State estimation in middle- (MV) and low-voltage (LV) electrical grids poses a number of challenges for the estimation method employed. A significant difference to high-voltage grids is the lack of measurements as the instrumentation with measurement equipment in MV and LV grids is very sparse due to economical reasons. Typically, pseudo-measurements are used as a replacement for actual measurements to this end. A recently proposed disturbance observer based on the extended Kalman filter uses a simplified dynamic model for the errors in the pseudo-measurements of bus power. The aim is then to estimate the errors in the pseudo-measurements and thereby improving the overall estimation result. Despite initial promising results of this so-called nodal load observer (NLO), the main disadvantage of this method is the need for a suitable dynamic model for the error of the pseudo-measurements. Therefore, we here propose a versatile dynamic model for the disturbance observer based on autoregressive processes (AR). We consider a recently proposed online learning algorithm for the prediction of the AR model parameters together with the extended Kalman filter disturbance observer. We demonstrate that this approach results in an efficient method for the dynamic state estimation for MV and LV grids than the original NLO method.

中文翻译：

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中压和低压电网的自适应准动态状态估计

中压（MV）和低压（LV）电网中的状态估计对所采用的估计方法提出了许多挑战。与高压电网的显着差异是缺少测量，因为出于经济原因，中压和低压电网中使用测量设备的仪器非常稀疏。为此，通常使用伪测量代替实际测量。最近提出的基于扩展卡尔曼滤波器的干扰观测器将简化的动态模型用于总线功率伪测量中的误差。然后，目的是估计伪测量中的误差，从而改善总体估计结果。尽管这种所谓的节点负载观测器（NLO）最初取得了令人鼓舞的结果，该方法的主要缺点是需要针对伪测量误差的合适的动态模型。因此，我们在此为基于自回归过程（AR）的扰动观察者提出了一种通用的动力学模型。我们考虑了最近提出的在线学习算法，用于与扩展的卡尔曼滤波器干扰观测器一起预测AR模型参数。我们证明，与原始NLO方法相比，该方法可为MV和LV网格提供一种有效的动态状态估计方法。我们考虑了最近提出的在线学习算法，用于与扩展的卡尔曼滤波器干扰观测器一起预测AR模型参数。我们证明，与原始NLO方法相比，该方法可为MV和LV网格提供一种有效的动态状态估计方法。我们考虑了最近提出的在线学习算法，用于与扩展的卡尔曼滤波器干扰观测器一起预测AR模型参数。我们证明，与原始NLO方法相比，该方法可为MV和LV网格提供一种有效的动态状态估计方法。