Application of AI techniques in monitoring and operation of power systems

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.

Keywords

power system operation and monitoring / artificial intelligence (AI) / deep learning / power flow analysis

References

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

[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

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

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

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

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

[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

[8] Bi T, Ni Y, Shen C, 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

[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

[10] Rodrigues F, Cardeira C, Calado J M F. 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

[11] Berriel R F, Lopes A T, Rodrigues A, Monthly energy consumption forecast: a deep learning approach. In: 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, 2017, 4283–4290

[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

[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

[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

[15] Fahiman F, Erfani S M, Rajasegarar S, 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

[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

[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

[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

[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

[20] Asai N, Onishi K, Mori S, 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

[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

[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

[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

[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

[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

[26] Zhang X Q, Chen K, Zou Y Q, 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

[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

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

[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

[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

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

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

[33] Zhang L, Yuan H. 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] Luo M, Lin S, Feng D, 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

[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

[36] Guo M F, Zeng X D, Chen D Y, 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

[37] Peng X, Pan F, Liang Y, 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

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

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

[40] Hochreiter S, Bengio Y, Frasconi P, Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. 2001, https://pdfs.semanticscholar.org/2e5f/2b57f4c476dd69dc22ccdf547e48f40a994c.pdf

Acknowledgments

This work was supported by State Grid Corporation of China (SGCC) Science and Technology Project (No. SGTJDK00DWJS1700060).

Acknowledgments

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

RIGHTS & PERMISSIONS

2018 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature