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基于APS-IPSO的构网型储能跟网-构网双模式切换策略
陈逍阳, 李晨阳, 徐恒山, 马鑫, 米玛, 索朗平措
电力建设 ›› 2026, Vol. 47 ›› Issue (1) : 15-24.
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PDF(2609 KB)
基于APS-IPSO的构网型储能跟网-构网双模式切换策略
Grid-forming and Grid-Following Dual-Mode Switching Strategy Based on APS-IPSO for Grid-Forming Energy Storage
【目的】针对构网型储能因变流器单一运行模式难以适应电网短路比变化及复杂故障扰动的问题，提出了一种基于幅相同步（amplitude and phase synchronization，APS）的双模式改进切换策略，并使用改进粒子群优化（improved particle swarm optimization，IPSO）算法对其关键参数进行辨识。【方法】首先，分析了常规跟网-构网模式切换策略存在的不足，揭示了功率环电压相位累计误差及其造成的无功电压偏差使得模式切换时暂态冲击增大的机理；基于此，提出了幅相同步补偿机制，在模式切换过程中同步修正电压幅值和相位信号，实现了内环电流参考信号的平滑变化；然后，针对常规切换策略中跟踪环参数难以整定的缺点，采用基于非线性惯性权重和学习因子的IPSO算法对4组跟踪环参数进行自适应辨识以提升储能变流器对跟网和构网模式运行点的跟踪性能和扰动抑制效果。【结果】在Matlab/Simulink里搭建MW级构网型储能电磁暂态模型开展验证，结果表明：所提控制策略可以顺利实现暂态功率冲击小于0.02 p.u.，且在连续切换和运行点波动场景下均能稳定运行。【结论】相比于常规切换策略，基于APS-IPSO的改进策略可实现储能变流器在跟网-构网模式切换中冲击小、稳定性好，为后续新能源场站内部署具备模式切换的储能或新能源机组提供了理论基础。
[Objective] Addressing the challenge that grid-forming energy storage converters，operating in a single mode，struggle to adapt to variations in grid short-circuit ratio and complex fault disturbances，this paper proposes a dual-mode switching strategy based on amplitude and phase synchronization（APS），and uses the improved particle swarm optimization（IPSO）to identify its key parameters. [Methods] First，the limitations of conventional grid-forming/grid-following switching strategies are analyzed. The mechanism by which the cumulative voltage phase error in the power loop induces reactive power/voltage deviations，thereby amplifying transient impacts during mode switching，is revealed. Based on this，an APS compensation mechanism is proposed to simultaneously correct the voltage amplitude and phase signal during the mode switching process，ensuring smooth changes in the inner-loop current reference signal. Second，to overcome the difficulty in tuning the parameters of the conventional strategy's tracking loops，an IPSO algorithm based on nonlinear inertia weights and learning factors is used to adaptively identify the parameters of the four sets of tracking loops. This enhances the tracking performance and disturbance suppression effect of the energy storage converter on the operation points of the grid-following and grid-forming modes. [Conclusions] Validation was conducted via an electromagnetic transient model of a MW-level grid-forming energy storage system built in MATLAB/Simulink. The results showed that the proposed control strategy could successfully achieve a transient power impact of less than 0.02 p.u.，and could operate stably in the scenarios of continuous switching and operation point fluctuation. [Conclusions] Compared with the conventional switching strategy，the APS-IPSO-based strategy enables energy storage converters to achieve low-impact switching and high stability during grid-following to grid-forming transitions，providing a theoretical basis for the subsequent deployment of energy storage or new energy units with mode switching in new energy stations.
构网型储能 / 弱电网 / 恒功率控制 / 虚拟同步机 / 控制模式切换
grid-forming energy storage / weak power grids / constant power control / virtual synchronous generator / control mode switching
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针对在局部阴影情况下光伏阵列的功率-电压（P-U）特性曲线呈多峰特性,粒子群算法应用于局部阴影下的最大功率点跟踪（MPPT）跟踪,存在搜索速度慢、精度低的缺点。提出自适应惯性权重粒子群优化（PSO）算法的最大功率点跟踪算法,自动更新惯性权重w和学习因子C<sub>1</sub>、C<sub>2</sub>,通过仿真实验,优化前的全局最大功率点（GMPP）跟踪时间是0.045 s,输出功率为468 W。优化后的自适应粒子群算法GMPP跟踪时间为0.020 s,输出功率稳定在为480 W,光伏阵列的输出功率跟踪误差小于30%。在所搭建辐照度突变模型仿真中,在4.022 s突变到300 W/m<sup>2</sup>时经过0.05 s又重新跟踪到了新的最大功率点稳定在0.075 MW。最后通过实验平台验证,优化后的自适应粒子群优化算法与传统的粒子群优化算法相比,追踪时间减少了55.5%,误差小于5%,验证了该算法可行性和实用性。
In view of the multi-peak characteristic of the power-voltage characteristic curve of photovoltaic array in the case of local shadow, particle swarm optimization algorithm is applied to MPPT tracking in the case of local shadow, which has the disadvantages of slow search speed and low accuracy. The maximum power point tracking algorithm of adaptive inertia weight particle swarm optimization algorithm is proposed to automatically update the inertia weights <em>w</em> and learning factors <em>C</em><sub>1</sub> and <em>C</em><sub>2</sub>. Through simulation experiments, the GMPP time before optimization is 0.045 s and the output power is 468 W. After optimization, the GMPP time of the adaptive particle swarm algorithm is 0.02 s and the output power is stabilized at 480 W. The output power tracking error of the PV array is less than 30%. In the simulation by the irradiance mutation model established in this paper, the tracking time is reduced by 55.5% and the tracking error is less than 5% compared with the traditional particle swarm algorithm. The feasibility and practicability of the algorithm are verified.
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