Frequency Control of Low Inertia Microgids in Presence of Wind and Solar Units Using Fuzzy-neural Controllers

M. Zadeh, Zakaria Afshar, M. J. Harandi, Seyed Mohammad Taghi Bathaee
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引用次数: 2

Abstract

Balance between generating power and load demand is one of the most important challenges of power systems which directly affects frequency stability. Power imbalances can cause the operation of relays and frequency instability, as a result. On the other hand, in a microgrid, increasing the penetration factor of wind and solar energy can cause lack of frequency control due to uncertainty of generating output power and also low inertia of these resources compared to other conventional sources. Furthermore, Adjusting the gain of droop and inertia control loops is very effective on frequency stability performance. However, due to the wind speed and solar radiation fluctuations as well as load changes, it seems impossible adjusting the mentioned control coefficients with conventional PI controllers expecting the best response in all conditions. Therefore, this paper, by modeling load changes and wind and solar generating power and the participation of battery energy storage and diesel generators in the microgrid, proposes a control method based on virtual inertia compensation and also a fuzzy-neural controller for faster performance and more robustness. In the end, the results of this control strategy will be compared with other conventional controllers.
基于模糊神经控制器的低惯量微栅在风能和太阳能发电机组中的频率控制
发电功率与负荷需求之间的平衡是电力系统面临的重要挑战之一,它直接影响到电力系统的频率稳定性。功率不平衡会导致继电器的运行和频率不稳定。另一方面,在微电网中,增加风能和太阳能的渗透系数,由于产生输出功率的不确定性,以及与其他常规能源相比,这些资源的惯性较低,可能导致缺乏频率控制。此外,调整下垂和惯性控制回路的增益对频率稳定性能是非常有效的。然而,由于风速和太阳辐射的波动以及负载的变化,用传统的PI控制器期望在所有条件下获得最佳响应似乎不可能调整上述控制系数。因此,本文通过对负荷变化和风能、太阳能发电以及电池储能和柴油发电机参与微电网的建模,提出了一种基于虚拟惯性补偿的控制方法,并提出了一种性能更快、鲁棒性更强的模糊神经控制器。最后,将该控制策略的结果与其他常规控制器进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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