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基于数字孪生的海上风电多相升压整流器健康监测
Digital-Twin-Based Health Monitoring for Multiphase Boost Rectifier in Wind Offshore Applications
| 作者 | Giulia Di Nezio · Marco Di Benedetto · Alessandro Lidozzi · Luca Solero |
| 期刊 | IEEE Journal of Emerging and Selected Topics in Power Electronics |
| 出版日期 | 2024年11月 |
| 技术分类 | 风电变流技术 |
| 技术标签 | 储能系统 SiC器件 |
| 相关度评分 | ★★★★★ 5.0 / 5.0 |
| 关键词 | 风力发电 电力电子转换器 数字孪生 在线实时监测 启发式优化算法 |
语言:
中文摘要
在风力等可再生能源发电领域,避免服务中断并实现计划性维护至关重要。为此,需对电力电子变换器(PECs)等关键部件进行状态监测。本文提出一种基于数字孪生(DT)的在线实时监测方法,针对海上风电系统中常用的交流-直流六相变换器,重点实现其参数估计。所构建的数字孪生体为与物理系统并行运行的实时数字模型(RTDM),采用Typhoon求解器实现小于1 μs的求解时间。结合粒子群优化(PSO)等启发式算法,可在线估计开关导通电阻、永磁同步电机定子阻抗及直流母线电容等关键参数。通过搭载相同控制算法的Typhoon HIL 606平台验证了该方法的有效性,并展示了实验结果。
English Abstract
In the field of energy generation from renewables, such as wind power, it is essential to avoid service interruption, as well as to ensure scheduled maintenance. For this reason, it is necessary to monitor critical components, such as the power electronic converters (PECs). This article proposes an online real-time (RT) monitoring method based on digital twin (DT) concept applied to PECs generally used for energy conversion in wind offshore systems, focusing on the parameters estimation of an ac-dc six-phase converter. The DT is realized as an RT digital model (RTDM) that runs in parallel to the physical six-phase boost rectifier for its entire lifecycle. The Typhoon solver is used to implement the RTDM of the six-phase boost rectifier to reach a solving time within 1~ s. By applying the heuristic optimization algorithm, such as the particle swarm optimization (PSO) method, the actual value of the monitored on-state resistances of the switches, the stator impedance of the permanent magnet synchronous machine (PMSM), and the dc-link capacitance can be found. The proposed monitoring method is validated through the RTDM implemented on Typhoon HIL 606, which is equipped with a control board that runs the same physical asset control algorithm. Experimental tests are illustrated.
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SunView 深度解读
该数字孪生健康监测技术对阳光电源储能和风电产品线具有重要应用价值。特别是对ST系列储能变流器和风电变流器的预测性维护具有直接借鉴意义。通过实时数字模型和PSO算法实现的关键器件参数在线估计,可提升iSolarCloud平台的智能诊断能力,实现设备故障预警。该技术可优化PowerTitan等大型储能系统的运维策略,降低维护成本。此外,文中提出的微秒级实时仿真方法,对SiC功率模块的性能评估和可靠性验证也具有参考价值。建议在阳光电源现有产品中植入类似的数字孪生监测模块,提升产品智能化水平。