基于天然气偏微分方程的综合能源系统鲁棒动态状态估计
Robust Dynamic State Estimator of Integrated Energy Systems based on Natural Gas Partial Differential Equations
摘要：
动态数据库的可靠性和准确性对综合能源系统的优化运行和全局控制至关重要。状态估计是获得精确状态的有效方法之一。提出了一种基于卡尔曼滤波器的天然气-电力电力系统鲁棒动态状态估计方法。为了充分利用量测冗余和预测来提高状态估计精度，建立了燃气轮机组耦合燃气系统和电力系统的动态状态估计模型。卡尔曼滤波器集成了指数平滑技术和气体物理模型。此外，针对卡尔曼滤波器算法中的坏数据，提出了时变标量矩阵的概念。将该方法应用于GasLib-40和IEEE 39节点系统组成的含5台燃气轮机机组的燃气-电力系统。仿真结果表明，该方法在三种不同的测量误差条件下都能获得准确的动态信息，且滤波性能优于单独的估计方法。此外，所提出的方法是强大的，当测量遇到坏的数据。
英文摘要：
The reliability and precision of dynamic database are vital for the optimal operating and global control of integrated energy systems. One of the effective ways to obtain the accurate states is state estimations. A novel robust dynamic state estimation methodology for integrated natural gas and electric power systems is proposed based on Kalman filter. To take full advantage of measurement redundancies and predictions for enhancing the estimating accuracy, the dynamic state estimation model coupling gas and power systems by gas turbine units is established. The exponential smoothing technique and gas physical model are integrated in Kalman filter. Additionally, the time-varying scalar matrix is proposed to conquer bad data in Kalman filter algorithm. The proposed method is applied to an integrated gas and power systems formed by GasLib-40 and IEEE 39-bus system with five gas turbine units. The simulating results show that the method can obtain the accurate dynamic states under three different measurement error conditions, and the filtering performance are better than separate estimation methods. Additionally, the proposed method is robust when the measurements experience bad data.
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