In electric power systems, health index algorithms are mostly used for evaluation of the transformer population. In this method, some assessment criteria are insensitive when it comes to judging the technical state of the edges of the age spectrum. This paper presents a new health index calculation method that aims to improve the overall effectiveness of the assessment. The proposed algorithm is based on regularly conducted oil diagnostics and easily available maintenance data to enable estimation and updating of the device’s health status in short intervals from an operational point of view. This method is compared to another health index algorithm built from the same parameters, but with different weights and an alternative result assessment philosophy. The two health index calculation methods are tested on a population of 96 power transformers and then compared to results obtained with an expert system, which is based on much more advanced diagnostic tests to determine the technical condition of the unit. The results of the experiment show that proper selection of weighting factors of the transformer’s technical condition parameters during health index calculation may help in simplifying its assessment while maintaining satisfactory accuracy in comparison to a highly advanced expert method.

Frequency Response Analysis (FRA) is a test method used for assessment of mechanical condition of transformer active parts. Its biggest problem is the interpretation of test results, namely the relationship between scale of differences between compared curves and the decision for further operation of the given transformer. Very often visible differences between two FRA curves do not mean that there is a deformation in the winding. The cause of the curve shift may come from other elements of the transformer that influence inductive or capacitive parameters. This paper takes under consideration the influence of both capacitance and inductance changes on transformer frequency response (FR). The analysis is performed with the computer model of a transformer and also some experimental results are presented, showing the influence of capacitance and inductance changes on the FR of real transformers. The results of research showed that this influence may lead to misleading effects on the shape of FR characteristics. The paper presents an analysis that can be used in the assessment of FRA measurement, especially in the case of uncertain data comparison results.

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The management of the power transformer population is a complex process, as the grid companies operate thousands of devices. For this issue, the health index method can be applied to facilitate asset management. The algorithm can be used not only in the technical assessment of the individual units, but also to determine the relationships within the whole population. In this paper, the presented health index method consists of periodic oil diagnostics, including the physicochemical properties, dissolved gas analysis, and furfural content, and further assessment in terms of the criticality of the device to determine the technical condition. The algorithm was specifically designed to reflect even the smallest changes of the input parameters in the final score. The performance of the health index was tested on 620 oil analyses from 220 transformers divided into four subpopulations based on the service conditions. The results have proven to be largely dependent on the criticality level and the operating conditions of the device. The analysis of the study group has shown the influence of corrective maintenance on the mean value of the health index score.