Studies On Dynamic Power System Modeling And Grid Computing

With the rapid growth of national power demands, the national power grid has become one of the largest man-made industry complex networks. The development of complex system-based modeling and simulation procedure can help planners and operators to cope with large power system security assessment situations. The main object of the research described in this thesis is to include these complex system-based modeling methods and grid computing techniques. The main contributions of the dissertation include the following.1. A wide-area power system identification method is present for large scale power system parallel modeling. The modified Kalman filter based PMU bad data monitoring and system identification methods are firstly used to identify the line parameters of the power system model. Secondly, stochastic differential equations and maximum likelyhood estimation based methods are introduced for the purpose of modeling generators.Thirdly, the parameters of static load models or dynamic load models are estimated using system identification methods. PMU expermental results of different scale power systems show that the proposed system identification methods can model the power system accurrately and efficiently.2. A grid computing resource static scheduling based parallel transient simulation method of power system is present. In order to improve the parallel computational efficiency, the following speedup techniques are used such as first-order Adams boundary system parameter prediction, improved first-order Adams multistep prediction, hybrid methods to solve the algebraic equations, and other strategies. Different scal experimental results show that the proposed algorithm can be used to solve the transient simulation problems of large scale power systems. To consider the improved multi-step parameter prediction strategy, the simulation time of up to 14.8% can be saved. And the near linear parallel speedup can be obtained.3. The grid computing resource adaptive scheduling based parallel transient stability simulation is studied. Considering the characteristics of"high cohesion-low coupling"and the heterogeneous of dynamic grid computing resources, an adaptive scheduling strategy is introduced. The stategies for improving the parallel simualtion of large scale power systems are as follows. Firstly, the multilevel kernel learning-based grid partition method is present. Secondly, the adaptive instruction level parallelism is used to improve the parallel effiency of multiple processes. Thirdly, the strategies of adaptive step size adjustment and early termination of transient stablitliy simulation are produced for the propuse of saving calculation tasks. And finaly, the adaptive multistep simulation method is introduced to consider the convergency differences of multiareas. Grid computing expermental results of different scale power systems show that the proposed adaptive method can improve the computation efficiency.4. To consider the three dimentional coordinate and grid computing technique of transient simulation, a parallel security asseement algorithm is present. In the algirithm, the domain partitioning based parallel method is firstly proposed to ensure the regional exchange plans and the convergence of power flow calculating. The random failure multiple time parallel analysis method of N-x and the attack multiple time parallel analysis method of vulnerability are secondly introduced for the static security assessment. And finally the feature-based functional parallelism method is used for the purposes of decision-making support and margin assessment. The simulation results of large scale power system show that this algorithm is feasible and has a high computational efficiency. It provides a viable approach for the security assessment with parallel transient stability analysis in grid computing research.5. A grid computation-based dynamic security assessment framework and demonstration platform is present. Firstly, taking into account the industrial standard of grid computation implementation, a globus toolkit-based prototype is introduced and analysed. To overcome the shortcomings of traditional globus-based simulation systems, an improved .NET-based grid computing platform for dynamic security assessment has been developed. In the proposed .NET grid computing environment, different computing resources, such as PCs, shared memory machines and clusters, are organized with high efficiency. Data-level parallelism tasks or functional parallelism tasks can be executed in different software operation systems. A demonstration application to Shanghai power grid shows that the proposed grid computing platform has many dynamic security assement functions. Predictably, more and more dyanmice security assessment simulation applications will be included in this type of grid computing enviorment.