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采用先进优化和模糊逻辑控制器的大型可再生能源系统的改进故障清除策略

Improved fault-clearing strategy for large renewable energy systems using advanced optimization and FLC.

作者信息

Almalaq Abdulaziz, Alqunun Khalid, Abbassi Rabeh, Aleem Shady H E Abdel, Mohamed Rania G

机构信息

Department of Electrical Engineering, College of Engineering, University of Hail, Hail, 55473, Saudi Arabia.

Basic Sciences Council, Academy of Scientific Research and Technology, Cairo, 11516, Egypt.

出版信息

Sci Rep. 2025 Sep 12;15(1):32455. doi: 10.1038/s41598-025-18167-8.

Abstract

This paper introduces a novel fault-clearing strategy for large-scale hybrid photovoltaic/wind/battery power systems (HPVWBPS). A modified fault-clearing strategy (MFCS) is developed using the Manta ray foraging optimization (MRFO) algorithm and a fuzzy logic controller (FLC). The FLC enhances decision-making in fault-clearing, while MRFO determines the optimal FLC gains for photovoltaic (PV) and wind turbine (WT)-based power plants, ensuring maximum power point tracking (MPPT). Additionally, MRFO optimizes power dispatch during faults by considering solar and wind resource availability, battery energy levels, and load demand. By mimicking the foraging behavior of manta rays, the algorithm efficiently balances power generation and consumption, minimizing fault impact. The proposed strategy is evaluated through extensive simulations on a large-scale HPVWBPS using MATLAB/SIMULINK 2022(b). The proposed method enhances system stability and fault recovery by determining optimal controller gains using the MRFO algorithm. A detailed comparison of key performance metrics, rise time (RT), settling time (ST), maximum overshoot (MOST), and optimal gains was conducted for both voltage and current regulators under four configurations: PI-based GWO, PI-based MRFO, FLC-based GWO, and FLC-based MRFO. This assessment isolates the effects of both the controller type and the optimization algorithm. The results show that MRFO consistently outperforms GWO in both PI and FLC frameworks. MRFO provides faster convergence, reduced overshoot, and shorter settling times. For the current regulator, the MRFO-FLC combination achieves an ST of 1.0020 ms, compared to 1.0033 ms for the PI-GWO controller, marking a 1.3% improvement. The RT is reduced from 3.2755 μs to 2.1885 μs, a 33.2% decrease. MOST is also lowered from 143.22% to 140.12%, a 2.17% reduction. These improvements enhance the regulator's dynamic performance and reduce component stress. The voltage regulator shows similar trends. The ST drops slightly from 1.0052 ms to 1.0051 ms. RT improves from 8.1978 μs to 8.1878 μs. MOST decreases from 86.79% to 86.57%. Though the changes are smaller, they remain consistent across all metrics. FLC-based controllers outperform PI-based controllers in terms of dynamic response and stability. They better handle system nonlinearities and delays, making them more suitable for hybrid renewable energy systems. Among all configurations tested, the FLC-MRFO setup delivers the best overall performance. Its superior adaptability, reduced overshoot, and faster response validate its effectiveness for robust and efficient power system control.

摘要

本文介绍了一种适用于大规模混合光伏/风力/电池电力系统(HPVWBPS)的新型故障清除策略。利用蝠鲼觅食优化(MRFO)算法和模糊逻辑控制器(FLC)开发了一种改进的故障清除策略(MFCS)。FLC增强了故障清除过程中的决策能力,而MRFO则为基于光伏(PV)和风力涡轮机(WT)的发电厂确定最佳FLC增益,确保最大功率点跟踪(MPPT)。此外,MRFO通过考虑太阳能和风能资源可用性、电池能量水平和负载需求,在故障期间优化功率分配。通过模仿蝠鲼的觅食行为,该算法有效地平衡了发电和消耗,将故障影响降至最低。通过使用MATLAB/SIMULINK 2022(b)对大规模HPVWBPS进行广泛仿真,对所提出的策略进行了评估。所提出的方法通过使用MRFO算法确定最佳控制器增益,提高了系统稳定性和故障恢复能力。在四种配置下,对电压和电流调节器的关键性能指标上升时间(RT)、调节时间(ST)、最大超调量(MOST)和最佳增益进行了详细比较:基于PI的灰狼优化算法(GWO)、基于PI的MRFO、基于FLC的GWO和基于FLC的MRFO。该评估分离了控制器类型和优化算法的影响。结果表明,在PI和FLC框架中,MRFO始终优于GWO。MRFO具有更快的收敛速度、更小的超调量和更短的调节时间。对于电流调节器,MRFO-FLC组合的调节时间为1.0020 ms,而PI-GWO控制器为1.0033 ms,提高了1.3%。上升时间从3.2755 μs降至2.1885 μs,下降了33.2%。最大超调量也从143.22%降至140.12%,降低了2.17%。这些改进提高了调节器的动态性能,降低了组件应力。电压调节器也呈现类似趋势。调节时间从1.0052 ms略有下降至1.0051 ms。上升时间从8.1978 μs提高到8.1878 μs。最大超调量从86.79%降至86.57%。尽管变化较小,但在所有指标上保持一致。基于FLC的控制器在动态响应和稳定性方面优于基于PI的控制器。它们能更好地处理系统非线性和延迟,使其更适合混合可再生能源系统。在所有测试配置中,FLC-MRFO设置的整体性能最佳。其卓越的适应性、更小的超调量和更快的响应验证了其在强大而高效的电力系统控制中的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83e3/12432210/3eefa1e5ebc9/41598_2025_18167_Fig1_HTML.jpg

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