2026· MATEC Web of Conferences· Vol 424, pp. 01011· 0 citations· 13 references
TL;DR
The research emphasizes the use of “data-knowledge hybrid driven” logic to break through the limitations of “black box” and improve interpretability, and future directions include the integration of cross-energy collaborative early warning systems with XAI.
Abstract
The global energy transition has given rise to a “double high” power system with low inertia and violent fluctuations, resulting in a deep coupling of physical, cyber and operational risks. Traditional systems have significant limitations in real-time maintenance. The risk analysis framework constructed in this paper uses CDCGAN for sample enhancement and SDAE for high-order feature extraction. The cloud edge scheduling mechanism based on KKT reduces the pressure on cloud master nodes by 35.19%, and the latency is compressed to 16-18 milliseconds. The system performance was verified by Texas GMD, EU Safegrid system and Chinese digital twin architecture. The research emphasizes the use of “data-knowledge hybrid driven” logic to break through the limitations of “black box” and improve interpretability, and future directions include the integration of cross-energy collaborative early warning systems with XAI.
Climate change, increasing renewable-energy penetration, and the growing dependence of power networks on digital and smart assets are changing the conditions under which power system security must be assessed and maintained. This paper presents a critical review of deterministic, probabilistic, risk-based, and intellig...
Mohammed Abdullah Alsulami, Dilan Jayaweera· Journal of Renewable and Sus...· 0 citations
A literature review of Machine Learning applications in the domain of Electricity Theft Detection (ETD) that is grounded in 71 scholarly studies produced between 2021 and 2026 and selected according to criteria of machine learning application in the process of theft/NTL detection and accompanying empirical validation.
Oladipupo Olatunji, Victor Edibo, Olumide Alamu· Journal of Science Research...· 0 citations
The introduction of IoT devices, high-frequency microprocessor sensors, and adaptive signal processing algorithms opens up new opportunities for meaningful interpretation of dynamic processes in pipelines. In particular, one of the most common but difficult-to-diagnose problems remains air voids and traffic jams that o...
A. N. Ushkov, A. Trofimov, A. A. Shestakova et al.· 2026 International Russian A...· 0 citations
This paper presents a multi-source data-fusion framework for fault diagnosis and early warning of relay protection secondary circuits in smart substations. The proposed method integrates seven data sources—SV statistics, GOOSE statistics, IED self-check records, MMS alarms, physical-link measurements, time-synchronizat...
Ke-Fei Xiao, Zhao-Xu Huang· Journal of Physics, Conferen...· 0 citations
The increasing complexity of modern power distribution networks and the integration of renewable energy sources require intelligent and reliable fault management systems. This paper presents , a smart grid framework that integrates Artificial Intelligence (AI) and edge computing for real-time fault detection, predictio...
Mrs. Anusuya Patil, Baliger Shashidhara, Harisha K, Nithin Kumar G. M. Avinash Naik· International Journal of Adv...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
