Rovnyak, StevenVaughan, Gregory AEKing, BrianDos Santos, Euzeli2019-04-232019-04-232019-05https://hdl.handle.net/1805/18921http://dx.doi.org/10.7912/C2/2500Indiana University-Purdue University Indianapolis (IUPUI)The power transmission network is stretched thin in Western North America. When generators or substations fault, the resultant cascading failures can diminish transmission capabilities across wide regions of the continent. This thesis examined several methods of determining one-shot controls based on frequency decline in electrical generators to reduce the effect of one or more phase faults and tripped generators. These methods included criteria based on indices calculated from frequency measured at the controller location. These indices included criteria based on local modes and the rate of change of frequency. This thesis primarily used particle swarm optimization (PSO) with inertia to determine a well-adapted set of parameters. The parameters included up to three thresholds for indices calculated from frequency. The researchers found that the best method for distinguishing between one or more phase faults used thresholds on two Fourier indices. Future lines of research regarding one-shot controls were considered. A method that distinguished nearby tripped generators from one or more phase faults and load change events was proposed. This method used a moving average, a negative threshold for control, and a positive threshold to reject control. The negative threshold for the moving average is met frequently during any large transient event. An additional index must be used to distinguish loss of generation events. This index is the maximum value of the moving average up to the present time and it is good for distinguishing loss of generation events from transient swings caused by other events. This thesis further demonstrated how well a combination of controls based on both rate of change of frequency and local modes reduces instability of the network as determined by both a reduction in RMSGA and control efficiency at any time after the events. This thesis found that using local modes is generally useful to diagnose and apply one-shot controls when instability is caused by one or more phase faults, while when disconnected generators or reduced loads cause instability in the system, the local modes did not distinguish between loss of generation capacity events and reduced load events. Instead, differentiating based on the rate of change of frequency and an initial upward deflection of frequency or an initial downward deflection of frequency did distinguish between these types of events.en-USAttribution-NonCommercial-NoDerivs 3.0 United StatesParticle swarmFault detectionPower systemsOne-shot controlMachine learningOptimization methodsDisconnected generatorsFourier indexThesis