Study On Detecting And Identifying And Correcting Bad And Wrong Data In Power System

The safety and stability of the power system count on the validity of the real-time data coming out of the power grid. And nowadays, the increasing complicacy of the power grid leads to the rapid increase of the already-huge quantity of the real-time data. In this dissertation, the detection, identification and correction of the bad or wrong data in the power system were investigated thoroughly.Firstly, aiming at the massive data from remote measurement, in order to decrease the amount of the calculation and speed the calculation, the gap statistics algorithm (GSA) was improved by importing and linearizing the gap statistics, the corresponding attestation of which was also given. The simulation of two examples shows that the improved gap statistics algorithm identifies bad data with a smaller amount of computation and a faster computing speed.Secondly, aiming at the distinction of the number, type and correlation, respectively, of bad data, three methods based on land-voltage, node power balance and BP neural network, respectively, were proposed to correct bad data, which are applicable in different situations. The simulation results show that the three methods can correct bad data effectively with a high precision in their own application scope.Finally, aiming at the wrong data arising from teleindication, a Boolean-calculation method based on telemetering was proposed to overcome the problem on detecting, identifying and correcting wrong data, when the amount of the dad data is enormous. Through simulations of two examples in different circumstances, respectively, the validity of this method in detecting, identifying and correcting wrong data was improved. And the simulation-result also shows that the simplicity and high efficiency of the method proposed in this dissertation make it completely valid in practice.