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Differentiable neural search architecture with zero-cost metrics for insulator fault prediction
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Resumo(s)
Reliable monitoring of high-voltage insulators is critical for maintaining the stability of electrical power systems, particularly under environmental contamination that can lead to flashover. Traditional inspection techniques struggle to anticipate degradation dynamics, while data-driven models often rely on fixed neural architectures that inadequately capture the complex temporal patterns in leakage current signals. This work proposes a Differentiable Neural Architecture Search (DARTS) framework, based on zero-cost metrics, tailored for time series forecasting in insulator monitoring. The method based on DARTS integrates a mixed encoder-decoder design with learnable selection over long short-term memory, gated recurrent units, and transformer components, coupled with a cross-attention bridge featuring temporal bias and gating mechanisms. To ensure efficient architecture exploration, the search leverages metrics such as SynFlow and Jacobian covariance for early candidate screening, followed by a bilevel optimization stage with entropy and diversity regularization. Experiments on real-world leakage current data demonstrate that the discovered architectures outperform manually designed baselines, offering improved forecasting performance.
Descrição
The APC was supported by the project Self-adaptive platform based on intelligent agents for the optimization and management of operational processes in logistic warehouses (PLAUTON), PID2023-151701OB-C21, funded by MCIN/AEI/10.13039/501100011033/FEDER, EU. Buratto would like to thank UDESC for the financial support for his interuniversity exchange doctorate at the University of Salamanca. Also, the authors would like to thank the Coordination for the Improvement of Higher Education Personnel (CAPES - Brazil)
for the scholarship to Buratto. This study was financed (i) in part by CAPES under the doctoral scholarship number 88887.808258/2023-00, and (ii) by Council for Scientific and Technological Development (CNPq) under grant numbers 305910/2024-8 and 307858/2025-1. A realização desta investigação foi parcialmente financiada por fundos nacionais através da FCT - Fundação para a Ciência e Tecnologia, I.P. no âmbito dos projetos UIDB/04466/2025 e UIDP/04466/2025.
Publisher Copyright:
© 2026 The Author(s).
Palavras-chave
Differentiable neural architecture Forecasting Neural network architectures Predictive maintenance General Engineering