High Performance Platform to Detect Faults in the Smart Grid by Artificial Intelligence Inference
Author:
Affiliation:
1. Electronic Technology Department, UPV/EHU, Bilbao, Spain
2. School of Electrical Engineering, Shandong University, Jinan, China
Funder
European Commission
Eusko Jaurlaritza
Hezkuntza, Hizkuntza Politika Eta Kultura Saila, Eusko Jaurlaritza
Ministerio de Ciencia e Innovaci?n
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Computer Science
Link
http://xplorestaging.ieee.org/ielx7/5165411/10372481/10109818.pdf?arnumber=10109818
Reference24 articles.
1. EDLA-EFDS: A Novel Ensemble Deep Learning Approach For Electrical Fault Detection Systems
2. On the Use of Artificial Intelligence for High Impedance Fault Detection and Electrical Safety
3. Implementation of a DPU-Based Intelligent Thermal Imaging Hardware Accelerator on FPGA
4. An Efficient Task Assignment Framework to Accelerate DPU-Based Convolutional Neural Network Inference on FPGAs
5. Real-Time FPGA-Based Hardware Neural Network for Fault Detection and Isolation in More Electric Aircraft
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1. Efficient HW/SW Co-design of FPGA Accelerator to Detect Anomaly Attacks in Smart Grids;2024 IEEE 33rd International Symposium on Industrial Electronics (ISIE);2024-06-18
2. Implementing a CNN in FPGA Programmable Logic for NILM Application;2023 38th Conference on Design of Circuits and Integrated Systems (DCIS);2023-11-15
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