# Unsupervised detection and open-set classification of fast-ramped flexibility activation events

arXiv: https://arxiv.org/abs/2111.02174 · HTML: https://mlsanity.com/paper/2111.02174/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 58 references that are in the map.
- First posted on arXiv 3 Nov 2021.

## Builds on (most cited first, 13 of 13)

- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Recent Advances in Open Set Recognition: A Survey](https://mlsanity.com/paper/1811.08581/) (2018)
- [Towards Open World Recognition](https://mlsanity.com/paper/1412.5687/) (2014)
- [Time-Series Anomaly Detection Service at Microsoft](https://mlsanity.com/paper/1906.03821/) (2019)
- [The Extreme Value Machine](https://mlsanity.com/paper/1506.06112/) (2015)
- [Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks](https://mlsanity.com/paper/1710.09207/) (2017)
- [Approaches and Applications of Early Classification of Time Series: A Review](https://mlsanity.com/paper/2005.02595/) (2020)
- [A Survey on Open Set Recognition](https://mlsanity.com/paper/2109.00893/) (2021)
- [Open-Category Classification by Adversarial Sample Generation](https://mlsanity.com/paper/1705.08722/) (2017)
- [All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey](https://mlsanity.com/paper/1808.05283/) (2018)
- [Collective decision for open set recognition](https://mlsanity.com/paper/1806.11258/) (2018)
- [Energy Disaggregation for Real-Time Building Flexibility Detection](https://mlsanity.com/paper/1605.01939/) (2016)
- [Disruptive Event Classification using PMU Data in Distribution Networks](https://mlsanity.com/paper/1703.09800/) (2017)

## Built on by (most cited first, 2 of 2)

- [An Unsupervised Adversarial Autoencoder for Cyber Attack Detection in Power Distribution Grids](https://mlsanity.com/paper/2404.02923/) (2024)
- [Analysis and Applications of Deep Learning with Finite Samples in Full Life-Cycle Intelligence of Nuclear Power Generation](https://mlsanity.com/paper/2311.04247/) (2023)

## Abstract (arXiv)

The continuous electrification of the mobility and heating sectors adds much-needed flexibility to the power system. However, flexibility utilization also introduces new challenges to distribution system operators (DSOs), who need mechanisms to supervise flexibility activations and monitor their effect on distribution network operation. Flexibility activations can be broadly categorized to those originating from electricity markets and those initiated by the DSO to avoid constraint violations. Simultaneous electricity market driven flexibility activations may cause voltage quality or temporary overloading issues, and the failure of flexibility activations initiated by the DSO might leave critical grid states unresolved. This work proposes a novel data processing pipeline for automated real-time identification of fast-ramped flexibility activation events. Its practical value is twofold: i) potentially critical flexibility activations originating from electricity markets can be detected by the DSO at an early stage, and ii) successful activation of DSO-requested flexibility can be verified by the operator. In both cases the increased awareness would allow the DSO to take counteractions to avoid potentially critical grid situations. The proposed pipeline combines techniques from unsupervised detection and open-set classification. For both building blocks feasibility is systematically evaluated and proofed on real load and flexibility activation data.