Resilient AFE Drive Control using Neural Networks with Tracking Guarantees

TL;DR

This paper introduces a neural network-based control method called reference-tracking Performance Boosting (rPB) to enhance the fault resilience of active front-end (AFE) drives, ensuring safety and stability during grid faults.

Contribution

It presents a novel neural network control framework that improves AFE drive resilience to faults while maintaining steady-state performance and safety constraints.

Findings

Successfully maintains DC bus voltage during faults

Keeps grid current within safe limits during phase loss

Activates only during grid faults, not during normal operation

Abstract

Industrial installations across several sectors have seen a dramatic increase in productivity, accuracy and efficiency over the last decade due to expanded utilization of medium voltage, variable speed power electronic converters to drive their processes. Specifically, active front-end (AFE) drives have become popular due to their ability to deliver power while maintaining safe electrical setpoints. However, under abnormal grid conditions such as phase loss, conventional AFE control may fail to enforce safety constraints, potentially leading to drive shutdown and significant financial losses. In this work, we propose using reference-tracking Performance Boosting (rPB) to improve the resilience of standard AFE control to faults. This neural-network control framework provides a principled way to optimize transient performance while preserving the steady-state tracking properties of AFE-based drives. By carefully shaping the input signals to the rPB controller, we ensure that it activates only during grid faults, leaving nominal operation unaffected. Simulation results show that the proposed approach successfully maintains the DC bus voltage and the grid current within safe limits during single-phase loss events.