Browsing by Author "Pakdel, Zahra"

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    Characterization, Modeling of Piezoelectric Pressure Transducer for Facilitation of Field Calibration
    Pakdel, Zahra (Virginia Tech, 2007-05-21)
    Currently in the marketplace, one of the important goals is to improve quality, and reliability. There is great interest in the engineering community to develop a field calibration technique concerning piezoelectric pressure sensor to reduce cost and improve reliability. This paper summarizes the algorithm used to characterize and develop a model for a piezoelectric pressure transducer. The basic concept of the method is to excite the sensor using an electric force to capture the signature characteristic of the pressure transducer. This document presents the frequency curve fitted model based on the high frequency excitation of the piezoelectric pressure transducer. It also presents the time domain model of the sensor. The time domain response of the frequency curve fitted model obtained in parallel with the frequency response of the time domain model and the comparison results are discussed. Moreover, the relation between model parameters and sensitivity extensively is investigated. In order to detect damage and monitor the condition of the sensor on line the resonance frequency comparison method is presented. The relationship between sensitivity and the resonance frequency characteristic of the sensor extensively is investigated. The method of resonance monitoring greatly reduces the cost of hardware. This work concludes with a software implementation of the signature comparison of the sensor based on a study of the experimental data. The software would be implemented in the control system.
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    Intelligent Instability Detection for Islanding Prediction
    Pakdel, Zahra (Virginia Tech, 2011-05-06)
    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.