On Some Subjects In Power System State Estimation

Power system state estimation is the core of electric energy management system and the bases of dispatch, control, security evaluation and so on. Since state estimation was introduced into power systems, many results have been obtained in aspects related to power system state estimation. The harvests can be witnessed in estimation criterion, detection and identification of bad data, correction of bad data, observability of system, measurement configuration as while as in stability of state estimation algorithms, state estimation with equality and inequality constraints, state estimation blocking and parallel calculation, state estimation of distribution networks, robust state estimation of power system. Researches on the determination of weight value and weight function in weighted estimation algorithm, estimation and identification of system parameters, together with dynamic power system state estimation and various subjects on the applications of new technique and theory could also be the markers of the improvements.However, problems still exist in certain aspects of power system state estimation. As the improvement of techniques and the changes of requirements on power system state estimation, amelioration becomes necessary in some fields of power system.This thesis focus on the problems, including the principle of state estimation, iteration calculation, state estimation and analysis in large scale power network, detection and identification of bad data, regression and prediction of state trajectories. Ordinary method of power system state estimation is first discussed in this thesis, and then discussion was carried out on equivalent current measurement transformation of power system state estimation, constrained power system state estimation and the applications of orthogonal transformation in power system state estimation.In large scale power systems, network blocking and equivalence analysis is an effective way to enhance real-time applications and the efficiency of power system analysis. On the basis of previous equivalence and blocking algorithms, a network blocking algorithm of power system analysis based on branch cutting is proposed, which would make large scale power system analysis get solved byanalyzing some small scale power system and calculating the consistent variables. This algorithm is simple, and can be compatible with previous power flow calculation method, what is more, it can be easily to realize distributed computing and accelerate the computation speed of large scale power system analysis. Simulation results show the validity of this algorithm.A new algorithm of power system state estimation based on network blocking using branch cutting method is suggested. On the basis of power flow calculation and network blocking using branch cutting method, the power system is divided into several smaller subsystems, so that state estimation iterations can be processed asynchronously. Problems related to this algorithm about reference bus, detection and identification of bad data, together with the resolutions are discussed.Detection and identification of bad data is a key factor in power system state estimation. A new algorithm of detection and identification of bad data is detailed. The current used algorithm, which detects and identifies bad data after estimation, is difficult to avoid residual mask and residual transfer phenomenon, so it is hard to obtain optimal estimation results. Through analyzing the correlativity of measurement and the correlativity of residual, a method to detect bad data is presented, making use of correlation coefficient of measurement variations. Simulation results show that this algorithm is able to distinguish bad data from sudden change data, meanwhile it can identify multi bad data once with low failing ratio. According to the correlativity of the measurements, topology structure and parameters of system, a new method is brought forward to identify measurement noise in the collection of suspicious bad data by means of estimation. The two algorithms constitute an integrated way to detect, identify and correct bad data before state estimation, which effectively guarantees the validity and efficiency of the state estimation.Applications of various new theories and techniques are one of the ways to accelerate the development of power system state estimation. Support vector machine regression is one of the popular regressions based on statistical-learning theory. Algorithms based on support vector machine regression in power system state estimation are introduced. Support vector machine regression and least square support vector machine regression are respectively used to accomplish one-step prediction of the system state, and then the iteration calculation of system statescan be achieved utilizing previous state estimation algorithms. Considering the operation characteristic of power system, support vector machine regression and least square support vector machine regression are applied to fulfill the training of active power model and reactive power model, to accelerate model training. According to the prediction of system state using the trained model, bad data will be detected and identified.Simulation results indicate that support vector machine regression is of superior performances in state tracking, noise-tolerant and robustness.