# Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids

arXiv: https://arxiv.org/abs/2303.08300 · HTML: https://mlsanity.com/paper/2303.08300/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 62 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [Concrete Autoencoders for Differentiable Feature Selection and Reconstruction](https://mlsanity.com/paper/1901.09346/) (2019)
- [Learning to Maximize Mutual Information for Dynamic Feature Selection](https://mlsanity.com/paper/2301.00557/) (2023)
- [A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications](https://mlsanity.com/paper/2111.01968/) (2021)
- [Federated Feature Selection for Cyber-Physical Systems of Systems](https://mlsanity.com/paper/2109.11323/) (2021)
- [A Stream Learning Approach for Real-Time Identification of False Data Injection Attacks in Cyber-Physical Power Systems](https://mlsanity.com/paper/2210.06729/) (2022)

## Abstract (arXiv)

The performance of fault diagnosis systems is highly affected by data quality in cyber-physical power systems. These systems generate massive amounts of data that overburden the system with excessive computational costs. Another issue is the presence of noise in recorded measurements, which prevents building a precise decision model. Furthermore, the diagnostic model is often provided with a mixture of redundant measurements that may deviate it from learning normal and fault distributions. This paper presents the effect of feature engineering on mitigating the aforementioned challenges in cyber-physical systems. Feature selection and dimensionality reduction methods are combined with decision models to simulate data-driven fault diagnosis in a 118-bus power system. A comparative study is enabled accordingly to compare several advanced techniques in both domains. Dimensionality reduction and feature selection methods are compared both jointly and separately. Finally, experiments are concluded, and a setting is suggested that enhances data quality for fault diagnosis.