# Supporting Future Electrical Utilities: Using Deep Learning Methods in EMS and DMS Algorithms

arXiv: https://arxiv.org/abs/2303.00428 · HTML: https://mlsanity.com/paper/2303.00428/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 1 Mar 2023.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [R-Transformer: Recurrent Neural Network Enhanced Transformer](https://mlsanity.com/paper/1907.05572/) (2019)
- [Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks](https://mlsanity.com/paper/1812.09464/) (2018)
- [Online Multi-agent Reinforcement Learning for Decentralized Inverter-based Volt-VAR Control](https://mlsanity.com/paper/2006.12841/) (2020)
- [PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control](https://mlsanity.com/paper/2011.12354/) (2020)
- [State Estimation in Electric Power Systems Leveraging Graph Neural Networks](https://mlsanity.com/paper/2201.04056/) (2022)
- [A Reinforcement Learning Approach for Fast Frequency Control in Low-Inertia Power Systems](https://mlsanity.com/paper/2007.05474/) (2020)
- [Distributed Nonlinear State Estimation in Electric Power Systems using Graph Neural Networks](https://mlsanity.com/paper/2207.11465/) (2022)
- [Learning Latent Interactions for Event classification via Graph Neural Networks and PMU Data](https://mlsanity.com/paper/2010.01616/) (2020)
- [Estimating Demand Flexibility Using Siamese LSTM Neural Networks](https://mlsanity.com/paper/2109.01258/) (2021)
- [Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks](https://mlsanity.com/paper/2207.11117/) (2022)

## Built on by (most cited first, 1 of 1)

- [Application of Deep Learning Methods in Monitoring and Optimization of Electric Power Systems](https://mlsanity.com/paper/2309.00498/) (2023)

## Abstract (arXiv)

Electrical power systems are increasing in size, complexity, as well as dynamics due to the growing integration of renewable energy resources, which have sporadic power generation. This necessitates the development of near real-time power system algorithms, demanding lower computational complexity regarding the power system size. Considering the growing trend in the collection of historical measurement data and recent advances in the rapidly developing deep learning field, the main goal of this paper is to provide a review of recent deep learning-based power system monitoring and optimization algorithms. Electrical utilities can benefit from this review by re-implementing or enhancing the algorithms traditionally used in energy management systems (EMS) and distribution management systems (DMS).