Comprehensive flexible framework for using multi-machine learning methods to optimal dynamic transient stability prediction by considering prediction accuracy and time
Transient stability, a crucial aspect of power system research, is the subject of this paper. It determines the system's stability under severe disturbances. In recent years, Machine/Deep Learning (ML/DL) techniques have been widely applied to predict transient stability conditions. This paper...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025008059 |
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| Summary: | Transient stability, a crucial aspect of power system research, is the subject of this paper. It determines the system's stability under severe disturbances. In recent years, Machine/Deep Learning (ML/DL) techniques have been widely applied to predict transient stability conditions. This paper presents a flexible framework for using the desired number of ML algorithms and combines the results of them to extract the final optimal transient stability perdition (TSP). This prediction includes stability status (stable/unstable) and remaining time until instability is labeled with related accuracy. To show the effectiveness of the proposed framework, for instance, four different ML approaches are used: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and K-nearest neighbor (KNN). The introduced framework combines the output of ML methods in two stages considering the time-dependent perdition accuracy; the first stage predicts stability status by using a hard voting system, and the second one estimates the remaining time until instability with a soft voting system. The final optimal outputs of the proposed approach are dynamic time-dependent curves for the prediction of stability status and time. The prediction accuracy changes by data size and can reach to 100 % and the remaining time until instability can predict with 0.03 s error, averagely. Supplementary studies examine how noise, missing data, and important inputs affect the projections. The stability dataset is collected from the DIgSILENT Power Factory and tested on the IEEE 39-bus system. Also, the proposed framework is coded with PYTHON software. |
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| ISSN: | 2590-1230 |