Physical–statistical learning in resilience assessment for power generation systems
Physical–statistical learning in resilience assessment for power generation systems
复制标题
发电系统弹性评估中的物理统计学习
DOI：
10.1016/j.physa.2023.128584
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Cheng, Changqing
中科院分区：
文献类型：
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作者：
Che, Yiming;Zhang, Ziang;Cheng, Changqing
极端天气条件和自然灾害的增加，加上间歇性可再生能源的无情渗透，使发电系统的恢复能力大大减轻。在这项研究中，我们采用了高阶物理模型来描述同步发电机动态中的全细节次瞬态行为，并因此利用流域稳定性（BS）来量化系统对潜在大扰动的弹性。这种高保真模型尚未被广泛探讨的BS估计，主要是由于涉及的巨大的计算开销。我们进行敏感性分析，挑选出最关键的系统状态，其扰动产生巨大的影响，因此是敏感的BS或系统弹性。在此之后，我们开发了一个多样性增强的主动学习框架，以顺序识别信息扰动状态，这将进一步评估的高保真次瞬态模型。这种方法只会引起一个微不足道的模拟工作相比，原油蒙特卡洛模拟，但具有可比的准确性BS估计。
Upswing in extreme weather conditions and natural disasters in conjunction with the relentless penetration of the intermittent renewable energy have brought resilience of the power generation systems into sharp relief. In this study, we adopt a high-order physical model to characterize the full-detail sub-transient behaviors in synchronous generator dynamics, and consequently utilize basin stability (BS) to quantify system resilience against potentially large perturbations. This high-fidelity model has not been extensive probed in estimate of BS, largely owing to the tremendous computational overhead involved. We conduct sensitivity analysis to pick out the most critical system states, whose perturbation exerts huge impact and hence are sensitive on BS or system resilience. Following this, we develop a diversity-enhanced active learning framework to sequentially identify the informative perturbed states, which will be further evaluated by the high-fidelity sub-transient model. This approach only incurs a paltry of simulation effort compared to the crude Monte Carlo simulation but with comparable accuracy on BS estimation.