Abstract: Various embodiments are described herein for a device and method for predicting an out-of-step or transient instability condition in a power system due to a disturbance, such as a fault, where the power system comprises a power generator. The method comprises determining values for a state plane trajectory for the power generator during the disturbance based on using an internal voltage angle of the power generator or an equivalent power generator and a during fault rotor speed of the power generator or equivalent power generator as state variables, determining time scale values; determining values for a critical state plane trajectory for the power generator after clearing the fault, determining values for a state plane trajectory for the power generator after clearing the fault, determining a critical clearing angle and a critical clearing time, and predicting that the power generator will be stable or unstable by comparing the time to clear the fault with the critical clearing time.

Abstract: This application describes a state deviation technique for identifying transient instabilities in power systems. Such instabilities may result from disturbances such as external faults and power swing conditions. Detection of transient instabilities is based on the direction of change of phase angle of a machine such as a generator at an equilibrium point. Method and apparatus as disclosed may also be used for assessing system-wide transient stability of a power system or portion thereof.

Abstract: Various embodiments are described herein for a device and method for predicting an out-of-step or transient instability condition in a power system due to a disturbance, such as a fault, where the power system comprises a power generator. The method comprises determining values for a state plane trajectory for the power generator during the disturbance based on using an internal voltage angle of the power generator or an equivalent power generator and a during fault rotor speed of the power generator or equivalent power generator as state variables, determining time scale values; determining values for a critical state plane trajectory for the power generator after clearing the fault, determining values for a state plane trajectory for the power generator after clearing the fault, determining a critical clearing angle and a critical clearing time, and predicting that the power generator will be stable or unstable by comparing the time to clear the fault with the critical clearing time.

Abstract: A knowledge-based system is defined that ensures network radiatity during reconfiguration of a power distribution system. Network radiality heuristics idenufy a pair of switching operations that will preserve radiality. A line section table specifies the line section identifier, start bus, end bus, line section type, and switch existence for each line section in the network. A bus table identifies the line section that supplies the bus and those line sections that are fed by the bus for each bus in the network. A matrix-structured knowledge base with five distinct rule bases is used. The line section and bus tables must be updated after any switching operations are enacted. Extensive simulations of the knowledge-based system revealed that a solution was found in a time-efficient manner. This knowledge-based system enhances network reliability and hence prevents power outages.

Abstract: The radial distribution system is the most commonly used power distribution system, but lacks reliability because there is only one power source for a group of customers. A knowledge-based system is defined that ensures network radiality during reconfiguration of a power distribution system. Network radiality heuristics identify a pair of switching operations that will preserve radiality. A line section table specifies the line section identifier, start bus, end bus, line section type, and switch existence for each line section in the network. A bus table identifies the line section that supplies the bus and those line sections that are fed by the bus for each bus in the network. A matrix-structured knowledge base with five distinct rule bases is used. Rule base A is used in determining which other rule bases to activate.