A Two-Stage transient stability prediction method using convolutional residual memory network and gated recurrent unit

Zhan, Xianwen; Han, Song; Na, Rong; Liu, Peili; Ao, Weizhi

doi:10.1016/j.ijepes.2022.107973

Cited by 13 publications

(2 citation statements)

References 33 publications

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“…Each DL method mentioned above has corresponding advantages. Therefore, based on these characteristics, some scholars have developed multi-model combinations, such as CNN-LSTM [ 32 ], CNN-GRU [ 33 ], and GNN-LSTM [ 34 ] models, to further mine data from various aspects of information for prediction purposes. It is noteworthy that none of the former studies focused on transformer models in the research field of power system stability.…”

Section: Related Workmentioning

confidence: 99%

Frequency Stability Prediction of Power Systems Using Vision Transformer and Copula Entropy

Liu

Han

et al. 2022

Entropy

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This paper addresses the problem of frequency stability prediction (FSP) following active power disturbances in power systems by proposing a vision transformer (ViT) method that predicts frequency stability in real time. The core idea of the FSP approach employing the ViT is to use the time-series data of power system operations as ViT inputs to perform FSP accurately and quickly so that operators can decide frequency control actions, minimizing the losses caused by incidents. Additionally, due to the high-dimensional and redundant input data of the power system and the O(N2) computational complexity of the transformer, feature selection based on copula entropy (CE) is used to construct image-like data with fixed dimensions from power system operation data and remove redundant information. Moreover, no previous FSP study has taken safety margins into consideration, which may threaten the secure operation of power systems. Therefore, a frequency security index (FSI) is used to form the sample labels, which are categorized as “insecurity”, “relative security”, and “absolute security”. Finally, various case studies are carried out on a modified New England 39-bus system and a modified ACTIVSg500 system for projected 0% to 40% nonsynchronous system penetration levels. The simulation results demonstrate that the proposed method achieves state-of-the-art (SOTA) performance on normal, noisy, and incomplete datasets in comparison with eight machine-learning methods.

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Section: Related Workmentioning

confidence: 99%

Frequency Stability Prediction of Power Systems Using Vision Transformer and Copula Entropy

Liu

Han

et al. 2022

Entropy

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“…Yu et al (2018) developed a TSA system based on the long short-term memory network (LSTM) to capture the long-term dependencies along the time steps of time series. Furthermore, an LSTM-based gated recurrent unit is added to a two-stage TSA method for the analysis of uncertain samples after the first stage (Zhan et al, 2022). Convolutional neural network (CNN) is also widely used in many outstanding TSA models (Gupta et al, 2019;Shi et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A multi-task transient stability assessment method adapted to topology changes using multi-graph sample and aggregate-attention network

Huang,

Dong,

Zhao

et al. 2024

Front. Energy Res.

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Transient stability assessment (TSA) plays a pivotal role in guiding power grid risk control strategies. However, it faces challenges when dealing with complex multi-graph inputs generated by pre-fault, fault occurrence, and post-fault states. Meanwhile, most previous research studies neglected the assessment of the transient stability level. To address this, we propose a multi-task transient stability assessment (MTTSA) approach. In MTTSA, we introduce a multi-graph sample and aggregate-attention network (GraphSAGE-A) designed to capture stability features even amidst topology changes. A multi-head attention mechanism and local normalization method are adopted for a better extraction of the global and contextual information. Additionally, we introduce a quantified transient stability risk index considering the transient stability boundary and incorporate a multi-task dense structure to enhance MTTSA’s performance. Empirical tests, under changing operating conditions, conducted on the IEEE 39-bus system showcase a significant performance improvement with the proposed MTTSA method.

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On Closed-Loop Current Prediction of Medium Voltage Feeders Based on CNN-GRU

Zheng¹,

Bai²,

Liu³

et al. 2023

Lecture Notes in Electrical Engineering

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A Two-Stage transient stability prediction method using convolutional residual memory network and gated recurrent unit

Cited by 13 publications

References 33 publications

Frequency Stability Prediction of Power Systems Using Vision Transformer and Copula Entropy

Frequency Stability Prediction of Power Systems Using Vision Transformer and Copula Entropy

A multi-task transient stability assessment method adapted to topology changes using multi-graph sample and aggregate-attention network

On Closed-Loop Current Prediction of Medium Voltage Feeders Based on CNN-GRU

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