# Discovery of False Data Injection Schemes on Frequency Controllers with Reinforcement Learning

arXiv: https://arxiv.org/abs/2408.16958 · HTML: https://mlsanity.com/paper/2408.16958/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 4 of 4)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Reinforcement Learning for Optimal Primary Frequency Control: A Lyapunov Approach](https://mlsanity.com/paper/2009.05654/) (2020)

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

- [Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction Method](https://mlsanity.com/paper/2409.04609/) (2024)
- [Continual Adversarial Reinforcement Learning (CARL) of False Data Injection detection: forgetting and explainability](https://mlsanity.com/paper/2411.10367/) (2024)

## Abstract (arXiv)

While inverter-based distributed energy resources (DERs) play a crucial role in integrating renewable energy into the power system, they concurrently diminish the grid's system inertia, elevating the risk of frequency instabilities. Furthermore, smart inverters, interfaced via communication networks, pose a potential vulnerability to cyber threats if not diligently managed. To proactively fortify the power grid against sophisticated cyber attacks, we propose to employ reinforcement learning (RL) to identify potential threats and system vulnerabilities. This study concentrates on analyzing adversarial strategies for false data injection, specifically targeting smart inverters involved in primary frequency control. Our findings demonstrate that an RL agent can adeptly discern optimal false data injection methods to manipulate inverter settings, potentially causing catastrophic consequences.