The unpredictable and highly variable nature of wind power generation demands advanced predictive models to enhance the operational stability of power systems under non-stationary wind conditions. This study presents ...
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With the widespread integration of renewable energy sources (REs), the number of power system scenarios to be analyzed has increased significantly, and the system structure has become increasingly complex, which incre...
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To tackle the issue of Sub-Synchronous control Interaction (SSCI) in doubly fed wind turbines featuring series capacitor compensation, a novel approach is undertaken by creating an impedance model for the Doubly Fed I...
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The modern power system exhibits a double high characteristic, where the physical structure and control strategies of power electronic devices have altered the stability characteristics of the modern power system. Thi...
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This paper introduces a novel algorithm, LSTM-DDQN, designed to address the stochastic economic dispatch problem in microgrids. The inherent variability and randomness of photovoltaic (PV) energy pose challenges for a...
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In large-scale renewable energy base and HVDC systems, the absence of conventional power support makes commutation failures a significant threat to system security and stability. An optimal dispatch method based on Ko...
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In blackout scenarios of new power systems, wind farms (WF) and photovoltaic power stations (PVS) configured with scaled energy storage system (ESS) can be used as sources to support the black-start of thermal power u...
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In the field of wind power generation, the significant geographical separation between wind farms and load centers often necessitated the long-distance and large-scale transmission of wind energy, which was typically ...
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Stability analysis of grid-connected inverters is advanced through impedance analysis in this study, which leads to the development of a comprehensive mathematical model and simulation. control parameters, notably the...
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This study focuses on the scheduling of a microgrid integrated with electric vehicles, employing a reinforcement learning algorithm to devise an optimal economic operation strategy. The approach addresses the challeng...
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