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

TL;DR

This paper introduces a novel LRCN-based model for estimating power system inertia using ambient measurements, achieving high accuracy even under noisy conditions, which enhances stability analysis in renewable-integrated power systems.

Contribution

The paper presents a new learning-assisted inertia estimation method using LRCN and ambient measurements, addressing challenges posed by renewable energy integration.

Findings

Achieves 97.56% accuracy at 60dB SNR

Maintains 93.07% accuracy at 45dB SNR

Effective in power systems with renewable resources

Abstract

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%.