Impact Of Parameter Errors Research Articles

Derivative-Free Kalman Filtering Based Approaches to Dynamic State Estimation for Power Systems With Unknown Inputs

This paper proposes a decentralized derivative-free dynamic state estimation method in the context of a power system with unknown inputs, to address cases when system linearization is cumbersome or impossible. The suggested algorithm tackles situations when several inputs, such as the excitation voltage, are characterized by uncertainty in terms of their status. The technique engages one generation unit only and its associated measurements, and it remains totally independent of other system wide measurements and parameters, facilitating in this way the applicability of this process on a decentralized basis. The robustness of the method is validated against different contingencies. The impact of parameter errors, process, and measurement noise on the unknown input estimation performance is discussed. This understanding is further supported through detailed studies in a realistic power system model.

Position and orientation error analysis and its compensation for a wheeled train uncoupling robot with four degrees‐of‐freedom

A wheeled train uncoupling robot with four degrees-of-freedom has been developed to replace humans in the uncoupling task in a marshalling field for designating freight cars to different destinations. To successfully achieve the task in practical applications, the positioning accuracy of the robot is an important issue to be considered. Based on the kinematic model using Denavit–Hartenberg method, the matrix differential method is applied here to establish the static position and orientation error model. The impact of parameter errors upon the static pose error of the uncoupling manipulator is analysed. The flexibility of the robot's key components is taken into consideration to analyse its impact on the position and orientation error of the manipulator. The position and orientation error compensation is developed by using input motion planning method to improve the pose accuracy of the robot. Additional motions are added to each joint of the robot such that the uncoupling manipulator can generate a corresponding tiny perturbation, which is used to eliminate the positioning error, ensuring the uncoupling action is completed successfully.

Role of parameter errors in the spring predictability barrier for ENSO events in the Zebiak-Cane model

The impact of both initial and parameter errors on the spring predictability barrier (SPB) is investigated using the Zebiak-Cane model (ZC model). Previous studies have shown that initial errors contribute more to the SPB than parameter errors in the ZC model. Although parameter errors themselves are less important, there is a possibility that nonlinear interactions can occur between the two types of errors, leading to larger prediction errors compared with those induced by initial errors alone. In this case, the impact of parameter errors cannot be overlooked. In the present paper, the optimal combination of these two types of errors [i.e., conditional nonlinear optimal perturbation (CNOP) errors] is calculated to investigate whether this optimal error combination may cause a more notable SPB phenomenon than that caused by initial errors alone. Using the CNOP approach, the CNOP errors and CNOP-I errors (optimal errors when only initial errors are considered) are calculated and then three aspects of error growth are compared: (1) the tendency of the seasonal error growth; (2) the prediction error of the sea surface temperature anomaly; and (3) the pattern of error growth. All three aspects show that the CNOP errors do not cause a more significant SPB than the CNOP-I errors. Therefore, this result suggests that we could improve the prediction of the El Nio during spring by simply focusing on reducing the initial errors in this model.

Applied tracer tests in fractured rock: Can we predict natural gradient solute transport more accurately than fracture and matrix parameters?

Applied tracer tests provide a means to estimate aquifer parameters in fractured rock. The traditional approach to analysing these tests has been using a single fracture model to find the parameter values that generate the best fit to the measured breakthrough curve. In many cases, the ultimate aim is to predict solute transport under the natural gradient. Usually, no confidence limits are placed on parameter values and the impact of parameter errors on predictions of solute transport is not discussed. The assumption inherent in this approach is that the parameters determined under forced conditions will enable prediction of solute transport under the natural gradient. This paper considers the parameter and prediction uncertainty that might arise from analysis of breakthrough curves obtained from forced gradient applied tracer tests. By adding noise to an exact solution for transport in a single fracture in a porous matrix we create multiple realisations of an initial breakthrough curve. A least squares fitting routine is used to obtain a fit to each realisation, yielding a range of parameter values rather than a single set of absolute values. The suite of parameters is then used to make predictions of solute transport under lower hydraulic gradients and the uncertainty of estimated parameters and subsequent predictions of solute transport is compared. The results of this study show that predictions of breakthrough curve characteristics (first inflection point time, peak arrival time and peak concentration) for groundwater flow speeds with orders of magnitude smaller than that at which a test is conducted can sometimes be determined even more accurately than the fracture and matrix parameters.