Pinned Repositories
first
gated-graph-neural-network-samples
Sample Code for Gated Graph Neural Networks
LLAE
machine-learning
Multi-Objective-Optimization-of-Distributed-Energy-Systems-under-Grid-Faults
Recently, riding through grid faults and supporting the grid voltage by using grid-connected converters (GCCs) have become major requirements reflected in the grid codes. This paper presents a novel reference current generation scheme with the ability to support the grid voltage by injecting a proper set of positive/negative active/reactive currents by using four controlling parameters. Analytical expressions are proposed to obtain the optimal values of these parameters under any grid voltage condition. The optimal performances can be obtained by achieving the following objectives: first, compliance with the phase voltage limits, second, maximized active and reactive power delivery, third, minimized fault currents, and fourth reduced oscillations on the active and reactive powers. These optimal behaviors bring significant advantages to emerging GCCs, such as increasing the efficiency, lowering the dc-link ripples, improving ac system stability, and avoiding equipment tripping. Simulation and experimental results verify the analytical results and the proposed expressions.
nn_playground
Experimental keras implementation of novel neural network structures
nerry95's Repositories
nerry95/first
nerry95/gated-graph-neural-network-samples
Sample Code for Gated Graph Neural Networks
nerry95/LLAE
nerry95/machine-learning
nerry95/Multi-Objective-Optimization-of-Distributed-Energy-Systems-under-Grid-Faults
Recently, riding through grid faults and supporting the grid voltage by using grid-connected converters (GCCs) have become major requirements reflected in the grid codes. This paper presents a novel reference current generation scheme with the ability to support the grid voltage by injecting a proper set of positive/negative active/reactive currents by using four controlling parameters. Analytical expressions are proposed to obtain the optimal values of these parameters under any grid voltage condition. The optimal performances can be obtained by achieving the following objectives: first, compliance with the phase voltage limits, second, maximized active and reactive power delivery, third, minimized fault currents, and fourth reduced oscillations on the active and reactive powers. These optimal behaviors bring significant advantages to emerging GCCs, such as increasing the efficiency, lowering the dc-link ripples, improving ac system stability, and avoiding equipment tripping. Simulation and experimental results verify the analytical results and the proposed expressions.
nerry95/nn_playground
Experimental keras implementation of novel neural network structures