# Long-Term Recurrent Convolutional Network-based Inertia Estimation using Ambient Measurements

arXiv: https://arxiv.org/abs/2112.00926 · HTML: https://mlsanity.com/paper/2112.00926/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2021.

## Built on by (most cited first, 8 of 8)

- [Reduced Optimal Power Flow Using Graph Neural Network](https://mlsanity.com/paper/2206.13591/) (2022)
- [Neural Network-based Power Flow Model](https://mlsanity.com/paper/2112.08418/) (2021)
- [Feasibility Layer Aided Machine Learning Approach for Day-Ahead Operations](https://mlsanity.com/paper/2208.06742/) (2022)
- [Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitment](https://mlsanity.com/paper/2306.01570/) (2023)
- [N-1 Reduced Optimal Power Flow Using Augmented Hierarchical Graph Neural Network](https://mlsanity.com/paper/2402.06226/) (2024)
- [HabitAction: A Video Dataset for Human Habitual Behavior Recognition](https://mlsanity.com/paper/2408.13463/) (2024)
- [Graph Neural Network-Accelerated Network-Reconfigured Optimal Power Flow](https://mlsanity.com/paper/2410.17460/) (2024)
- [Constraints and Variables Reduction for Optimal Power Flow Using Hierarchical Graph Neural Networks with Virtual Node-Splitting](https://mlsanity.com/paper/2411.06268/) (2024)

## Abstract (arXiv)

Conventional synchronous machines are gradually replaced by converter-based renewable resources. As a result, synchronous inertia, an important time-varying quantity, has substantially more impact on modern power systems stability. The increasing integration of renewable energy resources imports different dynamics into traditional power systems; therefore, the estimation of system inertia using mathematical model becomes more difficult. In this paper, we propose a novel learning-assisted inertia estimation model based on long-term recurrent convolutional network (LRCN) that uses system wide frequency and phase voltage measurements. The proposed approach uses a non-intrusive probing signal to perturb the system and collects ambient measurements with phasor measurement units (PMU) to train the proposed LRCN model. Case studies are conducted on the IEEE 24-bus system. Under a signal-to-noise ratio (SNR) of 60dB condition, the proposed LRCN based inertia estimation model achieves an accuracy of 97.56% with a mean squared error (MSE) of 0.0552. Furthermore, with a low SNR of 45dB, the proposed learning-assisted inertia estimation model is still able to achieve a high accuracy of 93.07%.