Research on fault diagnosis method for transformer based on fuzzy genetic algorithm and artificial neural network

Abstract

Purpose -The purpose of this paper is to define a new method (grey relational analysis (GRA)) for extracting pattern samples of dissolved gases in power transformer oil, then a hybrid algorithm of the back-propagation (BP) network and fuzzy genetic algorithm-artificial neural network (FGA-ANN) is used to power transformer fault diagnosis based on extracted pattern samples. Design/methodology/approach -The existing manners (e.g. international electro technical commission triple-ratio method), in practice, have certain faultiness due to the ambiguity of the inference and insufficient standard for judgment. So GRA method is chosen to solve a problem of optimal pattern samples data, then a hybrid algorithm of the BP network and FGA-ANN is developed to optimize initial weights and to enable fast convergence of the BP network, and lastly, this algorithm is applied to the classification of dissolved gas analysis (DGA) data and power transformer fault diagnosis. Findings -If possible, the results should be accompanied by significance. For comparative studies, the proposed scheme does not require the three ratio code and high diagnosis accuracy is obtained. In addition, useful information is provided for future fault trends and multiple faults analysis. Research limitations/implications -Accessibility and availability of data are the main limitations which model will be applied. Practical implications -This paper provides useful advice for power transformer fault diagnosis method based on DGA data. Originality/value -The new method of optimal choice of options of pattern samples due to GRA. The paper is aimed at optimized samples data classified and abandons the traditional ratio method.

Inflation Rate Prediction in Indonesia using Optimized Support Vector Regression Model

“…In the generation of 4000, the fitness value has decreased as the lowest fitness value. If the number of generations is more, the longer computation time needful and the resulting solutions are not necessarily optimal [23].…”

“…Evolutionary biology is GA origins which used to find approximate solutions for optimization problems [22]. A population in GA consist of some chromosome that represents the possible solutions [23]. There are three main processes in GA to create a new generation in each iteration, there are selection, crossover, and mutation [24].…”

Using Research Methods in Human Computer Interaction to Design Technology for Resilience

“…If the pre-set parameters, such as crossover rate ( c p ) and mutation rate ( m p ) are inappropriate, the fittest solution cannot be found. In standard GA, c p and m p are fixed, which may influence the performance of the algorithm (Peng and Song, 2010). Many scholars suggest improving the standard GA method by changing the pre-set parameters, such as c p and m p (Maiti, 2011).…”

“…GA method is a search method used to find approximate solutions for optimization problems which use techniques inspired by evolutionary biology such as selection, crossover and mutation (Peng and Song, 2010). The standard process of GA is an iterative process loosely based on natural selection, crossover and mutation (shown in Fig.1).…”

“…However, as Peng and Song (2010) pointed out, how to change the value of c p and m p in the process is quite complex, as they cannot be defined as precise formula. Fuzzy theory is used in literature to integrate with standard GA to improve the performance of GA (Peng and Song, 2010;Maiti, 2011;Taleizadeh, et al, 2013).…”