首页> 外文会议>IEEE Power and Energy Conference at Illinois >Impact of measurement selection on load model parameter estimation
【24h】

Impact of measurement selection on load model parameter estimation

机译:测量选择对负荷模型参数估计的影响

获取原文

摘要

Measurement-based load model parameter estimation uses measurements from a disturbance on the grid. Those measurements can include voltage and/or real and reactive power. In this paper, we show that the type of measurements used directly impacts the accuracy of parameter estimation. We look at four scenarios. With wide-area deployment of voltage sensors, such as PMUs, the resulting parameter estimation is very accurate at high signal-to-noise ratios (SNR), but is very poor at low SNRs, because voltage has low sensitivity to the parameters. With only local deployment of complex power sensors, the estimate is worse than the first scenario at all SNRs. However, with wide-area deployment of complex power sensors, the estimate becomes very robust to low SNR, because complex power has much higher sensitivity to the parameters. Combining wide-area voltage and power measurements produces the best results.
机译:基于测量的负载模型参数估计使用来自网格上的干扰的测量。这些测量可以包括电压和/或实际和无功功率。在本文中,我们表明使用的测量类型直接影响参数估计的准确性。我们看四种情景。随着电压传感器(如PMU)的广域展开,所得到的参数估计在高信噪比(SNR)处非常精确,但在低SNR时非常差,因为电压对参数的敏感性低。只有当地部署复杂的电源传感器,估计比所有SNR的第一个场景更差。但是,随着复杂功率传感器的广域部署,估计变得非常强大,低SNR,因为复杂的功率对参数具有更高的敏感性。相结合的广域电压和功率测量产生了最佳效果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号