TRACE: Time series representation learning with contrastive embeddings for anomaly detection in photovoltaic systems

Reliable anomaly detection in photovoltaic (PV) inverters is critical for ensuring operational efficiency and reducing maintenance costs in renewable energy systems. We introduce TRACE ( T ime series R epresentation learning with A utoencoder-based C ontrastive E mbeddings), a self-supervised contrastive learning framework for multivariate time series anomaly detection in PV systems. TRACE employs a two-stage architecture: autoencoder-based representation learning with interchangeable backbones followed by contrastive training through a Siamese network. The framework generates semantically coherent augmentations by perturbing autoencoder reconstructions and applies three negative mining strategies to create challenging contrastive pairs. Comprehensive experiments on a real-world PV inverter dataset and two industrial benchmarks demonstrate TRACE’s superiority. Autoencoder-based augmentations deliver a 21.3% relative improvement in mean F1 (0.616 vs. 0.508) over traditional perturbation methods, with TransformerAE emerging as the optimal backbone architecture. While negative sampling strategies show dataset-specific advantages, their impact remains secondary to encoder capacity. TRACE with TransformerAE and reconstruction-error negatives consistently outperforms fourteen state-of-the-art time series anomaly detection methods, achieving highest F1 scores on all the three datasets while maintaining exceptional precision up to 0.99. Visualization analysis confirms TRACE’s capacity for early fault detection up to three days before failure and interpretable embedding separation. The framework addresses the fundamental challenge of label scarcity in industrial monitoring through self-supervised learning, providing a practical and transparent solution for predictive maintenance in PV systems and broader industrial applications. • Novel contrastive learning framework for PV inverter anomaly detection without labels. • Autoencoder-driven augmentations maintain sequence dynamics for reliable anomaly detection. • Transformer-based encoder excels across PV and industry benchmark datasets. • Contrastive learning improves F1 by 21.3% over traditional augmentation techniques. • High precision and early warnings support cost-effective predictive maintenance.