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多源数据融合的火电机组AGC系统动态优化方法研究
摘要: 本文针对火电机组自动发电控制（AGC）系统控制精度不足、响应速度慢等问题，提出一种基于多源数据融合的动态优化方法。研究通过建立AGC系统数学模型，构建多源数据融合框架，并采用模型预测控制与强化学习等先进算法设计动态优化策略。在仿真与真实机组数据实验表明，所提方法能有效提升AGC系统控制精度、响应速度及运行稳定性，同时改善经济性。相比现有研究，本方法在性能提升与算法融合上具有创新性。尽管存在对数据质量敏感、算法复杂等局限，未来仍可向深度学习融合、多机组协同等方向拓展。本研究为火电机组AGC系统优化提供了有效理论支撑与实践路径。
Abstract: To address the issues of insufficient control precision and slow response speed in the Automatic Generation Control (AGC) systems of thermal power units, this paper proposes a dynamic optimization method based on multi-source data fusion. A mathematical model of the AGC system is established, and a multi-source data fusion framework is constructed. Advanced algorithms, including Model Predictive Control (MPC) and reinforcement learning, are then employed to design a dynamic optimization strategy. Simulations and experiments with real unit data demonstrate that the proposed method effectively enhances the control precision, response speed, and operational stability of the AGC system, while also improving its economic performance. In contrast to existing studies, this method exhibits innovation in both performance enhancement and algorithmic integration. Despite limitations such as sensitivity to data quality and algorithm complexity, future work can explore integration with deep learning and multi-unit coordination. This research provides a solid theoretical foundation and a practical pathway for the optimization of AGC systems in thermal power units.
文章引用：丁伟平. 多源数据融合的火电机组AGC系统动态优化方法研究[J]. 现代工程与应用, 2025, 3(3): 24-37. DOI: https://doi.org/10.61784/mea2003
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参考文献

[1] 董锴, 蔡新雷, 崔艳林, 等. 基于旋转门算法的电池储能辅助单机跟踪AGC指令控制策略. 电力系统保护与控制, 2022, 50(15): 147-156.

[2] 王育飞, 程伟, 薛花, 等. 基于串级PI-(1+PＤ)算法的含飞轮储能互联电网AGC控制器设计. 电力系统保护与控制, 2023, 51(14): 127-138.

[3] ULLAH K, BASIT A, ULLAH Z, et al. Automatic generation control strategies in conventional and modern power systems: a comprehensive overview. Energies, 2021, 14(9): 2376.

[4] 胡泽春, 罗浩成. 大规模可再生能源接入背景下自动发电控制研究现状与展望.电力系统自动化, 2018, 42(8)：2-15.

[5] YAN Wei, ZHAO Ruifeng, ZHAO Xia, et al. Dynamic optimization model of AGC strategy under CPS for interconnected power system. International Review of Electrical Engineering-IREE, 2012, 7(5): 5733-5743.

[6] 李滨, 韦化, 农蔚涛, 等. 基于现代内点理论的互联电网控制性能评价标准下的 AGC 控制策略.中国电机工程学报, 2008, 28(25): 56-61.

[7] DE ASSIS L S, CAMPONOGARA E, ZIMBERG B, et al. A piecewise McCormick relaxation-based strategy for scheduling operations in a crude oil terminal. Computers & Chemical Engineering, 2017, 106: 309-321.

[8] Yan Gangui, Liu Yu, Mu Gang, et al. The Ultra-short Term Prediction of Wind PowerBased on Chaotic Time Series . Energy Procedia, 2012, 17: 1490-1496.

[9] ZHANG X, KANG D-J, CHOI S. Evaluation of false data injection attacks on automatic generation control considering wind power dynamics. International Journal of Electrical Power & Energy Systems, 2022, 143.

[10] NAYAK J R, SHAW B, SAHU B K, et al. Application of optimized adaptive crow search algorithm based two degree of freedom optimal fuzzy PID controller for AGC system. Engineering Science and Technology, an International Journal, 2022, 32: 101061.

[11] 路小俊, 伊建伟, 李炎. 基于多目标网格自适应搜索算法的储能系统参与AGC 优化控制策略. 电网技术, 2019, 43(06): 2116-24.

[12] REN L, NING X, WANG Z. A competitive Markov decision process model and a recursive reinforcement-learning algorithm for fairness scheduling of agile satellites. Computers & Industrial Engineering, 2022, 169: 108242.

[13]  Latif A, Hussain S, Das D C, et al. State-of-the-art of Controllers and Soft Computing Techniques for Regulated Load Frequency Management of Single/Multi-Area Traditional and Renewable Energy based Power Systems.Applied Energy, 2020, 266: 114858.

[14] Safari A, Babaei F, Farrokhifar M. A load frequency control using a PSO-based ANN for micro-grids in the presence of electric vehicles. International Journal of Ambient Energy, 2021, 42(6): 688-700.

[15] 刘连光, 刘鸿熹, 刘自发, 等. 新能源接入下风火网三方非对称进化博弈分析. 中国科学: 技术科学, 2015, 45: 1297-1303.

[16] 李川军.自动化技术在火力发电中的应用. 电子技术, 2023, 52(01): 282-283.

[17] GE Xiaolin, QIAN Jia, FU Yang, et al. Transient stability evaluation criterion of multi-wind farms integrated power system. IEEE Transactions on Power Systems, 2022, 37(4): 3137-3140.

[18] ARYA Y. AGC of restructured multi-area multi-source hydrothermal power systems incorporating energy storage units via optimal fractional-order fuzzy PID controller. Neural Computing and Applications, 2019, 31(3): 851-872.

[19] XIE Xianyi, WANG Yuhan, JIN Lisheng, et al. Model predictive control trajectory tracking control based on adjusting the step size of control horizon. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(3): 620-630.

[20] WANG Tao, KANG Yu, LI Pengfei. Adaptive event-triggered distributed model predictive control for tracking consensus of multiagent systems. Scientia Sinica (Technologica), 2023, 53(11): 1885-1894.

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