|dc.description.abstract||In recent years, the trade-off between quality and cost of power system components has become a matter of interest for many utilities. The widespread use of costly electricity networks either in residential or industrial areas has encouraged service providers to find a proper strategy that will minimize the overall life-cycle cost while keeping components in good working condition. The power transformer, which represents approximately 60% of the overall cost of the network, is ranked as one of the most important and expensive components. However, the transformer's sudden failure puts the system in a serious or critical condition which in most cases causes catastrophic loss to both utilities and customers. Significant attention has been given to monitoring and diagnostic techniques that observe any abnormal behaviour, assess the transformer's condition, and therefore minimize the probability of unplanned outage. Yet, applying many various monitoring tests is not always applicable due to the following factors: some tests require the unit to be taken out from service for testing, insufficient availability of man power, and significant cost of applying all the tests. Thus, there is a vital demand for an intelligent method of minimizing the number of monitoring tests without losing much information about the transformer's actual condition.
In this research, data mining techniques have been employed to evaluate the transformer's state through intelligent selection criteria that determines the optimal number of monitoring tests in cost-effectiveness. Feature selection technique based on ranker search method has been used to rank the monitoring tests (features) in a priority sequence from their individual evaluation, and to select the most inductive tests that provide the most information about the unit's condition. When the measured data from monitoring tests is collected and prepared, a diagnostic technique is applied to assess the condition of the transformer. In this regard, Support Vector Machine (SVM) has been utilized to perform this task due to its robust classification accuracy. SVM is first applied to the full number of tests, and then the number of monitoring tests is reduced by one after each classification process using the feature selection algorithm. The selected number of monitoring tests has shown the best possible accuracy the classifier can reach over the whole number of tests. Radial Basis Function (RBF) classifier has been used in the classification process for results comparison purposes. This proposed work contributes towards finding an intelligent method of evaluating the transformer state as well as minimizing the number of tests without losing much information about the unit's actual condition. Therefore, this method facilitates deciding a wise course of action regarding the transformer: either maintain, repair, or replace.||en