Intelligent Instability Detection for Islanding Prediction
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The goal of the proposed procedure in this dissertation is the implementation of phasor measurement unit (PMU) based instability detection for islanding prediction procedures using decision tree and neural network modeling. The islanding in the power system define as a separation of the coherent group of generators from the rest of the system due to contingencies, in the case that all generators are coherent together after introducing a fault, it is called stable or non-islanding. The main philosophy of islanding detection in the proposed methodology is to use decision trees and neural network data mining algorithms, performed off-line, to determine the PMU locations, detection parameters, and their triggering values for islanding detection. With the information obtained from accurate system models PMUs can be used online to predict system islanding with high reliability. The proposed approach is proved using a 4000 bus model of the California system. Before data mining was performed, a large number of islanding and non-islanding cases were created for the California model. PMUs data collection was simulated by collecting the voltage and current information in all 500 kV nodes in the system. More than 3000 cases were collected and classified by visual inspection as islanding and non-islanding cases. The proposed neural network and decision tree procedures captured the knowledge for the correct determination of system islanding with a small number of PMUs.
- Doctoral Dissertations