Pub Date : 2024-01-08DOI: 10.37256/jeee.3120243655
T. Johnson, S. Banu, T. Moger
The main aim is to estimating the voltage profile at all the buses in the system before the arrival of next set of hybrid measurements from field. The effectiveness of the algorithm ISCKF during load variations is evaluated with respect to already implemented Kalman filter approaches for this application. This includes the sudden changes in loads which occurring in practical power systems. The utilization of an iterated square-root cubature Kalman filter (ISCKF) for power system forecasting-aided state estimation (FASE) is being studied during normal load variations. Its implementation involves "Newton-Gauss iterative method being embedded into the square-root cubature Kalman filter (SCKF)" at the measurement update step of Kalman filter. The square-root factor of error covariance matrices is calculated by utilizing QR decomposition to avoid losing of positive definiteness of the matrix. The estimation is carried out utilizing hybrid measurements from remote terminal units and phasor measurement units. The state vector is forecasted using the proposed method in the interval period between two measurement arrivals from the devices. Thereby, caters to state estimation of the voltages at buses in the system even when the measurements are unavailable. The efficacy of the proposed algorithm to FASE is evaluated for IEEE 30-bus system and Northern Region Power Grid (NRPG) 246-bus system. The simulation results show that the proposed ISCKF outperforms the CKF by significant improvement in accuracy of forecasting-aided state estimation. ISCKF will be able to give results before the next set of hybrid data arrives (expected from an estimation algorithm). Therefore, the proposed estimation algorithm is applicable for real-time practical application, with respect to large power systems as well.
主要目的是在下一组现场混合测量数据到来之前，估算出系统中所有母线的电压曲线。在负载变化期间，对 ISCKF 算法的有效性进行了评估，并将其与卡尔曼滤波法相比较。这包括实际电力系统中出现的负载突变。在正常负荷变化期间，正在研究利用迭代平方根立方卡尔曼滤波器（ISCKF）进行电力系统预测辅助状态估计（FASE）。其实施包括在卡尔曼滤波器的测量更新步骤中将 "牛顿-高斯迭代法嵌入平方根立方卡尔曼滤波器（SCKF）"。利用 QR 分解法计算误差协方差矩阵的平方根因子，以避免矩阵失去正定性。利用远程终端设备和相位测量设备的混合测量进行估计。利用所提出的方法，可在设备两次测量到达之间的间隔期内预测状态矢量。因此，即使在无法获得测量值的情况下，也能对系统中总线的电压进行状态估计。针对 IEEE 30 总线系统和北部地区电网 (NRPG) 246 总线系统，对所提出的 FASE 算法的有效性进行了评估。仿真结果表明，所提出的 ISCKF 在预测辅助状态估计的准确性方面明显优于 CKF。ISCKF 能够在下一组混合数据到达之前给出结果（估计算法的预期）。因此，所提出的估计算法适用于实时实际应用，也适用于大型电力系统。
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