What is it about?
To fully learn the latent temporal dependencies from post-disturbance system dynamic trajectories, deep learning is utilized for short-term voltage stability (STVS) assessment of power systems in this paper. First of all, a semi-supervised cluster algorithm is performed to obtain class labels of STVS instances due to the unavailability of reliable quantitative criteria. Secondly, a long short-term memory (LSTM) based assessment model is built through learning the time dependencies from the post-disturbance system dynamics. Finally, the trained assessment model is employed to determine the systems stability status in real time. The test results on the IEEE 39-bus system suggest that the proposed approach manages to assess the stability status of the system accurately and timely. Furthermore, the superiority of the proposed method over traditional shallow learning-based assessment methods has also been proved.
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Why is it important?
(1) Unlike the existing state-of-the-art assessment approaches, the LSTM-based STVS approach in this paper learns from the temporal data dependencies, which improves assessment accuracy and significantly reduces the length of OTW. (2) Besides accuracy, considering AUC and F1-score, the simulation results on IEEE 39-bus system comprehensively validate the effectiveness of the proposed approach. (3) Furthermore, the superiority of the proposed deep learning-based assessment approach over traditional shallow learning-based approaches has also been verified.
Perspectives
(1) This paper attempts to introduce deep learning for fully capturing the potential temporal dependencies from the post-disturbance power system dynamics. The LSTM network with deep architecture can extract sequential STVS features from PMU data, which is novel in the STVS assessment field. (2) The simulation results on the IEEE 39-bus system demonstrate that the proposed LSTM-based STVS assessment approach manages to make a more accurate assessment with a faster response speed, comparing with the traditional models based on shallow machine learning such as support vector machine (SVM) and DT. (3) Besides assessment accuracy, this study carries out statistical tests to comprehensively evaluate the performance of the proposed approach by adopting indicators such as receiver operating characteristic (ROC) curve, area under the curve (AUC), and F1-score.
Professor/PhD Supervisor/SMIEEE Yang Li
Northeast Electric Power University
Read the Original
This page is a summary of: Deep Learning for Short-Term Voltage Stability Assessment of Power Systems, IEEE Access, January 2021, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/access.2021.3057659.
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