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Frontiers in Energy

ISSN 2095-1701

ISSN 2095-1698(Online)

CN 11-6017/TK

邮发代号 80-972

2019 Impact Factor: 2.657

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Frontiers in Energy  2019, Vol. 13 Issue (1): 71-85   https://doi.org/10.1007/s11708-018-0589-4
  本期目录
人工智能技术在电力系统监测与运行中的应用
GAO David Wenzhong1, WANG Qiang2, ZHANG Fang3(), YANG Xiaojing2, HUANG Zhigang2, MA Shiqian5, LI Qiao4, GONG Xiaoyan6, WANG Fei-Yue6
1. 丹佛大学，美国丹佛 80210
2. 国网天津电力公司，中国天津 300010
3. 清华大学电力系统国家重点实验室，电气工程系，中国北京 100084
4. 国网天津电力研究院，中国天津30038
5. 中国科学院（SKL-MCCS，CASIA），中国北京 100190
6. Chinese Academy of Sciences (SKL-MCCS, CASIA), Beijing 100190, China
Application of AI techniques in monitoring and operation of power systems
David Wenzhong GAO1, Qiang WANG2, Fang ZHANG3(), Xiaojing YANG2, Zhigang HUANG2, Shiqian MA5, Qiao LI4, Xiaoyan GONG6, Fei-Yue WANG6
1. University of Denver, Denver, CO 80210, USA; China State Key Laboratory of Power System, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
2. State Grid Tianjin Electric Power Company, Tianjin 300010, China
3. China State Key Laboratory of Power System, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
4. University of Denver, Denver, CO 80210, USA
5. State Grid Tianjin Electric Power Research Institute, Tianjin 300384, China
6. Chinese Academy of Sciences (SKL-MCCS, CASIA), Beijing 100190, China
 全文: PDF(1176 KB)   HTML
摘要:

近年来，由于数据处理性能优异，人工智能（artificial intelligence，AI）技术在许多领域内快速发展。目前，人工智能技术在电力系统中的应用仍处于初期阶段。本文主要针对电力系统的运行和监测方面讨论人工智能技术在电力系统中的应用潜力。本文针对电力系统运行讨论了AI技术在控制、优化和决策方面存在的问题、需求和可能的应用。进而，研究了电力系统监测中的故障检测和稳定性分析问题。最后，提供了一个使用神经网络（neural network，NN）进行潮流分析的案例研究，以此作为一个简单的例子来说明AI技术在解决电力系统问题中的可行性。
Abstract：

In recent years, the artificial intelligence (AI) technology is becoming more and more popular in many areas due to its amazing performance. However, the application of AI techniques in power systems is still in its infancy. Therefore, in this paper, the application potentials of AI technologies in power systems will be discussed by mainly focusing on the power system operation and monitoring. For the power system operation, the problems, the demands, and the possible applications of AI techniques in control, optimization, and decision making problems are discussed. Subsequently, the fault detection and stability analysis problems in power system monitoring are studied. At the end of the paper, a case study to use the neural network (NN) for power flow analysis is provided as a simple example to demonstrate the viability of AI techniques in solving power system problems.
Key words： power system operation and monitoring    artificial intelligence (AI)    deep learning    power flow analysis
收稿日期: 2017-12-31      出版日期: 2019-03-20
通讯作者: ZHANG Fang     E-mail: thu.zhangfang@gmail.com
Corresponding Author(s): Fang ZHANG   
 引用本文:   
GAO David Wenzhong, WANG Qiang, ZHANG Fang, YANG Xiaojing, HUANG Zhigang, MA Shiqian, LI Qiao, GONG Xiaoyan, WANG Fei-Yue. 人工智能技术在电力系统监测与运行中的应用[J]. Frontiers in Energy, 2019, 13(1): 71-85.
David Wenzhong GAO, Qiang WANG, Fang ZHANG, Xiaojing YANG, Zhigang HUANG, Shiqian MA, Qiao LI, Xiaoyan GONG, Fei-Yue WANG. Application of AI techniques in monitoring and operation of power systems. Front. Energy, 2019, 13(1): 71-85.
 链接本文:  
https://academic.hep.com.cn/fie/CN/10.1007/s11708-018-0589-4
https://academic.hep.com.cn/fie/CN/Y2019/V13/I1/71
Fig.1  
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Fig.7  
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Fig.9  
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Fig.11  
Fig.12  
Fig.13  
Fig.14  
Fig.15  
Fig.16  
NN structure Accuracy (MSE) Time by NN Time by NR
20-121 0.0441 0.0103s 0.0855s
20-20-121 0.0802 0.0135s 0.0859s
20-20-20-121 0.1583 0.0139s 0.1336s
100-121 0.0143 0.0139s 0.0982s
100-100-121 0.1357 0.0196s 0.0925s
100-100-100-121 0.2149 0.0186s 0.1010s
300-121 0.0051 0.0147s 0.0841s
300-300-121 0.1897 0.0234s 0.0900s
300-300-300-121 0.1677 0.0242s 0.0960s
500-121 0.0084 0.0184 0.0883
1000-121 0.3315 0.0323 0.1495
Tab.1  
Bus # V/(p.u.) θ/rad PL/MW QL/Mvar PG/MW QG/Mvar
1 1 0 0 0 0 0
2 1 0 0 0 0 0
3 1 0 322 2.4 0 0
4 1 0 500 184 0 0
5 1 0 0 0 0 0
6 1 0 0 0 0 0
7 1 0 233.8 84 0 0
8 1 0 522 176 0 0
9 1 0 0 0 0 0
10 1 0 0 0 0 0
11 1 0 0 0 0 0
12 1 0 7.5 88 0 0
13 1 0 0 0 0 0
14 1 0 0 0 0 0
15 1 0 320 153 0 0
16 1 0 329 32.3 0 0
17 1 0 0 0 0 0
18 1 0 158 30 0 0
19 1 0 0 0 0 0
20 1 0 628 103 0 0
21 1 0 274 115 0 0
22 1 0 0 0 0 0
23 1 0 247.5 84.6 0 0
24 1 0 308.6 192 0 0
25 1 0 224 47.2 0 0
26 1 0 139 17 0 0
27 1 0 281 75.5 0 0
28 1 0 206 27.6 0 0
29 1 0 283.5 26.9 0 0
30 1.0475 0 0 0 250 0
31 0.982 0 9.2 4.6 0 0
32 0.9831 0 0 0 650 0
33 0.9972 0 0 0 632 0
34 1.0123 0 0 0 508 0
35 1.0493 0 0 0 650 0
36 1.0635 0 0 0 560 0
37 1.0278 0 0 0 540 0
38 1.0265 0 0 0 830 0
39 1.03 0 1104 250 1000 0
Tab.2  
Bus In Bus Out R/(p.u.) X/(p.u.) B/(p.u.) Tr. tap
1 2 0.0035 0.0411 0.6987 1
1 39 0.0010 0.0250 0.7500 1
2 3 0.0013 0.0151 0.2572 1
2 25 0.0070 0.0086 0.1460 1
3 4 0.0013 0.0213 0.2214 1
3 18 0.0011 0.0133 0.2138 1
4 5 0.0008 0.0128 0.1342 1
4 14 0.0008 0.0129 0.1382 1
5 6 0.0002 0.0026 0.0434 1
5 8 0.0008 0.0112 0.1476 1
6 7 0.0006 0.0092 0.1130 1
6 11 0.0007 0.0082 0.1389 1
7 8 0.0004 0.0046 0.0780 1
8 9 0.0023 0.0363 0.3804 1
9 39 0.0010 0.0250 1.2000 1
10 11 0.0004 0.0043 0.0729 1
10 13 0.0004 0.0043 0.0729 1
13 14 0.0009 0.0101 0.1723 1
14 15 0.0018 0.0217 0.3660 1
15 16 0.0009 0.0094 0.1710 1
16 17 0.0007 0.0089 0.1342 1
16 19 0.0016 0.0195 0.3040 1
16 21 0.0008 0.0135 0.2548 1
16 24 0.0003 0.0059 0.0680 1
17 18 0.0007 0.0082 0.1319 1
17 27 0.0013 0.0173 0.3216 1
21 22 0.0008 0.0140 0.2565 1
22 23 0.0006 0.0096 0.1846 1
23 24 0.0022 0.0350 0.3610 1
25 26 0.0032 0.0323 0.5130 1
26 27 0.0014 0.0147 0.2396 1
26 28 0.0043 0.0474 0.7802 1
26 29 0.0057 0.0625 1.0290 1
28 29 0.0014 0.0151 0.2490 1
12 11 0.0016 0.0435 0.0000 1.006
12 13 0.0016 0.0435 0.0000 1.006
6 31 0.0000 0.0250 0.0000 1.070
10 32 0.0000 0.0200 0.0000 1.070
19 33 0.0007 0.0142 0.0000 1.070
20 34 0.0009 0.0180 0.0000 1.009
22 35 0.0000 0.0143 0.0000 1.025
23 36 0.0005 0.0272 0.0000 1.000
25 37 0.0006 0.0232 0.0000 1.025
2 30 0.0000 0.0181 0.0000 1.025
29 38 0.0008 0.0156 0.0000 1.025
19 20 0.0007 0.0138 0.0000 1.060
Tab.3  
1 YLeCun, Y Bengio, GHinton. Deep learning. Nature, 2015, 521(7553): 436–444
https://doi.org/10.1038/nature14539
2 F YWang, P K Wong. Intelligent systems and technology for integrative and predictive medicine: an ACP approach. ACM Transactions on Intelligent Systems and Technology, 2013, 4(2): 32
https://doi.org/10.1145/2438653.2438667
3 FWang. Parallel control: a method for data-driven and computational control. Acta Automatica Sinica, 2013, 39(4): 293–302
https://doi.org/10.3724/SP.J.1004.2013.00293
4 LLi, Y L Lin, D P Cao, et al. Parallel learning—a new framework for machine learning. Acta Automatica Sinica, 2017, 43(1): 1–8
https://doi.org/10.16383/j.aas.2017.y000001
5 DSilver, A Huang, C JMaddison, et al. Mastering the game of go with deep neural networks and tree search. Nature, 2016, 529(7587): 484–489
https://doi.org/10.1038/nature16961
6 J LDeng, F Y Wang, Y B Chen, et al. From industries 4.0 to energy 5.0: concept and framework of intelligent energy systems. Acta Automatica Sinica, 2015, 41: 2003–2016
https://doi.org/10.16383/j.aas.2015.c150259
7 YWang, M Liu, ZBao. Deep learning neural network for power system fault diagnosis. In: 2016 35th Chinese Control Conference (CCC), Chengdu, China, 2016, 6678–6683
https://doi.org/10.1109/ChiCC.2016.7554408
8 TBi, Y Ni, CShen, et al. A novel ANN fault diagnosis system for power systems using dual GA loops in ANN training. In: 2000 Power Engineering Society Summer Meeting, Seattle, WA, USA, 2000, 425–430
https://doi.org/10.1109/PESS.2000.867624
9 YZhu, L Hou, JLu. Bayesian networks-based approach for power systems fault diagnosis. IEEE Transactions on Power Delivery, 2006, 21(2): 634–639
https://doi.org/10.1109/TPWRD.2005.858774
10 FRodrigues, C Cardeira, J M FCalado. The daily and hourly energy consumption and load forecasting using artificial neural network method: a case study using a set of 93 households in Portugal. Energy Procedia, 2014, 62: 220–229
https://doi.org/10.1016/j.egypro.2014.12.383
11 R FBerriel, A T Lopes, A Rodrigues, et al. Monthly energy consumption forecast: a deep learning approach. In: 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, 2017, 4283–4290
https://doi.org/10.1109/IJCNN.2017.7966398
12 K TWilliams, J D Gomez. Predicting future monthly residential energy consumption using building characteristics and climate data: a statistical learning approach. Energy and Building, 2016, 128: 1–11
https://doi.org/10.1016/j.enbuild.2016.06.076
13 AAlmalaq, G Edwards. A review of deep learning methods applied on load forecasting. In: 16th IEEE International Conference on Machine Learning and Applications ( ICMLA ), Los Ageles, CA, USA, 2017
https://doi.org/10.1109/ICMLA.2017.0-110
14 LLi, K Ota, MDong. Everything is image: CNN based short-term electrical load forecasting for smart grid. In: 2017 14th International Symposium on Pervasive Systems, Algorithms and Networks & 2017 11th International Conference on Frontier of Computer Science and Technology & 2017 Third International Symposium of Creative Computing (ISPAN-FCST-ISCC), Exeter, UK, 2017, 344–351
https://doi.org/10.1109/ISPAN-FCST-ISCC.2017.78
15 FFahiman, S M Erfani, S Rajasegarar, et al. Improving load forecasting based on deep learning and K-shape clustering. In: 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, 2017, 4134–4141
https://doi.org/10.1109/IJCNN.2017.7966378
16 HShi, M Xu, RLi. Deep learning for household load forecasting–a novel pooling deep RNN. IEEE Transactions on Smart Grid, 2017: 1–1
https://doi.org/10.1109/TSG.2017.2686012
17 RFayek, R Sweif. AI based reconfiguration technique for improving performance and operation of distribution power systems with distributed generators. In: 2013 4th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG), Istanbul, Turkey, 2013, 215–221
https://doi.org/10.1109/PowerEng.2013.6635609
18 ADavid, Z Rongda. Advances in AI applications for power system planning. In: 1st International Conference on Expert Planning Systems, Brighton, UK, 1990, 36–41
19 WLi, J Ying. Design and analysis artificial intelligence (AI) research for power supply—power electronics expert system (PEES). In: 23rd Applied Power Electronics Conference and Exposition, Austin, TX, USA, 2008, 2009–2015
https://doi.org/10.1109/APEC.2008.4523003
20 NAsai, K Onishi, SMori, et al. Development of an AI supporting system for knowledge acquisition and refinement (nuclear power plant applications). In: Proceedings of the International Workshop on Artificial Intelligence for Industrial Applications, Hitachi City, Japan, 1988, 47–51
https://doi.org/10.1109/AIIA.1988.13268
21 A JGermond. Application of AI techniques to monitoring of transformers and optimal allocation of facts in power systems. In: IEEE/PES Transmission and Distribution Conference and Exhibition, Yokohama, Japan, 2002, 651–653
22 RSubha, S Himavathi. Active power control of a photovoltaic system without energy storage using neural network-based estimator and modified P&O algorithm. IET Generation, Transmission & Distribution, 2018, 12(4): 927–934
https://doi.org/10.1049/iet-gtd.2017.0156
23 WMeng, X Wang, BFan, QYang, I Kamwa. Adaptive non-linear neural control of wide-area power systems. IET Generation, Transmission & Distribution, 2017, 11(18): 4531–4536
https://doi.org/10.1049/iet-gtd.2017.0299
24 DXu, J Liu, X GYan, WYan. A novel adaptive neural network constrained control for multi-area interconnected power system with hybrid energy storage. IEEE Transactions on Industrial Electronics, 2017, 65(8): 6625–6634
https://doi.org/10.1109/TIE.2017.2767544
25 JWang, Y Jin, YWang. Transient rotation speed control of power system for hydraulic walking platform based on neural network structure PID. In: 2017 4th International Conference on Information Science and Control Engineering (ICISCE), Changsha, China, 2017, 1313–1317
https://doi.org/10.1109/ICISCE.2017.273
26 X QZhang, K Chen, Y QZou, et al. A direct adaptive neural control with voltage traverse for maximum power point tracking of photovoltaic system. In: 2017 29th Chinese Control and Decision Conference (CCDC), Chongqing, China, 2017, 4493–4498
https://doi.org/10.1109/CCDC.2017.7979290
27 SHalilcevic, C Moraga, BImamovic. Neural network-based equipment for the power system frequency control. In: 10th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MedPower 2016), Belgrade, Serbia, 2016, 95–102
28 A JWood, B F Wollenberg. Power Generation, Operation, and Control. John Wiley & Sons, 2012
29 ABhattacharya, J Kharoufeh, BZeng. Managing energy storage in microgrids: a multistage stochastic programming approach. IEEE Transactions on Smart Grid, 2017, 9(1): 483–496
https://doi.org/10.1109/TSG.2016.2618621
30 H AAalami, S Nojavan. Energy storage system and demand response program effects on stochastic energy procurement of large consumers considering renewable generation. IET Generation, Transmission & Distribution, 2016, 10(1): 107–114
https://doi.org/10.1049/iet-gtd.2015.0473
31 JDupačová, NGroewe-Kuska, WRoemisch. Scenario reduction in stochastic programming: an approach using probability metrics. Mathematical Programming, 2003, 95: 493–511
https://doi.org/10.1007/s10107-002-0331-0
32 J RBirge, F Louveaux. Introduction to Stochastic Programming. New York: Springer Science & Business Media, 2011
33 LZhang, H Yuan. Research on software architecture of prognostic and health management system for airborne equipment using multi-agent. In: 2012 2nd International Conference on Applied Robotics for the Power Industry (CARPI), Zurich, Switzerland, 2012
34 MLuo, S Lin, DFeng, et al. Design of the prognostics and health management platform of high-speed railway traction power supply equipment. In: Prognostics and System Health Management Conference (PHM-Harbin), Harbin, China, 2017
https://doi.org/10.1109/PHM.2017.8079110
35 KChen, J Hu, JHe. Detection and classification of transmission line faults based on unsupervised feature learning and convolutional sparse auto-encoder. IEEE Transactions on Smart Grid, 2016, 9(3): 1748–1758
https://doi.org/10.1109/TSG.2016.2598881
36 M FGuo, X D Zeng, D Y Chen, et al. Deep-learning-based earth fault detection using continuous wavelet transform and convolutional neural network in resonant grounding distribution systems. IEEE Sensors Journal, 2018, 18(3): 1291–1300
https://doi.org/10.1109/JSEN.2017.2776238
37 XPeng, F Pan, YLiang, et al. Fault detection algorithm for power distribution network based on sparse self-encoding neural network. In: 2017 International Conference on Smart Grid and Electrical Automation (ICSGEA), Changsha, China, 2017: 9–12
https://doi.org/10.1109/ICSGEA.2017.19
38 MPai. Energy Function Analysis for Power System Stability. New York: Springer Science & Business Media, 2012
39 M FMøller. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 1993, 6(4): 525–533
https://doi.org/10.1016/S0893-6080(05)80056-5
40 SHochreiter, Y Bengio, PFrasconi, et al. Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. 2001,
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