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Application of AI techniques in monitoring and operation of power systems

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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.

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References

  1. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521 (7553): 436–444

    Article  Google Scholar 

  2. Wang F Y, Wong P K. Intelligent systems and technology for integrative and predictive medicine: an ACP approach. ACM Transactions on Intelligent Systems and Technology, 2013, 4(2): 32

    Google Scholar 

  3. Wang F. Parallel control: a method for data-driven and computational control. Acta Automatica Sinica, 2013, 39(4): 293–302

    Article  Google Scholar 

  4. Li L, Lin Y L, Cao D P, et al. Parallel learning—a new framework for machine learning. Acta Automatica Sinica, 2017, 43(1): 1–8

    MATH  Google Scholar 

  5. Silver D, Huang A, Maddison C J, et al. Mastering the game of go with deep neural networks and tree search. Nature, 2016, 529(7587): 484–489

    Article  Google Scholar 

  6. Deng J L, Wang F Y, Chen Y B, et al. From industries 4.0 to energy 5.0: concept and framework of intelligent energy systems. Acta Automatica Sinica, 2015, 41: 2003–2016

    Google Scholar 

  7. Wang Y, Liu M, Bao Z. Deep learning neural network for power system fault diagnosis. In: 2016 35th Chinese Control Conference (CCC), Chengdu, China, 2016, 6678–6683

    Book  Google Scholar 

  8. Bi T, Ni Y, Shen C, 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

    Google Scholar 

  9. Zhu Y, Hou L, Lu J. Bayesian networks-based approach for power systems fault diagnosis. IEEE Transactions on Power Delivery, 2006, 21(2): 634–639

    Article  Google Scholar 

  10. Rodrigues F, Cardeira C, Calado JMF. 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

    Article  Google Scholar 

  11. Berriel R F, Lopes A T, Rodrigues A, 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

    Book  Google Scholar 

  12. Williams K T, Gomez J D. Predicting future monthly residential energy consumption using building characteristics and climate data: a statistical learning approach. Energy and Building, 2016, 128: 1–11

    Article  Google Scholar 

  13. Almalaq A, Edwards G. 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

    Book  Google Scholar 

  14. Li L, Ota K, Dong M. 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

    Google Scholar 

  15. Fahiman F, Erfani S M, Rajasegarar S, 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

    Book  Google Scholar 

  16. Shi H, Xu M, Li R. Deep learning for household load forecasting–a novel pooling deep RNN. IEEE Transactions on Smart Grid, 2017: 1–1

    Google Scholar 

  17. Fayek R, Sweif R. 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

    Google Scholar 

  18. David A, Rongda Z. Advances in AI applications for power system planning. In: 1st International Conference on Expert Planning Systems, Brighton, UK, 1990, 36–41

    Google Scholar 

  19. Li W, Ying J. 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

    Google Scholar 

  20. Asai N, Onishi K, Mori S, 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

    Google Scholar 

  21. Germond A J. 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

    Google Scholar 

  22. Subha R, Himavathi S. 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

    Article  Google Scholar 

  23. Meng W, Wang X, Fan B, Yang Q, Kamwa I. Adaptive non-linear neural control of wide-area power systems. IET Generation, Transmission & Distribution, 2017, 11(18): 4531–4536

    Article  Google Scholar 

  24. Xu D, Liu J, Yan X G, Yan W. 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

    Article  Google Scholar 

  25. Wang J, Jin Y, Wang Y. 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

    Google Scholar 

  26. Zhang X Q, Chen K, Zou Y Q, 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

    Google Scholar 

  27. Halilcevic S, Moraga C, Imamovic B. 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

    Google Scholar 

  28. Wood A J, Wollenberg B F. Power Generation, Operation, and Control. John Wiley & Sons, 2012

    Google Scholar 

  29. Bhattacharya A, Kharoufeh J, Zeng B. Managing energy storage in microgrids: a multistage stochastic programming approach. IEEE Transactions on Smart Grid, 2017, 9(1): 483–496

    Article  Google Scholar 

  30. Aalami H A, Nojavan S. 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

    Article  Google Scholar 

  31. Dupačová J, Groewe-Kuska N, Roemisch W. Scenario reduction in stochastic programming: an approach using probability metrics. Mathematical Programming, 2003, 95: 493–511

    Article  MathSciNet  Google Scholar 

  32. Birge J R, Louveaux F. Introduction to Stochastic Programming. New York: Springer Science & Business Media, 2011

    Book  MATH  Google Scholar 

  33. Zhang L, Yuan H. Research on software architecture of prognostic and health management system for airborne equipment using multiagent. In: 2012 2nd International Conference on Applied Robotics for the Power Industry (CARPI), Zurich, Switzerland, 2012

    Google Scholar 

  34. Luo M, Lin S, Feng D, 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

    Book  Google Scholar 

  35. Chen K, Hu J, He J. 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

    Google Scholar 

  36. Guo M F, Zeng X D, Chen D Y, 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

    Article  Google Scholar 

  37. Peng X, Pan F, Liang Y, 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

    Google Scholar 

  38. Pai M. Energy Function Analysis for Power System Stability. New York: Springer Science & Business Media, 2012

    Google Scholar 

  39. Møller M F. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 1993, 6(4): 525–533

    Article  Google Scholar 

  40. Hochreiter S, Bengio Y, Frasconi P, et al. Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. 2001, https://doi.org/pdfs.semanticscholar.org/2e5f/2b57f4c476dd69dc22ccdf547e48-f40a994c.pdf

    Google Scholar 

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Acknowledgements

This work was supported by State Grid Corporation of China (SGCC) Science and Technology Project (No. SGTJDK00DWJS-1700060).

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Correspondence to Fang Zhang.

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Gao, D.W., Wang, Q., Zhang, F. et al. Application of AI techniques in monitoring and operation of power systems. Front. Energy 13, 71–85 (2019). https://doi.org/10.1007/s11708-018-0589-4

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