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

Mingjian Tuo; Xingpeng Li

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arXiv · 2112.00926

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

Mingjian Tuo·Xingpeng Li

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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%.

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BibTeX RIS

Mingjian Tuo, Xingpeng Li. 2021-12-02. Long-Term Recurrent Convolutional Network-based Inertia Estimation using Ambient Measurements. https://arxiv.org/abs/2112.00926

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