论文标题
基于贝叶斯推断的碱性电解系统的在线动态参数估计
Online Dynamic Parameter Estimation of an Alkaline Electrolysis System Based on Bayesian Inference
论文作者
论文摘要
当直接与波动的能源（例如风能和光电压功率）结合时，需要在动力到水（P2H）系统中的碱性电解（AEL）来灵活地通过动态调节其氢生产率来灵活操作。 AEL系统的挠性特性，例如加载范围和坡道速率受到重新介绍到AEL系统动态过程的某些参数的显着影响。这些选项通常很难直接衡量，甚至可能随着时间而变化。为了准确评估AEL系统在在线操作中的灵活性，本文介绍了基于贝叶斯推理的Markov Chain Monte Carlo（MCMC）方法来估计这些参数。同时，获得估计参数的后近关节概率分布是作为副产品获得的，该副产品为AEL系统提供了宝贵的物理见解。在25 kW电解酶上进行的实验验证了提出的PA-型估计方法。
When directly coupled with fluctuating energy sources such as wind and photovoltage power, the alkaline electrolysis (AEL) in a power-to-hydrogen (P2H) system is required to operate flexibly by dynamically adjusting its hydrogen production rate. The flex-ibility characteristics, e.g., loading range and ramping rate, of an AEL system are significantly influenced by some parameters re-lated to the dynamic processes of the AEL system. These parame-ters are usually difficult to measure directly and may even change with time. To accurately evaluate the flexibility of an AEL system in online operation, this paper presents a Bayesian Inference-based Markov Chain Monte Carlo (MCMC) method to estimate these parameters. Meanwhile, posterior joint probability distribu-tions of the estimated parameters are obtained as a byproduct, which provides valuable physical insight into the AEL systems. Experiments on a 25 kW electrolyzer validate the proposed pa-rameter estimation method.
