Online assessment of conservation voltage reduction (CVR) has great effects on the performance of CVR-based applications, especially for real-time CVR-based controls. The challenges of online CVR assessment mainly come from time-varying load composition and inevitable noise. Besides, the dynamic process resulting from motors further complicates this problem. This article proposes a micro-perturbation based load-to-voltage (LTV) process identification method to online assess the CVR effects. In particular, we construct a well-designed pseudo-random-binary-sequence voltage perturbation to excite the CVR process continually and fully. With the experimental data, the CVR factor and transfer function of LTV are estimated by the cross-correlation method, which can reflect both steady-state and dynamic CVR effects without being affected by noise. The proposed method is verified on a specific hardware-in-the-loop testbed under various cases. In addition, through the energy-saving and power smoothing applications, the impact of CVR assessment on performance of CVR-based applications is also analyzed, which further verify the validity and economic benefits of the proposed assessment method.

中文翻译：

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在线估计微扰动的守恒电压降低效果

在线评估保护电压降低（CVR）会对基于CVR的应用程序的性能产生重大影响，尤其是对于基于CVR的实时控件。在线CVR评估的挑战主要来自随时间变化的负载组成和不可避免的噪声。此外，由电动机引起的动态过程进一步使这个问题复杂化。本文提出了一种基于微扰的负载到电压（LTV）过程识别方法，以在线评估CVR效果。特别地，我们构造了一个设计良好的伪随机二进制序列电压扰动，以连续不断地激发CVR过程。利用实验数据，通过互相关方法估计了LTV的CVR因子和传递函数，它既可以反映稳态CVR效果，又可以反映动态CVR效果，而不受噪声的影响。在各种情况下，该方法在特定的硬件在环测试平台上得到了验证。此外，通过节能和电源平滑应用，还分析了CVR评估对基于CVR的应用性能的影响，从而进一步验证了所提出评估方法的有效性和经济效益。