| Power system static voltage stability and the small disturbance rotor angle stability analysis are involved in this thesis. The voltage stability analysis includes the studies of algorithms for the quadratic continuation power flow (QCPF) and the probabilistic continuation power flow (PCPF). Various FACTS devices are considered in the probabilistic eigenvalue computation for the rotor stability analysis.In voltage stability analysis, the concern is the saddle-node bifurcations (SNBs) that indicate the transmitted electric power limits and critical voltage values. These points are also called as critical, collapse or turning points. There are many methods to obtain the critical points, and the continuation power flow (CPF) is one of the effective techniques in recent years. In the CPF, a load parameter A is often introduced. With the continual increasing of load parameter A, power flow equations are resolved, and then the PV curve of the power system will be obtained. The main problem in the CPF is the predictor-corrector scheme, but the application of this scheme is based on the one order Taylor representation. In the prediction process, the initial value of next load level point is often predicted by selecting a certain step along the tangent direction. Because of the introduction of the load parameter, a complementary equation is required, which makes the correction process diversified for the difference of the complementary equation. The vertical-horizontal iteration technique will overcome the singularity of the Jacobian matrix near the critical point.By incorporating the constant power flow computation retaining the second order terms into the CPF for voltage stability analysis, the algorithm of quadratic continuation power flow (QCPF) is formulated in this thesis. With nodal voltages expressed in the rectangular form, nodal power equations can be expanded in complete representation with the second order terms retained. Second order prediction of initial point makes the estimations of state variables of each load level more close to their exact values. By the local parameterization and the vertical-horizontal iteration technique, the singularity in the Jacobian can be avoided easily. From the determination of formulas, the forms of iteration equations in the prediction and correction processes are the entirely same, which makes the prediction and correction processes incorporated. Meantime, the application of second order iteration method-3-makes it possible to fully use the advantage of fixed Jacobian matrix and corresponding factor-table technique, which largely reduces the computational requirement. Results indicate that, for one times computation, the running time for QCPF is about 1/2-2/3 of that for the constant continuation power flow. Although the time saved is limit in one times computation, the advantages will becomes more evident in some cases when a large amount of computation is required, such as that in voltage stability control.The constant power flow computation and the CPF are all based on the deterministic system operating conditions and the constant network parameters. Because of the operation of a power system is always disturbed by continuous load changes and various random factors, it is desired to consider more uncertainties and operating states into one numerical calculation. The probabilistic theory has been successfully applied in many aspects of power system analysis. For example, in probabilistic power flow studies, each nodal injection is corresponded to one typical daily operation curve and described by statistical nature (the mean and variance ); by useing the probabilistic power flow analysis, the statistical nature of each nodal voltage, line power and other index can be obtained.Based on these, the QCPF is introduced into the probabilistic circumstance. More system operating conditions and load variations, as well as the variation dependency are considered to determine the related equations. An algorithem for the probabilistic continuation power flow (PCPF...
|
PDF Full Text Request