The declining cost of solar Photovoltaics (PV) generation is driving its worldwide deployment. As conventional generation with large rotating masses is being replaced by renewable energy such as PV, the power system’s inertia will be affected. As a result, the system’s frequency may vary more dramatically in the case of a disturbance, and the frequency nadir may be low enough to trigger protection relays such as under-frequency load shedding. The existing frequency-watt function mandated in power inverters cannot provide grid frequency support in a loss-of-generation event, as PV plants usually do not have power reserves. In this article, a novel adaptive PV frequency control strategy is proposed to reserve the minimum power required for grid frequency support. A machine learning model is trained to predict system frequency response under varying system conditions, and an adaptive allocation of PV headroom reserves is made based on the machine learning model as well as real-time system conditions including inertia. Case studies show the proposed control method meets the frequency nadir requirements using minimal power reserves compared to a fixed headroom control approach.

中文翻译：

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基于实时惯性估计的自适应光伏频率控制策略

太阳能光伏（PV）发电成本的下降正在推动其在全球的部署。随着具有大旋转质量的常规发电被PV等可再生能源替代，电力系统的惯性将受到影响。结果，在发生干扰的情况下，系统的频率可能会发生更大的变化，并且频率的最低点可能会低到足以触发保护继电器（例如欠频甩负荷）。由于光伏电站通常没有电力储备，因此在发电中断事件中，功率逆变器中规定的现有频率瓦功能无法提供电网频率支持。在本文中，提出了一种新颖的自适应PV频率控制策略，以保留电网频率支持所需的最小功率。对机器学习模型进行训练以预测在变化的系统条件下的系统频率响应，并根据机器学习模型以及包括惯性在内的实时系统条件对PV净空余量进行自适应分配。案例研究表明，与固定净空控制方法相比，所建议的控制方法使用最低的功率储备即可满足最低频率要求。