Biblio

Account data of electrical power system is the link of all businesses in the whole life cycle of equipment. It is of great significance to improve the data quality of power equipment account data for improving the information level of power enterprises. In the past, there was only the error correction technology to check whether it was empty and whether it contained garbled code. The error correction technology for same field of the same kind of power equipment account data is proposed in this paper. Combined with the characteristics of production business, the possible similar power equipment can be found through the function location type and other fields of power equipment account data. Based on the principle of search scoring, the horizontal comparison is used to search and score in turn. Finally, the potential spare parts and existing data quality are identified according to the scores. And judge whether it is necessary to carry out inspection maintenance.

Online Dynamic Security Assessment (DSA) is a dynamical system widely used for assessing and analyzing an electrical power system. The outcomes of DSA are used in many aspects of the operation of power system, from monitoring the system to determining remedial action schemes (e.g. the amount of generators to be shed at the event of a fault). Measurement from supervisory control and data acquisition (SCADA) and state estimation (SE) results are the inputs for online-DSA, however, the SE error, caused by sudden change in power flow or low convergence rate, could be unnoticed and skew the outcome. Therefore, generator shedding scheme cannot achieve optimum but must have some margin because we don't know how SE error caused by these problems will impact power system stability control. As a method for solving the problem, we developed SE error detection system (EDS), which is enabled by detecting the SE error that will impact power system transient stability. The method is comparing a threshold value and an index calculated by the difference between SE results and PMU observation data, using the distance from the fault point and the power flow value. Using the index, the reliability of the SE results can be verified. As a result, online-DSA can use the SE results while avoiding the bad SE results, assuring the outcome of the DSA assessment and analysis, such as the amount of generator shedding in order to prevent the power system's instability.