State estimation of a power network
AssigneeSiemens Aktiengesellschaft
InventorsSascha Batz, Mathias Duckheim
Life of this patent
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Abstract
Various embodiments include a computer-aided method for state estimation of a power network using an artificial neural network with one or more inputs technically associated with the power network. The method may include calculating a state estimation using captured temporal measurement signals of the power network as inputs for the neural network. At least an amplitude of a harmonic of one of the measurement signals is used as an input for the neural network.
Claims12
A method for state estimation of a power network using an artificial neural network with one or more inputs technically associated with the power network, the method comprising:
- calculating a state estimation using captured temporal measurement signals of the power network calculated at nodes of the power network as inputs for the neural network, the state estimation includes real-time voltages at the nodes and real-time branch currents between the nodes;
- wherein at least an amplitude of a harmonic of one of the measurement signals is used as an input for the neural network;
- wherein the neural network has been trained using a training dataset comprising a plurality of amplitudes of harmonics of one or more measurement signals or comprising a plurality of rise times and/or fall times of one or more measurement signals.
A method for training an artificial neural network for the state estimation of a power network with one or more inputs technically associated with the power network, wherein the state estimation is calculated using captured temporal measurement signals of the power network calculated at nodes of the power network, the state estimation includes real-time voltages at the nodes and real-time branch currents between the nodes, the method comprising:
- training the network using a training dataset with a plurality of amplitudes of harmonics, a plurality of rise times, and/or a plurality of fall times of one or more measurement signals;
- and
- using the amplitudes of the harmonics, the rise times, and/or the fall times as input to train the neural network.
Dependencies are inferred from the claim text.