论文标题
基于天然气部分微分方程的集成能量系统的稳健动态估计器
Robust Dynamic State Estimator of Integrated Energy Systems based on Natural Gas Partial Differential Equations
论文作者
论文摘要
动态数据库的可靠性和精度对于集成能源系统的最佳运行和全局控制至关重要。获得准确状态的有效方法之一是国家估计。基于卡尔曼滤波器提出了一种新型的综合天然气和电力系统的强大动态估计方法。为了充分利用测量冗余和提高估计准确性的预测，建立了动态状态估计模型耦合气体和燃气轮机单元的动力系统。指数平滑技术和气体物理模型集成在卡尔曼过滤器中。此外，提出了随时间变化的标量矩阵来征服Kalman Filter算法中的不良数据。所提出的方法应用于由Gaslib-40和IEEE 39-BUS系统组成的集成气体和动力系统，该系统具有五个燃气轮机单元。模拟结果表明，该方法可以在三个不同的测量误差条件下获得准确的动态状态，并且过滤性能比单独的估计方法更好。此外，当测量结果经历不良数据时，提出的方法是可靠的。
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.
