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Assessing the performance of deep learning models for multivariate probabilistic energy forecasting
(Elsevier, 2021-01-16)
Deep learning models have the potential to advance the short-term decision-making of electricity market participants and system operators by capturing the complex dependences and uncertainties of power system operation. ...
Probability density function forecasting of residential electric vehicles charging profile
(Elsevier, 2022-07-13)
Residential electric vehicle (REV) is an advanced technology with a rapid growth rate in transportation and electric grids. One key challenge in the operation of REVs is the necessity of the accurate, reliable, and practical ...
Fault Location and Faulty Line Selection in Transmission Networks: Application of Improved Gated Recurrent Unit
(IEEE, 2022-06-02)
Wide-area measurement systems (WAMS) are efficient tools to estimate the fault location in the large-scale power systems. In this study, a fast and accurate deep learning based structure is proposed to detect the faulty ...
Fast GRNN-Based Method for Distinguishing Inrush Currents in Power Transformers
(IEEE, 2021-09-08)
Differential protection, as the key protection element in the power transformers, has always been threatened with sending false trips subjected to external transient disturbances. As a result, differential protection needs ...
Multibody Dynamics and Control using Machine Learning
(Springer Nature, 2023-02-27)
Artificial intelligence and mechanical engineering are two mature fields of science that intersect more and more often. Computer-aided mechanical analysis tools, often multibody system software, are very versatile and have ...
Wide-Area Composite Load Parameter Identification Based on Multi-Residual Deep Neural Network
(IEEE, 2021-12-22)
Accurate and practical load modeling plays a critical role in the power system studies including stability, control, and protection. Recently, wide-area measurement systems (WAMSs) are utilized to model the static and ...