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.

Condition Assessment of Power Transformers Based on Health Index Value

A new model of faults classification in power transformers based on data optimization method

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/hve2.12074

High voltage power transformer condition assessment considering the health index value and its decreasing rate

In this study, statistical distribution model (SDM) is used to predict the health index (HI) of transformers by utilizing the condition parameters data from dissolved gas analysis (DGA), oil quality analysis (OQA), and furanic compound analysis (FCA), respectively. First, the individual condition parameters data were categorized based on transformer age from year 1 to 15. Next, the individual condition parameters data for every age were fitted while using a probability plot to find the representative distribution models. The distribution parameters were calculated based on 95% confidence level and extrapolated from year 16 to 25 through representative fitting models. The individual condition parameters data within the period were later calculated based on the estimated distribution parameters through the inverse cumulative distribution function (ICDF) of the selected distribution models. The predicted HI was then determined based on the conventional scoring method. The Chi-square test for statistical hypothesis reveals that the predicted HI for the transformer data is quite close to the calculated HI. The average percentage of absolute error is 2.7%. The HI that is predicted based on SDM yields 97.83% accuracy for the transformer data.

Application of Statistical Distribution Models to Predict Health Index for Condition-Based Management of Transformers

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.

The Analysis of Power Transformer Population Working in Different Operating Conditions with the Use of Health Index

Power transformers represent the highest value of the equipment installed in transmission substations, comprising up the 60% of the total investment. They are expected to operate for several decades without faults and possibly without relevant unscheduled maintenance practice. The new approach is developed for reducing time based maintenance and, increasing condition based maintenance and to introduce predictive maintenance as well. The aim of predictive maintenance is first to predict when transformer failure might occur, and secondly, to prevent occurrence of the failure by performing maintenance. Diagnostic information can be evaluated individually or better by a complex algorithm which merges all the single inputs and their Rate of Increase (RoI) creating a mono-dimensional figure called Health Index (HI). This concept represents a real ‘shifting of paradigm’, as it deeply affects the criteria for transformers grid management and selection of the electrical utilities and grid companies.

Luigi Calacara

Papers

Transformers Fleet Management Through the use of an Advanced Health Index