The present invention related to the field of power system protection and control, presents a voltage travelling wave differential protection method considering the influence of frequency-dependent parameters, which provides more accurate and rapid fault identification. The technical scheme of the present invention is as follows: a calculation method of voltage travelling-wave differential protection for VSC-HVDC transmission lines, taking the influence of the frequency-dependent parameters into consideration, the steps are as follows: calculating a characteristic impedance and propagation coefficient of the frequency-dependent transmission line in time domain, collecting voltage and current signals at the both ends of the transmission line and then calculating the forward and backward voltage travelling wave, respectively, comparing a differential value of voltage travelling wave with a preset threshold value to determine whether the internal fault occurs. The present invention is mainly applied in the field of power system protection and control.

A method for determining an electrical resistance of an electrical supply lead of an electrical system. The system is connected to a first and a second electrically conductive supply lead, which are electrically conductively connected on the current-source side. The system is configured to electrically connect an electrical load of the system selectably to the first or to the second supply lead. The method includes: configuring an electrical connection between the first supply lead and the load; configuring an electrical interruption between the second supply lead and electrical loads of the system; impressing a current in the first supply lead; determining a first voltage at the input of the first supply lead; determining a second voltage at the input of the second supply lead; determining a first resistance value of the first supply lead using the first and the second voltage and the impressed current.

An RF impedance measurement circuit includes a sensing capacitor connectable with an RF signal path; a first amplitude detector and a first frequency divider, each coupled, with the measurement circuit in operation, to the RF signal path at a first terminal of the sensing capacitor; a second amplitude detector and a second frequency divider, each coupled, with the measurement circuit in operation, to a second terminal of the sensing capacitor; and a phase detection circuit connected to an output of the first frequency divider and to an output of the second frequency divider.

An RF impedance measurement circuit includes a sensing capacitor connectable with an RF signal path; a first amplitude detector and a first frequency divider, each coupled, with the measurement circuit in operation, to the RF signal path at a first terminal of the sensing capacitor; a second amplitude detector and a second frequency divider, each coupled, with the measurement circuit in operation, to a second terminal of the sensing capacitor; and a phase detection circuit connected to an output of the first frequency divider and to an output of the second frequency divider.

A device or system for use with an electric power system is provided. The electric power system has a first bus, a second bus, a third bus, a first line between the first and second buses, and a second line between the third bus and one of the first and second buses. The device or system is operative to determine, responsive to at least one trip event, one or several updated impedances of an equivalent model across a line.

A device or system for use with an electric power system is provided. The electric power system has a first bus, a second bus, a third bus, a first line between the first and second buses, and a second line between the third bus and one of the first and second buses. The device or system is operative to determine, responsive to at least one trip event, one or several updated impedances of an equivalent model across a line.

The present invention relates to a battery cell resistance measurement device and method. A battery cell resistance measurement device according to an embodiment of the present invention including: a carrier signal generation module configured to generate a carrier signal of a first frequency and a second frequency different from the first frequency; a resistance unit including a first resistor and a second resistor having a different resistance value from the first resistor; an impedance measurement unit configured to measure an impedance of both ends of a corresponding application target in a state where the carrier signal is applied to any one of the first resistor, the second resistor, and a battery cell; a switching unit configured to selectively connect any one of the first resistor, the second resistor, and the battery cell to the impedance measurement unit; and a control unit configured to calculate an internal resistance of the battery cell based on the impedance value measured by the impedance measurement unit.

A method for adjusting the value of the characteristic impedance Zo of a microstrip transmission line printed on an outer layer of a printed circuit board (PCB) comprises performing a post-manufacturing process directly on the artwork of a production PCB.

A method for adjusting the value of the characteristic impedance Zo of a microstrip transmission line printed on an outer layer of a printed circuit board (PCB) comprises performing a post-manufacturing process directly on the artwork of a production PCB.

High impedance fault (HIF) detection and location accuracy is provided. An HIF has random, irregular, and unsymmetrical characteristics, making such a fault difficult to detect in distribution grids via conventional relay measurements with relatively low resolution and accuracy. Embodiments disclosed herein provide a stochastic HIF monitoring and location scheme using high-resolution time-synchronized data in micro phasor measurement units (μ-PMUs) for distribution network protection. In particular, a fault detection and location process is systematically designed based on feature selections, semi-supervised learning (SSL), and probabilistic learning.