 12(20): 4557 doi: 10.1049/iet-gtd.2018.5847 [52] Wang H Z, Yi H Y, Peng J C, et al. Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network. Energy Convers Manag, 2017, 153: 409 doi: 10.1016/j.enconman.2017.10.008 [53] Sun Y C, Sz?cs G, Brandt A R. Solar PV output prediction from video streams using convolutional neural networks. Energy Environ Sci, 2018, 11(7): 1811 doi: 10.1039/C7EE03420B [54] Zang H X, Cheng L L, Ding T, et al. Day-ahead photovoltaic power forecasting approach based on deep convolutional neural networks and meta learning. Int J Electr Power Energy Syst, 2020, 118: 105790 doi: 10.1016/j.ijepes.2019.105790 [55] Yan J C, Hu L, Zhen Z, et al. Frequency-domain decomposition and deep learning based solar PV power ultra-short-term forecasting model. IEEE Trans Ind Appl, 2021, 57(4): 3282 doi: 10.1109/TIA.2021.3073652 [56] Hong Y Y, Rioflorido C L P P. A hybrid deep learning-based neural network for 24-h ahead wind power forecasting. Appl Energy, 2019, 250: 530 doi: 10.1016/j.apenergy.2019.05.044 [57] Liu X, Yang L X, Zhang Z J. Short-term multi-step ahead wind power predictions based on A novel deep convolutional recurrent network method. IEEE Trans Sustain Energy, 2021, 12(3): 1820 doi: 10.1109/TSTE.2021.3067436 [58] Wang F, Xuan Z M, Zhen Z, et al. A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework. Energy Convers Manag, 2020, 212: 112766 doi: 10.1016/j.enconman.2020.112766 [59] Zhang Q, Ma Y, Li G L, et al. Applications of frequency domain decomposition and deep learning algorithms in short-term load and photovoltaic power forecasting. Proc CSEE, 2019, 39(8): 2221張倩, 馬愿, 李國麗, 等. 頻域分解和深度學習算法在短期負荷及光伏功率預測中的應用. 中國電機工程學報, 2019, 39(8):2221 [60] Song L Q, Xie Q Y, He Y K, et al. Ultra-short-term wind power combination forecasting model based on MEEMD-SAE-Elman // 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference. Chongqing, 2020: 1844 [61] Hossain M A, Chakrabortty R K, Elsawah S, et al. Predicting wind power generation using hybrid deep learning with optimization. IEEE Trans Appl Supercond, 2021, 31(8): 1 [62] Wang H Z, Wang G B, Li G Q, et al. Deep belief network based deterministic and probabilistic wind speed forecasting approach. Appl Energy, 2016, 182: 80 doi: 10.1016/j.apenergy.2016.08.108 [63] Jing B, Tan L N, Qian Z, et al. An overview of research progress of short-term photovoltaic forecasts. Electr Measur Instrum, 2017, 54(12): 1 doi: 10.3969/j.issn.1001-1390.2017.12.001荊博, 譚倫農, 錢政, 等. 光伏發電短期預測研究進展綜述. 電測與儀表, 2017, 54(12):1 doi: 10.3969/j.issn.1001-1390.2017.12.001 [64] Wang Y F, Fu Y C, Xue H. Ultra-short-term forecasting method of photovoltaic power generation based on Chaos-EEMD-PFBD decomposition and GA-BP neural networks. Acta Energiae Solaris Sin, 2020, 41(12): 55王育飛, 付玉超, 薛花. 基于Chaos-EEMD-PFBD分解和GA-BP神經網絡的光伏發電功率超短期預測法. 太陽能學報, 2020, 41(12):55 [65] Yan J, Gao C Y, Yu G B. Comparative analysis of prediction errors based on offshore wind power characteristics. Comput Integr Manuf Syst, 2020, 26(3): 648閆健, 高長元, 于廣濱. 基于海上風電功率特性的預測誤差對比分析. 計算機集成制造系統, 2020, 26(3):648 [66] Wang K J, Qi X X, Liu H D, et al. Deep belief network based k-means cluster approach for short-term wind power forecasting. Energy, 2018, 165: 840 doi: 10.1016/j.energy.2018.09.118 [67] Sun C, Ma M, Zhao Z B, et al. Deep transfer learning based on sparse autoencoder for remaining useful life prediction of tool in manufacturing. IEEE Trans Ind Inform, 2019, 15(4): 2416 doi: 10.1109/TII.2018.2881543 [68] Su L C, Xing M L, Zhang H. Fault detection of key components of wind turbine based on combination prediction model. Acta Energiae Solaris Sin, 2021, 42(10): 220蘇連成, 邢美玲, 張慧. 基于組合預測模型的風電機組關鍵部位故障檢測. 太陽能學報, 2021, 42(10):220 [69] Qi B, Wang Y M, Zhang P, et al. Deep recurrent belief network model for trend prediction of transformer oil chromatography data. Power Syst Technol, 2019, 43(6): 1892 doi: 10.13335/j.1000-3673.pst.2019.0030齊波, 王一鳴, 張鵬, 等. 面向變壓器油色譜趨勢預測的深度遞歸信念網絡. 電網技術, 2019, 43(6):1892 doi: 10.13335/j.1000-3673.pst.2019.0030 [70] Guo L, Lei Y G, Li N P, et al. Machinery health indicator construction based on convolutional neural networks considering trend burr. Neurocomputing, 2018, 292: 142 doi: 10.1016/j.neucom.2018.02.083 [71] Yang B Y, Liu R N, Zio E. Remaining useful life prediction based on a double-convolutional neural network architecture. IEEE Trans Ind Electron, 2019, 66(12): 9521 doi: 10.1109/TIE.2019.2924605 [72] Lu H, Vahid B, Nemani V P, et al. GAN-LSTM predictor for failure prognostics of rolling element bearings // IEEE International Conference on Prognostics and Health Management. Detroit, 2021: 1 [73] Dai J J, Song H, Sheng G H, et al. Prediction method for power transformer running state based on LSTM network. High Volt Eng, 2018, 44(4): 1099 doi: 10.13336/j.1003-6520.hve.20180329008代杰杰, 宋輝, 盛戈皞, 等. 采用LSTM網絡的電力變壓器運行狀態預測方法研究. 高電壓技術, 2018, 44(4):1099 doi: 10.13336/j.1003-6520.hve.20180329008 [74] Sun S G, Wen Z T, Du T H, et al. Remaining life prediction of conventional low-voltage circuit breaker contact system based on effective vibration signal segment detection and MCCAE-LSTM. IEEE Sens J, 2021, 21: 21862 doi: 10.1109/JSEN.2021.3104290 [75] Ma X, Hu H, Shang Y Z. A new method for transformer fault prediction based on multifeature enhancement and refined long short-term memory. IEEE Trans Instrum Meas, 2021, 70: 1 [76] Zhao H S, Liu H H, Hu W J, et al. Anomaly detection and fault analysis of wind turbine components based on deep learning network. Renew Energy, 2018, 127: 825 doi: 10.1016/j.renene.2018.05.024 [77] Lu B L, Liu Z H, Wei H L, et al. A deep adversarial learning prognostics model for remaining useful life prediction of rolling bearing. IEEE Trans Artif Intell, 2021, 2(4): 329 doi: 10.1109/TAI.2021.3097311 [78] Jiang G Q, Xie P, He H B, et al. Wind turbine fault detection using a denoising autoencoder with temporal information. IEEE/ASME Trans Mechatron, 2018, 23(1): 89 doi: 10.1109/TMECH.2017.2759301 [79] Meng Z, Zhan X Y, Li J, et al. An enhancement denoising autoencoder for rolling bearing fault diagnosis. Measurement, 2018, 130: 448 doi: 10.1016/j.measurement.2018.08.010 [80] Xia M, Li T, Shu T X, et al. A two-stage approach for the remaining useful life prediction of bearings using deep neural networks. IEEE Trans Ind Inform, 2019, 15(6): 3703 doi: 10.1109/TII.2018.2868687 [81] Sun Z X, Sun H X. Stacked denoising autoencoder with density-grid based clustering method for detecting outlier of wind turbine components. IEEE Access, 2019, 7: 13078 doi: 10.1109/ACCESS.2019.2893206 [82] Chen Z Q, Chen X D, Olivira J, et al. Application of deep learning in equipment prognostics and health management. Chin J Sci Instrum, 2019, 40(9): 206 doi: 10.19650/j.cnki.cjsi.J1905122陳志強, 陳旭東, Olivira J, 等. 深度學習在設備故障預測與健康管理中的應用. 儀器儀表學報, 2019, 40(9):206 doi: 10.19650/j.cnki.cjsi.J1905122 [83] Fang X L, Yang Q, Liu G F, et al. Power supply restoration strategy for offshore multi-platform interconnected power system with faults. Autom Electr Power Syst, 2021, 45(7): 53方曉倫, 楊強, 劉國鋒, 等. 海上多平臺互聯電力系統故障后的供電恢復策略. 電力系統自動化, 2021, 45(7):53 -
點擊查看大圖
圖(4) / 表(4)
計量
- 文章訪問數: 1020
- HTML全文瀏覽量: 598
- PDF下載量: 173
- 被引次數: 0
