| Title | Deep Learning-Based False Sensor Data Detection for Battery Energy Storage Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Lee, Hyunjun, Bere, Gomanth, Kim, Kyungtak, Ochoa, Justin J., Park, Joung-hu, Kim, Taesic |
| Conference Name | 2020 IEEE CyberPELS (CyberPELS) |
| Date Published | oct |
| Keywords | Batteries, Battery energy storage systems, Circuit faults, Computational modeling, convolution, convolutional neural network, Data models, Deep Learning, false data inject attack, false trust, Mathematical model, policy-based governance, pubcrawl, Resiliency, Scalability, sensor data fault, state of charge |
| Abstract | Battery energy storage systems are facing risks of unreliable battery sensor data which might be caused by sensor faults in an embedded battery management system, communication failures, and even cyber-attacks. It is crucial to evaluate the trustworthiness of battery sensor data since inaccurate sensor data could lead to not only serious damages to battery energy storage systems, but also threaten the overall reliability of their applications (e.g., electric vehicles or power grids). This paper introduces a battery sensor data trust framework enabling detecting unreliable data using a deep learning algorithm. The proposed sensor data trust mechanism could potentially improve safety and reliability of the battery energy storage systems. The proposed deep learning-based battery sensor fault detection algorithm is validated by simulation studies using a convolutional neural network. |
| DOI | 10.1109/CyberPELS49534.2020.9311542 |
| Citation Key | lee_deep_2020 |
Deep Learning-Based False Sensor Data Detection for Battery Energy Storage Systems