Reinforcement Learning-Based Controlled Switching Approach for Inrush Current Minimization in Power Transformers

Abstract

Transformers are essential components for the reliable operation of power grids. The transformer core is constituted by a ferromagnetic material, and accordingly, depending on the magnetization state, the energization of the transformer can lead to high magnetizing inrush currents. Such high amplitudes shorten the life expectancy of a transformer and cause power quality issues in power grids. Various techniques have been proposed to minimize the inrush current; however, the application of Reinforcement Learning (RL) for this challenge has not been investigated. RL incorporates the ability to learn inrush minimization strategies adjusted to the dynamic transformer operation environment. This study proposes an inrush current minimization framework by combining controlled switching with RL. Depending on the opening angle of the circuit breaker and the remanent fluxes at disconnection, the proposed method learns the optimal closing instant of the circuit breaker. Two RL algorithms have been trained and tested through an equivalent duality-based model of a real 7.4 MVA power transformer. The evaluation of the RL algorithms is carried out with real measurement data and compared with real laboratory inrush currents. The results show that the inrush current is effectively minimized with the proposed framework.