ALARM-BASED FAULT DIAGNOSIS IN POWER SYSTEMS USING ABDUCTIVE INFERENCE
Date
1996-01
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Degree Level
Masters
Abstract
Electric power utilities rely on extensive monitoring of the power system to insure integrity, safety and reliability. Information gathered throughout the network is transmitted to a control centre for assessment by trained professionals. The information consists of measured values, such as, voltages, currents and power, as well as alarms indicating abnormal conditions. System operators use this information to diagnose system faults in the event of a disturbance. The large number of alarms generated during contingencies can make the diagnosis process very difficult. Various computer- based diagnosis techniques have been suggested in the literature, using either Expert Systems or Artificial Neural Networks. Both of these approaches rely on experientially derived rules or expert knowledge and suffer from other limitations.
The research reported in this thesis proposes a new diagnosis approach using Abductive Inference. The diagnosis method is based on the power system's protection logic and requires no experientially derived rules. The implementation can be effected directly from the protection logic and instrumentation diagrams. The diagnosis algorithm, abductive inference algorithm and the software implementation of a fault diagnosis system are presented. The resulting diagnosis tool also serves as a fault simulator and has been shown to produce accurate results.
The proposed diagnostic technique was extensively tested by using simulated and field recorded data. Using only breaker status alarms as input data, the diagnosis method was able to correctly identify the corresponding faults for a variety of contingencies.
Test results show that the proposed technique is simple to implement and can produce accurate results.
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Degree
Master of Science (M.Sc.)
Department
Electrical Engineering