Reliability of the wide-area power system is becoming a greater concern as the power grid is growing. Delivering electric power from the most economical source through fewest and shortest transmission lines to customers frequently increases the stress on the system and prevents it from maintaining its stability.
Events like loss of transmission equipment and phase to ground faults can force the system to cross its stability limits by causing the generators to lose their synchronism. Therefore, a helpful solution is detection of these dynamic events and prediction of instability.
Decision Trees (DTs) were used as a pattern recognition tool in this thesis. Based on training data, DT generated rules for detecting event, predicting loss of synchronism, and selecting stabilizing control. To evaluate the accuracy of these rules, they were applied to testing data sets.
To train DTs of this thesis, direct system measurements like generator rotor angles and bus voltage angles as well as calculated indices such as the rate of change of bus angles, the Integral Square Bus Angle (ISBA) and the gradient of ISBA were used. The initial method of this thesis included a response based DT only for instability prediction. In this method, time and location of the events were unknown and the one shot control was applied when the instability was predicted. The control applied was in the form of fast power changes on four different buses.
Further, an event detection DT was combined with the instability prediction such that the data samples of each case was checked with event detection DT rules. In cases that an event was detected, control was applied upon prediction of instability. Later in the research, it was investigated that different control cases could behave differently in terms of the number of cases they stabilize.
Therefore, a third DT was trained to select between two different control cases to improve the effectiveness of the methodology. It was learned through internship at Midwest Independent Transmission Operators (MISO) that post-event steady-state analysis is necessary for better understanding the effect of the faults on the power system. Hence, this study was included in this research.
Source: IUPUI
Author: Naghsh Nilchi, Maryam
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