AI 中文总结
针对多端口自主可重构太阳能发电厂（MARS）的电容电压不平衡及稳定性问题，提出机器学习辅助的能量平衡控制准则，经仿真与硬件在环测试验证可提升系统效率与运行范围。
AI 中文摘要
多端口自主可重构太阳能发电厂（MARS）是将可再生能源和储能系统接入交流电网及高压直流（HVdc）联络线的有前景方案。在MARS系统中，各类输入电源通过直流-直流转换器连接至各子模块（SM），但外部电源的存在会导致SM电容电压不平衡，进而在多种/不同运行工况下违反稳定性约束。本文旨在通过精确确定MARS系统的稳定边界填补该研究空白，提出一种新型机器学习（ML）辅助的能量平衡控制（EBC）准则。结合改进型EBC，该方法确保各类SM的电容电压平衡，显著提升整体系统效率；所提EBC准则可有效控制EBC的激活与去激活，达到优异精度。通过PSCAD/EMTDC仿真及控制硬件在环（cHIL）测试验证了所提方法的可行性与效率，将EBC与ML辅助EBC准则结合，可为含多输入电源的系统（如MARS）实现高效能量管理，使系统在扩大的运行范围内充分发挥潜力，同时维持高效率标准。
英文摘要
The multiport autonomous reconfigurable solar power plant (MARS) is a promising solution to integrate renewable energy resources and energy storage systems into the ac power grid and HVdc links. In the MARS system, various input power sources are connected to the individual submodules (SMs) through dc-dc converters. However, the presence of external power sources can result in unbalanced capacitor voltages of SMs, thereby violating stability constraints under multiple/diverse operating conditions. This paper aims to address the research gap by accurately determining the stability boundary for the MARS system. A novel machine learning (ML)-assisted energy balancing control (EBC) criterion is proposed. In conjunction with a refined EBC, this approach ensures balanced capacitor voltages across various types of SMs, significantly enhancing the overall system efficiency. The proposed EBC criterion effectively controls EBC activation and deactivation, achieving remarkable accuracy. Both PSCAD/EMTDC simulations and control hardware-in-the-loop (cHIL) tests are conducted to validate the feasibility and efficiency of the proposed method. By combining the EBC and ML-assisted EBC criteria, efficient energy management becomes achievable for systems featuring multiple input power sources, such as MARS. This approach enables the system to fully exploit its potential across an expanded operational range while upholding high efficiency standards.
