Candidate
Sally Daif
Title
Condition Assessment of Power Transformer Winding Insulation Based on Partial Discharge Detection
Supervisor
Magdy Salama
Abstract
Power transformers are important components of power systems, as their failure can result in major losses to electric utilities. Transformer windings are responsible for approximately 30% of transformer failures, and of these failures, a principal cause is winding insulation failure due to partial discharge (PD). When PD occurs in power transformers, the insulation system is damaged in two ways: by gases created from the oil and paper, and by degradation of its solid insulation. Solid insulation degradation is correlated with the PD apparent charge value, which is usually measured by conventional PD detectors. However, these are not suitable for use in a transformer environment due to noise interference and the heavy weight of the equipment. In addition to considering the PD apparent charge, identifying the nature of the PD is essential for assessing the transformer winding insulation condition. Due to the difficulties associated with PD measurement in a transformer environment, PD severity assessment is still performed by dissolved gas analysis, which does not provide details about the PD’s crucial characteristics. To address these several shortcomings, this research develops two distinct procedures: PD detection and PD severity assessment. Using the leakage current measured at the transformer neutral, the detection procedure determines the PD charge, location, and source. The severity assessment procedure then uses this information to assess the transformer winding insulation condition.
In the PD detection procedure, the leakage current measured at the transformer neutral undergoes two procedures: PD source classification and PD charge determination. As well, a PD localization procedure is developed for the PD charge determination procedure. The techniques used for PD source classification and localization are similar and are based on designing feedforward neural network classifiers using the statistical features extracted from PD leakage current signals that correspond to different origins, sources, or locations. The method is tested on a three-phase transformer. The selection of the proper feature combination results in better recognition of PD source type and location. In the PD determination procedure, the PD charge is calculated from the PD current injected into the transformer winding during a PD event, using the corresponding PD leakage current and the winding transfer function from the PD location to the transformer neutral. For the transformer under test, a bank of transfer functions from all possible locations along the winding to the transformer neutral is developed and used in the charge calculation for test PD signals. The error between the measured and calculated charges for test PD signals is 10% or less.
In the PD severity assessment procedure, a fuzzy logic system maps the PD characteristics, charge and source into a quantitative index called the PDI. Based on the damage caused to the insulation system due the PD charge and source, the PDI is used to classify the transformer condition into one of five categories: normal, questionable, harmed, critical, and dangerous. This PDI can also be used in transformer health index calculations.