Jaafari Mousavi, Mir Rasoul (2005-12). Underground distribution cable incipient fault diagnosis system. Doctoral Dissertation. Thesis uri icon

abstract

  • This dissertation presents a methodology for an efficient, non-destructive, and online incipient fault diagnosis system (IFDS) to detect underground cable incipient faults before they become catastrophic. The system provides vital information to help the operator with the decision-making process regarding the condition assessment of the underground cable. It incorporates advanced digital signal processing and pattern recognition methods to classify recorded data into designated classes. Additionally, the IFDS utilizes novel detection methodologies to detect when the cable is near failure. The classification functionality is achieved through employing an ensemble of rule-based and supervised classifiers. The Support Vector Machines, designed and used as a supervised classifier, was found to perform superior. In addition to the normalized energy features computed from wavelet packet analysis, two new features, namely Horizontal Severity Index, and Vertical Severity Index are defined and used in the classification problem. The detection functionality of the IFDS is achieved through incorporating a temporal severity measure and a detection method. The novel severity measure is based on the temporal analysis of arrival times of incipient abnormalities, which gives rise to a numeric index called the Global Severity Index (GSI). This index portrays the progressive degradation path of underground cable as catastrophic failure time approaches. The detection approach utilizes the numerical modeling capabilities of SOM as well as statistical change detection techniques. The natural logarithm of the chronologically ordered minimum modeling errors, computed from exposing feature vectors to a trained SOM, is used as the detection index. Three modified change detection algorithms, namely Cumulative Sum, Exponentially Weighted Moving Averages, and Generalized Likelihood Ratio, are introduced and applied to this application. These algorithms determine the change point or near failure time of cable from the instantaneous values of the detection index. Performance studies using field recorded data were conducted at three warning levels to assess the capability of the IFDS in predicting the faults that actually occurred in the monitored underground cable. The IFDS presents a high classification rate and satisfactory detection capability at each warning level. Specifically, it demonstrates that at least one detection technique successfully provides an early warning that a fault is imminent.
  • This dissertation presents a methodology for an efficient, non-destructive, and online
    incipient fault diagnosis system (IFDS) to detect underground cable incipient faults before they
    become catastrophic. The system provides vital information to help the operator with the
    decision-making process regarding the condition assessment of the underground cable. It
    incorporates advanced digital signal processing and pattern recognition methods to classify
    recorded data into designated classes. Additionally, the IFDS utilizes novel detection
    methodologies to detect when the cable is near failure.
    The classification functionality is achieved through employing an ensemble of rule-based
    and supervised classifiers. The Support Vector Machines, designed and used as a supervised
    classifier, was found to perform superior. In addition to the normalized energy features
    computed from wavelet packet analysis, two new features, namely Horizontal Severity Index,
    and Vertical Severity Index are defined and used in the classification problem.
    The detection functionality of the IFDS is achieved through incorporating a temporal
    severity measure and a detection method. The novel severity measure is based on the temporal
    analysis of arrival times of incipient abnormalities, which gives rise to a numeric index called the
    Global Severity Index (GSI). This index portrays the progressive degradation path of
    underground cable as catastrophic failure time approaches. The detection approach utilizes the
    numerical modeling capabilities of SOM as well as statistical change detection techniques. The
    natural logarithm of the chronologically ordered minimum modeling errors, computed from
    exposing feature vectors to a trained SOM, is used as the detection index. Three modified change
    detection algorithms, namely Cumulative Sum, Exponentially Weighted Moving Averages, and
    Generalized Likelihood Ratio, are introduced and applied to this application. These algorithms
    determine the change point or near failure time of cable from the instantaneous values of the
    detection index.
    Performance studies using field recorded data were conducted at three warning levels to
    assess the capability of the IFDS in predicting the faults that actually occurred in the monitored underground cable. The IFDS presents a high classification rate and satisfactory detection
    capability at each warning level. Specifically, it demonstrates that at least one detection
    technique successfully provides an early warning that a fault is imminent.

publication date

  • December 2005