AI Explainability for Power Electronics: From a Lipschitz Continuity Perspective

TL;DR

This paper introduces a Lipschitz continuity-based framework to evaluate and improve AI explainability in power electronics, focusing on inference stability and training convergence for safer, more reliable AI applications.

Contribution

It proposes a novel Lipschitz-oriented approach for assessing AI explainability, including a learning rate strategy, validated through neural network analysis and power converter case studies.

Findings

Validated Lipschitz-based explainability framework on neural networks

Demonstrated improved training convergence with Lipschitz-aware learning rate

Confirmed inference stability enhances robustness in power electronics applications

Abstract

Lifecycle management of power converters continues to thrive with emerging artificial intelligence (AI) solutions, yet AI mathematical explainability remains unexplored in power electronics (PE) community. The lack of theoretical rigor challenges adoption in mission-critical applications. Therefore, this letter proposes a generic framework to evaluate mathematical explainability, highlighting inference stability and training convergence from a Lipschitz continuity perspective. Inference stability governs consistent outputs under input perturbations, essential for robust real-time control and fault diagnosis. Training convergence guarantees stable learning dynamics, facilitating accurate modeling in PE contexts. Additionally, a Lipschitz-aware learning rate selection strategy is introduced to accelerate convergence while mitigating overshoots and oscillations. The feasibility of the proposed Lipschitz-oriented framework is demonstrated by validating the mathematical explainability of a state-of-the-art physics-in-architecture neural network, and substantiated through empirical case studies on dual-active-bridge converters. This letter serves as a clarion call for the PE community to embrace mathematical explainability, heralding a transformative era of trustworthy and explainable AI solutions that potentially redefine the future of power electronics.