Application of Artificial Intelligence in the Field of Power Systems
Journal of Electrical and Electronic Engineering
Volume 7, Issue 1, February 2019, Pages: 23-28
Received: Mar. 7, 2019; Published: Mar. 8, 2019
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Xu Jizhi, School of Electrical Engineering, Xinjiang University, Urumqi, China
Zhang Xinyan, Engineering Research Center for Renewable Energy Power Generation and Grid Technology, Education Ministry, Urumqi, China
Li Jianwei, School of Electrical Engineering, Xinjiang University, Urumqi, China
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In recent years, the development of power systems has advanced by leaps and bounds. With the development of artificial intelligence, new directions have emerged. China has upgraded the development of artificial intelligence to a national strategy. A new proportion of new energy terminals is connected to the power grid. Modern power systems present complexity and uncertainty. Artificial intelligence technology will be an effective measure to solve complex system control and decision problems. Based on the application of artificial intelligence in the field of power system application. As a key link in the end-use of energy systems, the degree of intelligence of the power system will greatly affect the smooth implementation of the above technological innovations and advancements. At the same time, the pivotal link of the power system will cause the energy system to focus on the power system and Energy-related technologies are the basis for the integration and integration of the entire energy system. Therefore, it is essential to focus on the development of intelligent technologies in the power system. this paper expounds the application of artificial intelligence technology in power system scheduling, planning and power market.
Artificial Intelligence, Intelligent Scheduling, New Generation of Electric Power System
To cite this article
Xu Jizhi, Zhang Xinyan, Li Jianwei, Application of Artificial Intelligence in the Field of Power Systems, Journal of Electrical and Electronic Engineering. Vol. 7, No. 1, 2019, pp. 23-28. doi: 10.11648/j.jeee.20190701.13
SUN Bolin. New report on artificial intelligence in the United States and its implications for Us [J]. Techniques of Automation & Applications, 2017, 36 (10): 1-7.
MAHMUD M, KAISER M S, HUSSAIN A, et al. Applications of deep learning and reinforcement learning to biological data [J]. IEEE Transactions on Neural Networks&Learning Systems, 2018, 29 (6): 2063-2079.
MCBEE M P, AWAN O A, COLUCCI A T, et al. Deep learning in radiology [J]. Academic Radiology, 20181-9.
LITJENS G, KOOI T, BEJNORDI B E, et al. A survey on deep learning in medical image analysis [J]. Medical Image Analysis, 2017, 42 [9]: 60-88.
HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets [J]. Neural Computation, 2006, 18 (7): 1527-1554.
HU B, LU Z, LI H, et al. Convolutional neural network architectures for matching natural language sentences [J]. Advances in Neural Information Processing Systems, 2015, 3: 2042-2050.
ZHU Qiaomu, LI Hongyi, WANG Ziqi, et al. Short-term wind power forecasting based on LSTM [J]. Power System Technology, 2017, 41 (12): 3797-3802.
GEHRING J, MIAO Y, METZE F, et al. Extracting deep bottleneck features using stacked auto-encoders [C] / / IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2013: 3377-3381.
Ali Y, Rasheed Z, Muhammad N, et al. Energy optimization in the wake of China pakistan economic corridor (CPEC) [J]. J of Control and Decision, 2018, 5 (2): 129-147.
Kopsidas K, Kapetanaki A, Levi V, et al. Optimal demand response scheduling with real time thermal ratings of overhead lines for improved network reliability [J]. IEEE Trans on Smart Grid, 2017, 8 (6): 2813-2825.
Sun Q Y, Teng F, Zhang H G. Energy internet and its key control issues [J]. Acta Automatica Sinica, 2017, 43 (2): 176-194.
Takeuchi A, Hayashi T, Nozaki Y, et al. Optimal scheduling using meta heuristics for energy networks [J]. IEEE Trans on Smart Grid, 2012, 3 (2): 968-974.
Sousa T, Morais H, Vale Z, et al. Intelligent energy resource management considering vehicle-to-grid: A simulated annealing approach [J]. IEEE Trans on Smart Grid, 2012, 3 (1): 535-542.
Zhao B, Guo C X, Cao Y J. A multiagent-based particle swarm optimization approach for optimal reactive power dispatch [J]. IEEE Trans on Power Systems, 2005, 20 (2): 1070-1078.
Li S, Shahidehpour S M, Wang C. Promoting the application of expert systems in short-term unit commitment [J]. IEEE Trans on Power Systems, 1993, 8 (1): 286-292.
Kurban M, Filik U B. Unit commitment scheduling by using the auto regressive and artificial neural network models based short-term load forecasting [C]. Int Confon Probabilistic Methods Applied To Power Systems. RinCon: IEEE, 2008: 1-5.
Quan H, Srinivasan D, Khosravi A. Incorporating wind power forecast uncertainties into stochastic unit commitment using neural network-based prediction intervals [J]. IEEE Trans on Neural Networks and Learning Systems, 2015, 26 (9): 2123-2135.
LU Shouyin, ZHANG Ying, LI Jianxiang, et al. Application of mobile robot in high voltage substation [J]. High Voltage Engineering, 2017, 43 (1): 276-284.
LIU Wei, ZHANG Dongxia, WANG Xinying, et al. A decision making strategy for generating unit tripping under emergency circumstances based on deep reinforcement learning [J]. Proceedings of the CSEE, 2018, 38 (1): 109-119, 347.
JIANG Haorong, XU M aoxin, WANG Keying. Grid know ledge transfer learning algorithm and its application in carbon-energy combined-flow optimization [J]. Electric Pow er Construction, 2017, 38 (7): 96-105.
Tan W S, Hassan M Y, Majid M S, et al. Optimal distributed renewable generation planning: A review of different approaches [J]. Renewable and Sustainable Energy Reviews, 2013, 18 (2): 626-645.
Gao W X, Luo X J, Zhu Y. A new distribution substation planning algorithm based on greedy algorithm and Hopfield neural network [J]. Power System Technology, 2004, 28 (7): 73-76.
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