基于多区域智能电网的新型mpc级联控制：应对可再生能源和电动汽车集成挑战。

IF 6.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions Pub Date : 2025-06-30 DOI:10.1016/j.isatra.2025.06.024

Muhammad S. Tolba , Muhammad Majid Gulzar , Ali Arishi , Mohamed Soliman , Ali Faisal Murtaza

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引用次数: 0

摘要

本文提出了一种适用于可再生能源和电动汽车多区域电力系统负荷频率调节的先进级联控制方案。所提出的模型预测控制与一加比例积分控制级联，倾斜控制与一加分数阶积分导数控制器（MPC-(1+PI)-(T+(1+ i - λ dμ)))）)并联，结合了预测、倾斜和分数阶动力学，以提高不确定性下的适应性和鲁棒性。控制器参数使用Lyrebird优化算法（LOA）进行调整，确保快速收敛和有效的全局搜索。在不同运行条件下的仿真结果，包括非线性影响，如产生率约束（GRC），总督死区（GDB）和通信时间延迟（CTD），证实了控制器的优越性。它实现了96.4 %的ITAE降低，98.6 %的低偏差缓解，并且稳定时间仅为5.8 s，优于现有的基准策略（GOA: PDf+(0.75+PI), CBOA: PI- pd， JSA: PI和ARA: 1+PID）。

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A novel MPC-based cascaded control for multi-area smart grids: Tackling renewable energy and EV integration challenges

This paper presents an advanced cascaded control scheme for load frequency regulation in multi-area power systems incorporating renewable energy sources (RES) and electric vehicles (EVs). The proposed design (Model predictive control cascaded with one plus proportional-integral control cascaded with tilt control in parallel with one plus fractional-order integral derivative controller (MPC-((1+PI)-(T+(1+I

^{λ}

^{μ}

)))) combines predictive, tilt, and fractional-order dynamics to improve adaptability and robustness under uncertainties. Controller parameters are tuned using the Lyrebird Optimization Algorithm (LOA), ensuring fast convergence and effective global search. Simulation results under varying operational conditions, including nonlinearity effects such as Generation Rate Constraints (GRC), Governor Dead Band (GDB), and Communication Time Delays (CTD), confirm the controller’s superiority. It achieves a 96.4 % ITAE reduction, 98.6 % undershoot mitigation, and a settling time of just 5.8 s outperforming existing benchmark strategies (GOA: PDf+(0.75+PI), CBOA: PI-PD, JSA: PI, and ARA: 1+PID).

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来源期刊

ISA transactions 工程技术-工程：综合

CiteScore

11.70

自引率

12.30%

发文量

824

审稿时长

4.4 months

期刊介绍： ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.