Experimental results on two different power systems demonstrate improved accuracy, as well as significant reduction in both training and testing time.
📌 3 Key Takeaways
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Objective & Hypothesis:Dynamic security assessment of a large power system operating over a wide range of conditions requires an intensive computation for evaluating the system's transient stability against a large number of contingencies.
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Key Findings & Data:In this study, we investigate the application of multilabel learning for improving training and prediction time, along with the prediction accuracy, of neural networks for online transient stability assessment of power systems.
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Conclusion & Impact:Experimental results on two different power systems demonstrate improved accuracy, as well as significant reduction in both training and testing time.
Dynamic security assessment of a large power system operating over a wide range of conditions requires an intensive computation for evaluating the system's transient stability against a large number of contingencies. In this study, we investigate the application of multilabel learning for improving training and prediction time, along with the prediction accuracy, of neural networks for online transient stability assessment of power systems. We introduce a new multilabel learning method, which uses a contingency clustering step to learn similar contingencies together in the same multilabel multilayer perceptron. Experimental results on two different power systems demonstrate improved accuracy, as well as significant reduction in both training and testing time.
BEYRANVAND, P., GENÇ, V. M. İ., ÇATALTEPE, Z. (2018). Multilabel learning for the online transient stability assessment of electric power systems. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 26 - Sayı: 5, 2661–2675. https://ajindex.com/articles/187433