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.
| Published in | Journal of Electrical and Electronic Engineering (Volume 7, Issue 1) |
| DOI | 10.11648/j.jeee.20190701.13 |
| Page(s) | 23-28 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Artificial Intelligence, Intelligent Scheduling, New Generation of Electric Power System
| [1] | 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. |
| [2] | 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. |
| [3] | MCBEE M P, AWAN O A, COLUCCI A T, et al. Deep learning in radiology [J]. Academic Radiology, 20181-9. |
| [4] | 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. |
| [5] | HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets [J]. Neural Computation, 2006, 18 (7): 1527-1554. |
| [6] | 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. |
| [7] | 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. |
| [8] | 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. |
| [9] | 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. |
| [10] | 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. |
| [11] | Sun Q Y, Teng F, Zhang H G. Energy internet and its key control issues [J]. Acta Automatica Sinica, 2017, 43 (2): 176-194. |
| [12] | 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. |
| [13] | 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. |
| [14] | 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. |
| [15] | 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. |
| [16] | 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. |
| [17] | 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. |
| [18] | 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. |
| [19] | 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. |
| [20] | 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. |
| [21] | 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. |
| [22] | 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. |
APA Style
Xu Jizhi, Zhang Xinyan, Li Jianwei. (2019). Application of Artificial Intelligence in the Field of Power Systems. Journal of Electrical and Electronic Engineering, 7(1), 23-28. https://doi.org/10.11648/j.jeee.20190701.13
ACS Style
Xu Jizhi; Zhang Xinyan; Li Jianwei. Application of Artificial Intelligence in the Field of Power Systems. J. Electr. Electron. Eng. 2019, 7(1), 23-28. doi: 10.11648/j.jeee.20190701.13
AMA Style
Xu Jizhi, Zhang Xinyan, Li Jianwei. Application of Artificial Intelligence in the Field of Power Systems. J Electr Electron Eng. 2019;7(1):23-28. doi: 10.11648/j.jeee.20190701.13
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.jeee.20190701.13,
author = {Xu Jizhi and Zhang Xinyan and Li Jianwei},
title = {Application of Artificial Intelligence in the Field of Power Systems},
journal = {Journal of Electrical and Electronic Engineering},
volume = {7},
number = {1},
pages = {23-28},
doi = {10.11648/j.jeee.20190701.13},
url = {https://doi.org/10.11648/j.jeee.20190701.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20190701.13},
abstract = {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.},
year = {2019}
}
TY - JOUR T1 - Application of Artificial Intelligence in the Field of Power Systems AU - Xu Jizhi AU - Zhang Xinyan AU - Li Jianwei Y1 - 2019/03/08 PY - 2019 N1 - https://doi.org/10.11648/j.jeee.20190701.13 DO - 10.11648/j.jeee.20190701.13 T2 - Journal of Electrical and Electronic Engineering JF - Journal of Electrical and Electronic Engineering JO - Journal of Electrical and Electronic Engineering SP - 23 EP - 28 PB - Science Publishing Group SN - 2329-1605 UR - https://doi.org/10.11648/j.jeee.20190701.13 AB - 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. VL - 7 IS - 1 ER -
Information
Email: