Abstract

Traction networks are attractive due to their advantages such as step-down of high voltage, precise speed control, energy efficiency, and lower maintenance. However, the railway traction loads inject a massive amount of harmonics into the traction power system, which leads to impacts such as thermal stress, mechanical fatigue, capacitor failure, electromagnetic interference, and high energy consumption, degrading the system performance. Hence, Total Harmonic Distortion (THD) mitigation is mandatory to improve operational stability and reliability performance of the traction network. Therefore, the research work considers the Flexible Cunning Search optimization-based Light Gradient Boost Machine Convolutional Neural Network (FCS-optimized GBCN) model to mitigate the harmonic distortion in the Traction Power Supply System (TPSS). The developed model adopts the time-varying and non-linear nature of harmonics to perform a more accurate and robust harmonic mitigation task. Especially, the FCS-optimized GBCN model predicts the harmonic distortion signal to remove the harmonics, which makes a clean sinusoidal waveform. Furthermore, the Flexible Cunning Search (FCS) optimization improves the solution quality, reduces the probability, and finds optimal solutions, which contributes to enhancing the model performance. Consequently, the FCS-optimized GBCN Model outperforms the switching output, train 1 current, train 2 current, train 1 voltage, train 2 voltage, power, and THD values are 4997, 2388.64 A, 846.49 A, 472.97 V, 538.70 V, 28022 W, and 0.68, respectively.

Keywords

Light Gradient Boosting Machine Classifier, Traction Power Supply System, Convolutional Neural Network, Total Harmonic Distortion and Harmonics,

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