虚拟同步发电机参数自适应调节

Parameter adaptive strategy for virtual synchronous generator control

中文关键词  电网  暂态稳定性  虚拟同步发电机  系统阻尼  自适应控制

英文关键词  power grid  transient stability  virtual synchronous generator  system damping  adaptive control

中文摘要

      虚拟同步发电机技术借由控制器设计达到模拟同步发电机外特性的效果, 改善了含有新能源的电网系统 的稳定性. 根据虚拟同步发电机参数可调的特点, 可采用参数自适应调节优化系统动态性能. 但传统虚拟同步发电 机参数自适应调节仍存在两方面不足: 一方面, 大范围调整阻尼下垂系数和虚拟转动惯量, 对系统储能裕量要求高; 另一方面, 暂态调节过程中存在功率超调现象, 对电力设备不利. 本文引入输出速度反馈调节系统阻尼, 为改善系 统暂态稳定性提供了方便的调节手段; 其次, 根据系统暂态功角特性, 提出了参数自适应控制策略, 缩短了调节时 间, 将频率偏差减小至允许范围内, 改善了电网频率调节的暂态性能, 同时抑制了功率超调. 最后, 对比实验结果表 明本文控制策略优于现有的自适应控制策略.

英文摘要

      The virtual synchronous generator technology analogs the characteristics of the synchronous generator via the controller design. It improved the stability of the grid systems which include the new energy. At the same time, according to the adjustable characteristics of the virtual synchronous generator parameters, the parameter adaptive adjustment is used to improve the dynamic performance of the system. However, the traditional virtual synchronous generator adaptive control technology still has two drawbacks: on the one hand, the large-scale adjustment of the damping droop coefficient and the virtual moment of inertia requires the system having a high energy storage margin; On the other hand, there is a power overshoot phenomenon in the transient regulation process, which is disadvantageous to the power equipment. First, this paper provides a convenient adjustment method for improving the transient stability of the system, the system damping is adjusted by introducing the output speed feedback. Second, according to the transient power-angle characteristics of the system, a parameter adaptive control strategy is proposed, which shortens the transient adjustment time and ensures that the deviation of the system frequency in the transient adjustment process is within the allowable range, and improves the transient performance of the grid frequency adjustment, at the same time, the power overshoot is suppressed. Finally, the experimental results show that the proposed control strategy is superior to the existing adaptive control strategy.