Browsing by Subject "Fault detection"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Determining One-Shot Control Criteria in Western North American Power Grid with Swarm Optimization
    (2019-05) Vaughan, Gregory AE; Rovnyak, Steven; King, Brian; Dos Santos, Euzeli
    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.
  • Thumbnail Image
    Item
    POLYNOMIAL CURVE FITTING INDICES FOR DYNAMIC EVENT DETECTION IN WIDE-AREA MEASUREMENT SYSTEMS
    (2013-08-14) Longbottom, Daniel W.; Rovnyak, Steven; Li, Lingxi; Chen, Yaobin
    In a wide-area power system, detecting dynamic events is critical to maintaining system stability. Large events, such as the loss of a generator or fault on a transmission line, can compromise the stability of the system by causing the generator rotor angles to diverge and lose synchronism with the rest of the system. If these events can be detected as they happen, controls can be applied to the system to prevent it from losing synchronous stability. In order to detect these events, pattern recognition tools can be applied to system measurements. In this thesis, the pattern recognition tool decision trees (DTs) were used for event detection. A single DT produced rules distinguishing between and the event and no event cases by learning on a training set of simulations of a power system model. The rules were then applied to test cases to determine the accuracy of the event detection. To use a DT to detect events, the variables used to produce the rules must be chosen. These variables can be direct system measurements, such as the phase angle of bus voltages, or indices created by a combination of system measurements. One index used in this thesis was the integral square bus angle (ISBA) index, which provided a measure of the overall activity of the bus angles in the system. Other indices used were the variance and rate of change of the ISBA. Fitting a polynomial curve to a sliding window of these indices and then taking the difference between the polynomial and the actual index was found to produce a new index that was non-zero during the event and zero all other times for most simulations. After the index to detect events was chosen to be the error between the curve and the ISBA indices, a set of power system cases were created to be used as the training data set for the DT. All of these cases contained one event, either a small or large power injection at a load bus in the system model. The DT was then trained to detect the large power injection but not the small one. This was done so that the rules produced would detect large events on the system that could potentially cause the system to lose synchronous stability but ignore small events that have no effect on the overall system. This DT was then combined with a second DT that predicted instability such that the second DT made the decision whether or not to apply controls only for a short time after the end of every event, when controls would be most effective in stabilizing the system.