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The development of a new dissolved gas analysis (DGA) method often requires a comparative study to assess the accuracy of the proposed technique. This is faced with the following challenges: (i) the time and effort required to implement and validate the implementation of existing DGA methods, adds to the comparative study cost; (ii) the output states of different DGA methods are not similar, which makes it difficult to put methods side by side in a comparative study; and (iii) the availability of test data is limited. In this study, a user‐friendly graphical user interface software package, DGALab, is developed to overcome these challenges. DGALab implements a unified DGA diagnosis framework to map the output states of DGA methods to uniform specifications. DGALab includes a library implementing most common DGA techniques, and includes a repository for input datasets available in the literature and collected directly from laboratories. DGALab simplifies the addition of new DGA techniques written in virtually any programming language. As a result, the process of developing a new DGA technique is greatly simplified using DGALab. To evaluate the software package results, the datasets and methods implemented therein were used to regenerate the results published in earlier research papers. [ABSTRACT FROM AUTHOR]

In this study, a conditional probability scheme is designed to diagnose transformer incipient faults based on the percentage of five dissolved gases (hydrogen (H2), methane (CH4), ethane (C2 H6), ethylene (C2 H4), and acetylene (C2 H2)) with respect to their summation. Based on the fault features, the point probabilities of each fault type occurrence and non‐occurrence are computed. Then, the conditional probability of certain fault occurrence is estimated to specify the probabilistic indication of each fault type occurrence. Multivariate normal probability density function is considered to point out the conditional probability of the fault occurrence. It is implemented in different scenarios to define the fault type (partial discharge, low energy discharge, high energy discharge, low thermal, medium thermal, and high thermal) where the best scenario is selected. The proposed technique has the merits of simplicity and ease of implementation. It is assessed through the analysis of 403 dissolved gas sample dataset collected from the Egyptian electric utility as well as from credited literatures. The proposed probabilistic technique is evaluated in comparison with other methods in literatures. Also, it is validated against uncertainty in DGA data up to 20%. The results reveal the ability and reliability of the proposed technique for transformer fault diagnosis. [ABSTRACT FROM AUTHOR]