Deadline Date: 31 May 2026
The surging integration of new energy power systems, driven by global decarbonization, introduces complex operational dynamics—high variability, multi-source data interdependencies, and escalating equipment complexity - posing critical challenges to traditional diagnostic paradigms. Artificial Intelligence (AI), particularly deep learning, image recognition, and hybrid algorithms, has emerged as a cornerstone for intelligent equipment health management, offering unprecedented capabilities in extracting latent patterns from heterogeneous data (sensors, images, vibration signals) and enabling proactive decision-making. This Special Issue solicits original contributions leveraging AI to address key technical gaps in new energy equipment health management, focusing on the following directions: (1) Real-time identification of incipient anomalies (e.g., insulation degradation in transformers, microcracks in PV modules) under variable loads and environmental conditions; (2) High-precision localization and classification of faults (e.g., gear box faults in wind turbines, CT saturation in secondary devices) via multi-modal AI models; (3) Data-driven degradation modeling for remaining useful life (RUL) estimation, integrating physics-informed learning and transfer learning; (4) Proactive risk assessment using AI-driven early warning systems to prevent cascading failures; (5) Quantitative assessment of equipment health indices (e.g., solar panel efficiency, circuit breaker contact wear) with explainable AI; (6) Scalable AI frameworks for interoperable health management across diverse equipment (e.g., coupling solar inverters with grid switches). We prioritize studies that bridge AI algorithm innovation with domain-specific engineering needs, emphasizing real-world validation and scalability. Contributions should advance both theoretical rigor (e.g., model generalizability, uncertainty quantification) and practical utility. By fostering interdisciplinary dialogue, this issue aims to accelerate the development of AI-empowered, resilient new energy power systems, paving the way for a sustainable energy future.
Editorial board
