An identification method for power grid error parameters based on sensitivity analysis and deep residual network

The planning, scheduling and operation decisions of the power grid depend on the calculation of the simulation model. Parameter errors in the grid model can lead to deviations between simulation calculations and actual grid operation. The strategy based on incorrect calculation data will lead to power outages in the actual power grid, which may cause significant economic losses and personal safety accidents. For the safe operation of power grid, a method for locating the wrong parameters of transmission line based on sensitivity analysis (SA) and deep residual network (DRN) is proposed. By calculating the sensitivity of apparent power to parameter error of each line in the power grid, the propagation characteristics of power flow error are analyzed quantitatively. An error region segmentation method is proposed to reduce the search range of error parameters from large-scale power grids to local networks which can reduce the computational complexity of the search algorithm, and increase accuracy. An error transmission line index for local power grids is proposed to identify error source in local power grids. Then, the specific wrong parameters are identified through the DRN. It can intelligently identify the error parameters from multiple parameters of the error transmission line. The calculation results of the 300-bus system verify the correctness and effectiveness of the proposed method.