Эффективность применения динамического метода оценивания состояния параметров режима электроэнергетической системы

Abstract

The results of the estimation of power system mode parameters are used to solve important technological tasks by real-time hardware-software packages (HSPs), for instance, the calculation of maximum allowed power flows (MAPFs) via sections by a Control System of Stability Margin (HSP CSSM). Now, in the HSP CSSM the state estimation is realized by the static method. Remote measurements (RMs) obtained from an operative informational complex are used as initial data. With the introduction of Wide-Area Measurement Systems and the possibility to obtain synchronized phasor measurements (SPMs) with a high update rate, it becomes possible to apply and improve state estimation dynamic methods. Even though, many researchers pay attention to the state estimation dynamic method, but practical application of this method and obtained results are presented in papers insufficiently. The goal of the study is to improve the state estimation dynamic method based on the extended Kalman filter and analyze the effectiveness in determining the mode parameters of electric power system. The studies are performed by the developed algorithm of the state estimation dynamic method based on extended Kalman filter. С# is the language for software code. Practical evaluation of the state estimation algorithm has been carried out on the basis of a power system model containing 55 nodes and 76 branches. An improved dynamic method to estimate the state of mode parameters is proposed. The test results show that in steady-state modes, when RMs are not updated on time, the developed dynamic method demonstrates high accuracy for the estimation of mode parameters and MAPFs. The estimation error of a voltage and an active power is low, therefore MAPFs are more specifically than MAPFs obtained by CSSM. Also, this method operates with high accuracy in the post emergency states, but only for that part of the power system, where the topology and mode have not been changed. For the part, where the topology and mode affected, the best result shows the static state estimation method by RMs and SPMs. In post emergency states the static state estimation method offers to form the transfer matrix for the dynamic method, therefore, static and dynamic state estimation methods must be used simultaneously in real-time HSPs. It is an undoubted fact that the use of synchronized phasor measurements as input data increases the accuracy of estimation. These results are expected to implement in the software of HSPs, involving the state estimation component.