Short-term harmonic forecasting and evaluation affected by electrified railways on the power grid based on stack auto encoder neural network method

With the development towards high-speed, high-capacity, high-density networking of high-speed railway, harmonic problems caused by high-speed locomotives are getting more and more serious, which has been a great threat to the safety and stability of power system operation. The way of evaluation on the influence of railway becomes increasingly important. The method of short-term harmonic forecasting and evaluation affected by electrified railways on the power grid based on Stack Auto Encoder (SAE) Neural Network model was proposed in this study. At first, this paper makes an introduction and analysis on the methods of harmonic evaluation affected by electrified railways on the power grid, and the importance of short-term harmonic forecasting and evaluation affected by electrified railways on the power grid was emphasized. Then, it makes an analysis on the theory of short-term harmonic forecasting affected by electrified railways on the power grid based on Stack Auto Encoder Neural Network model, and the method of evaluation on harmonic evaluation was introduced. Then, the model and method of short-term harmonic forecasting and evaluation affected by electrified railways on the power grid based on Stack Auto Encoder Neural Network Method was proposed, which was used in the case. Besides, the results of harmonic forecasting achieves the purpose of the harmonic forecasting, and the harmonic value is evaluated using the ways of harmonic evaluation. It provides a theoretical reference for the harmonic evaluation on the influence of railway, which can also make an improvement of the power quality in power network.