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3 results about "Susceptance" patented technology

In electrical engineering, susceptance (B) is the imaginary part of admittance, where the real part is conductance. The inverse of admittance is impedance, where the imaginary part is reactance and the real part is resistance. In SI units, susceptance is measured in siemens. Oliver Heaviside first defined this property in June 1887.

Variable admittance-based fault current limiting system and method for grid-forming converter

Disclosed in the present invention are a variable admittance-based fault current limiting system and method for a grid-forming converter. The system comprises: a power calculation module, an excitation link module, a virtual rotor module, a coordinate transformation module, a dual-loop control module, and a space vector pulse width modulation module. The power calculation module is used for calculating active power and reactive power of a current power grid; the excitation link module is used for performing steady-state stepless variable susceptance control on the basis of the calculated reactive power, so as to adjust the electromotive force of the current power grid; the virtual rotor module is used for performing transient stepped variable conductance control on the basis of the calculated active power, so as to adjust the phase angle of the current power grid; the coordinate transformation module is used for converting the adjusted electromotive force and phase angle into electromotive force components in a direct current coordinate system; the dual-loop control module is used for generating and calculating a current instruction on the basis of the converted electromotive force components; and the space vector pulse width modulation module is used for generating a modulation signal to guide operation of a switching device.
Owner:CRRC ZHUZHOU ELECTRIC LOCOMOTIVE RESEARCH INSTITUTE CO LTD

Power distribution network parameter abnormality positioning and correction method based on residual sensitivity matrix

ActiveCN122043147BAlgorithmTransformer
The application discloses a power distribution network parameter abnormality positioning and correction method based on a residual sensitivity matrix, and the method comprises the following steps: determining the types of each branch of the power distribution network to obtain to-be-checked parameters of each branch; obtaining actual values of active power and reactive power of each branch in the power distribution network; obtaining conductance and susceptance of each branch, and calculating theoretical values of the active power and the reactive power of each branch according to the conductance, the susceptance and the to-be-checked parameter data of each branch, and calculating active power residuals and reactive power residuals of each branch; constructing a sensitivity matrix and a measurement residual vector, and establishing a normal equation through the sensitivity matrix and the measurement residual vector, and solving the normal equation to obtain line length deviation and transformer ratio deviation; and correcting the line length and the transformer ratio. The application realizes high-precision, anti-interference and stable identification of the line length and the transformer ratio.
Owner:ELECTRIC POWER RES INST OF STATE GRID ZHEJIANG ELECTRIC POWER COMAPNY

Deep learning-based approach for solving optimal power flow problems with flexible topology

A method for solving multiple OPF equations of an AC electrical power system with plural topologies and admittances is provided. The method includes determining a continuous admittance space to embed the plural topologies, determining a plurality of voltage magnitudes and a plurality of voltage phase angles of the plurality of buses by a deep neural network, and reconstructing an active power generation and a reactive power generation using power flow equations according to the plurality of voltage magnitudes, the plurality of voltage phase angles, and the load inputs. The continuous admittance space is an admittance matrix of a plurality of line admittances each having a default admittance computed using a conductance and a susceptance of the plurality of branches. The deep neural network receives the load inputs and the plurality of line admittances as inputs.
Owner:CITY UNIVERSITY OF HONG KONG