What question did this study set out to answer?

The aim is to develop a predictive fault diagnosis framework for dynamic wireless power transfer systems that integrates with distribution networks.

February 28, 2026Smart Cities1 citationsOpen Access

From Hours to Milliseconds: Dual-Horizon Fault Prediction for Dynamic Wireless EV Charging via Digital Twin Integrated Deep Learning

MMMohammed Ahmed MousaYanbu University College ASAli SaygheYanbu University College SBSalem BatiyahYanbu University College

Key Points

The aim is to develop a predictive fault diagnosis framework for dynamic wireless power transfer systems that integrates with distribution networks.
Utilized deep neural networks for fault diagnosis of dynamic wireless power transfer systems.
Employed multi-station sensor fusion using 36 sensor features across three charging stations.
Integrated the framework with the IEEE 13-bus distribution network for enhanced monitoring.
Implemented dual-horizon prediction for long-term forecasting and real-time detection.
Achieved classification accuracy of 97.85% across eight fault types.
Demonstrated real-time detection capability under 50 ms for critical protection.
Outperformed other models like SVM, Random Forest, and LSTM with statistical significance (p<0.001).
Validated across multiple scenarios with detection accuracies exceeding 95%.

Abstract

Dynamic Wireless Power Transfer (DWPT) is emerging as critical smart city infrastructure for sustainable urban mobility, enabling electric vehicle charging while driving. However, DWPT introduces complex fault scenarios requiring intelligent monitoring. Existing fault diagnosis approaches for wireless power transfer systems face three key complexities: (1) they are limited to static charging with only 2–4 fault categories, failing to address the time-varying coupling dynamics and segmented coil handover transients inherent in dynamic charging; (2) they lack integration with the host distribution grid, ignoring grid-side disturbances that propagate to charging stations; and (3) they offer only reactive detection without predictive capability for incipient fault management. This paper presents a deep neural network (DNN)-based fault diagnosis framework utilizing multi-station sensor fusion for DWPT systems integrated with the IEEE 13-bus distribution network to address these limitations. The system monitors 36 sensor features across three charging stations, employing feature-level concatenation with station-specific normalization for multi-station fusion, achieving 97.85% classification accuracy across eight fault types. Unlike static charging, the framework explicitly models time-varying coupling dynamics due to vehicle motion, including segmented coil handover effects. A digital twin provides dual-horizon prediction: long-term forecasting (24–72 h) for incipient faults and real-time detection under 50 ms for critical protection, with fault probability outputs and ranked fault lists enabling actionable maintenance decisions. The DNN outperforms SVM (92.45%), Random Forest (94.82%), and LSTM (96.54%) with statistical significance (p<0.001), while maintaining model inference latency of 4.2 ms, suitable for edge deployment. Circuit-based analysis provides analytical justification for fault signatures, and practical parameter acquisition methods enable real-world implementation. Five case studies validate robustness across highway, urban, and grid disturbance scenarios with detection accuracies exceeding 95%.

Demander à l'IA

Bookmark

View Full Paper