An intelligent system for condition assessment of power transformers
Many utilities have migrated from the conventional time-based to condition-based maintenance.
byMohamed Khalil
1. Introduction
The continuous demand for optimising the lifetime cost of transformers through informed decisions has led to adopting condition monitoring tools in addition to the traditional offline testing methods. Many utilities have migrated from the conventional time-based to condition-based maintenance. The outcome of the current diagnostic methods, online and offline, is large volumes of accumulated data. As a result, the use of quantitative indicators, such as health index (HI), is gaining wide popularity, especially when it comes to prioritising maintenance and replacement activities.
HI is an approach that combines all the information of a transformer in order to provide a single quantitative index that expresses the overall condition based on the measured data. The available data can be online, from condition monitoring systems, operational, offline, visual inspection, etc.
Fuzzy logic is utilised to build up an intelligent model for evaluating the health index of power transformers; fuzzy logic is based on a network of if-then rules that are constructed using the experience of experts
Several methods are developed to convert the existing diagnostic data into a HI. For instance, binary logistic regression is used in [1] for this purpose. The input data are classified into categories, healthy or unhealthy. Weights, assigned to each input, are calculated using the maximum likelihood criterion. Another approach is introduced in [2-5] to calculate the HI using the weighting average such that:
Despite the simplicity of weighting methods, the determination of the weight factors for the diagnostic tests is based on the experience of experts, which differs from one person to another. In addition, setting a sharp threshold of diagnostic measurements for scoring is very difficult. In practice, there can be overlaps of scores; exact measurand does not exist due to the unavoidable imperfection involved in the measurement process [7]. Moreover, from experience, the traditional weighting average method may omit the influence of a bad diagnostic test result on the overall health condition.where n is the number of diagnostic tests, S is the score of each test measurement, and W is the allocated weight given to each diagnostic test. In [6], the weighting sum is used instead to calculate the HI.
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