Ping Boyu, Zeng Deliang, Hu Yong, Xie Yan
North China Electric Power University, Beijing, 102206, China.
Heliyon. 2024 Jun 3;10(12):e31997. doi: 10.1016/j.heliyon.2024.e31997. eCollection 2024 Jun 30.
To mitigate the impact of large-scale renewable energy power on the national grid in China, it is imperative to enhance the flexible peaking capability of coal-fired thermal power units. The coordinated control system, central to the load control of coal-fired units, faces challenges such as multivariable coupling, sluggish response, and uncertain coal quality parameters. This paper introduces a neural network predictive controller based on the improved TPA-LSTM model, aimed at addressing these issues. Initially, a data-driven control model is established to break through the limitations of traditional linear predictive control and effectively handle disturbance uncertainties. Then, a multivariable coordinated control strategy based on the neural network controller is designed, achieving effective decoupling of multiple parameters and ensuring high adaptability across all load conditions. Additionally, by integrating an automatic model updating mechanism, the system can recalibrate in real-time when model mismatches occur due to equipment aging, maintenance, or changes in coal quality, thereby enhancing overall control performance. Simulation results demonstrate that this strategy has excellent control effectiveness, meeting the flexible peaking demands of 1000 MW ultra-supercritical units. The calibration feature of the data-driven model significantly improves control performance following model mismatches.
为减轻大规模可再生能源电力对中国国家电网的影响，提高燃煤火力发电机组的灵活调峰能力势在必行。协调控制系统是燃煤机组负荷控制的核心，但面临多变量耦合、响应迟缓以及煤质参数不确定等挑战。本文介绍了一种基于改进的TPA-LSTM模型的神经网络预测控制器，旨在解决这些问题。首先，建立数据驱动控制模型，突破传统线性预测控制的局限性，有效处理干扰不确定性。然后，设计基于神经网络控制器的多变量协调控制策略，实现多个参数的有效解耦，并确保在所有负荷工况下具有高适应性。此外，通过集成自动模型更新机制，当因设备老化、维护或煤质变化导致模型失配时，系统能够实时重新校准，从而提高整体控制性能。仿真结果表明，该策略具有出色的控制效果，满足1000MW超超临界机组的灵活调峰需求。数据驱动模型的校准特性在模型失配后显著提高了控制性能。
