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Stability Analysis and Optimization of Energy Power System Based on Advanced Control Strategy

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DOI: 10.23977/jeeem.2024.070109 | Downloads: 1 | Views: 71

Author(s)

Yunyan Li 1, Xingbang Song 1

Affiliation(s)

1 Department of Economic Management, North China Electric Power University, Baoding, 071003, China

Corresponding Author

Yunyan Li

ABSTRACT

Advanced control strategy has important application value in stability analysis and optimization of energy and power system. The main purpose of this study is to explore the application of advanced control strategy in the stability analysis and optimization of energy power system, and verify its effectiveness and superiority through simulation experiments. Specifically, this paper deeply analyzes the stability of energy power system and its influencing factors; The principle of advanced control strategy and its application method in energy and power system are studied. The optimization method of system stability based on advanced control strategy is proposed and verified by simulation experiments. The experimental results show that compared with the traditional control methods, the advanced control strategy in this paper can better adapt to the complex and changeable system operating environment and diversified energy access requirements, and significantly improve the stability and security of the system. This study is of great significance to promote the in-depth development of the stability research of energy power system and ensure the reliability and safety of power supply. At the same time, it also provides new ideas and directions for future research.

KEYWORDS

Control strategy; Energy power system; Neural network; Stability analysis

CITE THIS PAPER

Yunyan Li, Xingbang Song, Stability Analysis and Optimization of Energy Power System Based on Advanced Control Strategy. Journal of Electrotechnology, Electrical Engineering and Management (2024) Vol. 7: 64-69. DOI: http://dx.doi.org/10.23977/jeeem.2024.070109.

REFERENCES

[1] Zhou Xiaoxin, Shi Dongyu, Chen Yong, et al. A Convolutional Neural Network Based Transient Stability Prevention and Control Method for Power Systems [J]. Power System Protection and Control, 2020, 48 (18): 8.
[2] Miao Linxin, Yu Jing, Lu Yiqi. Security Analysis of Power Systems Based on Artificial Neural Networks [J]. Electrical Technology and Economics, 2023 (6): 123-125.
[3] Wang Xiaomin. Monitoring and optimization analysis of secondary equipment in power systems using neural networks [J]. Electrical Technology, 2021, 000 (012): 137-139.
[4] Li Yong, Ma Gaoshan, Han Feifei, et al. Artificial neural network control strategy for unmanned aerial vehicle fuel cell hybrid power system [J]. Science and Technology and Engineering, 2023, 23 (27): 11878-11885.
[5] He Yujing, Chen Jie, Zhang Jiuming. Online energy management strategy combining wavelet packet analysis and BP neural network prediction [J]. Power Grid and Clean Energy, 2023, 39 (9): 9-18.
[6] Li Lingling, Feng Huan. Virtual synchronous generator frequency control strategy for renewable energy power systems [J]. Journal of Tianjin University of Technology, 2021 (5): 81-88.
[7] Chen Shiming, Lu Jiasheng, Gao Yanli. Distributed Adaptive Control of Transient Stability in Power Systems Based on Neural Networks [J]. Control and Decision Making, 2021, 36 (6): 8.
[8] Mao Kai, Yang Shujie, Liu Dan. Global asymptotic stability analysis of time-delay dependence for a class of discrete time-delay static neural network systems [J]. Journal of Chongqing University of Technology: Natural Science, 2020, 34 (3): 8.
[9] Ding Kun, Sun Yalu, Yang Changhai, et al. Research on Multi model Weighted Predictive Control of Solar Thermal Power Generation Systems [J]. Journal of Northwest Normal University: Natural Science Edition, 2023, 59 (6): 43-49.
[10] Liao Siyang, Chen Yilin, Xu Jian, et al. Emergency load shedding control method for new energy power systems based on feeder load decomposition [J]. Grid Technology, 2023, 47 (11): 4405-4415. 

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