Traction load modeling considering multiple types of locomotives and analysis of its impact on power grid

An accurate and practical traction load model is of great significance for in-depth quantitative analysis of the impact of traction load on the power grid. Traction load models applied to grids in the past are often too simple, while traction load models used in electrified railways are too complex to be directly used in grid analysis. Therefore, probabilistic models of traction loads based on locomotives have been used to balance this contra-diction. However, the previous traction load probabilistic model did not deeply consider the influence of different locomotive types on the model itself, and the relevant parameter identification algorithm used in it was not convenient enough for algorithm novice. Based on this, this paper proposes a probabilistic model of traction load considering multiple locomotive types and develops a model parameter identification method based on artificial fish swarm algorithm. This parameter identification method can directly identify continuous variables, eliminating the need for variables discretization process. In order to verify the practicability of the proposed probability model and the accuracy of the proposed parameter identification method, we verified the proposed method based on the measured traction load data. The simulation results are in better agreement with the measured distribution, which verifies the accuracy and practicability of the proposed method.