Yubin Yao

The effect of small impedance branches on the convergence of the Newton Raphson power flow

This paper investigates the process of the Newton Raphson (N-R) method with flat voltage start when it is used for systems with small impedance branches. And find out that the divergence occurs in the cases which both ends of the small impedance branch are connected to PQ buses. Even under the cases with the divergence problem the Jacobian matrix is still nonsingular and the equations are solvable, and the results from the equations are very precise but unreasonable. So this paper draws a conclusion that it is not the numerical instability but the characteristic of the equations of the N-R method that cause the divergence. Further investigation shows that the improvement of voltage start can avoid the divergence problem. Based on the theoretical analysis, a method to prevent the N-R method from divergence by improving voltage start is presented. Experimental results validate the convergence analysis and illustrate the effectiveness of the proposed method.

Convergence analysis on the power flow methods for distribution networks with small impedance branches

The small impedance branch is a primary factor that results in the divergence of the power flow methods. The study of the methods of power calculation in Newton-Raphson (N-R) method and backward/forward sweep (BFS) method indicates that the method of power calculation together with small impedances may result in the convergence problem. The impact of small impedance branches on the convergence of the two power flow methods is different because the two methods are different in mechanism. So the ways to improve the convergence performance of the power flow methods are also different. Based on theoretical analysis, a branch dividing method is presented which divides a transformer with small impedance into a transformer with normal impedance and a capacitive line. The proposed method can conveniently and effectively deal with the convergence problem in the BFS method caused by small impedance transformer branches, and experimental results illustrate the effectiveness of the method.

The Effect of Transformer Representation on the Convergence of the Backward Forward Sweep Method for Distribution Power Network

The small impedance branch has a great significant effect on the convergence of the power flow methods. Whether the power flow converges or not when it used for systems with small impedance branches not only depends on the power flow methods but also the representation of the network elements. The backward forward sweep (BFS) method which is widely used for distribution networks is discussed in the paper. When the BFS method is applied to systems with small impedance transformer branches, the divergence of power flow may occur. The transformer representation affects the convergence property of the BFS method greatly. The Pi equivalent circuit usually causes the divergence of the BFS method, while the standard equivalent circuit with an ideal transformer does not. The results of an experimental system validate our conclusion.

Diagnosis of Actuator Lock-in-place for Flight Control Systems

In this paper, the diagnosis problem of actuator lock-in-place is studied. Based on multiple-model structure, an adaptive unknown input observer (UIO) approach is developed to detect and isolate aircraft actuator faults. Existing multiple-model FDI (fault detection and isolation) approaches assumed that the model for each fault case is known a priori. However, in the cases of lock-in-place, the locked position is unknown and varies in the range of maximum deflections. Thus, there exists an infinite family of models which is impossible to model exhaustively. To solve this problem, we developed an adaptive approach to estimate the unknown parameters on-line. The proposed adaptive algorithms guarantee that both the residual signals and the estimation errors of the unknown parameters converge exponentially when a model matches the system. To the best of our knowledge, this is the first adaptive UIO presented.