International Journal of Computer Science Issues

More than a traditional journal...

A power transformer is a static piece of apparatus with two or more windings. By electromagnetic induction, it transforms a system of alternating voltages and current into another system of alternating voltages and current of different values, of the same frequency, for the purpose of transmitting electrical power. For example, distribution transformers convert high-voltages electricity to lower voltages levels acceptable for use in home and business. A power transformer is one of the most expensive pieces of equipment in an electricity system. Monitoring the performance of a transformer is crucial in minimizing power outages through appropriate maintenance thereby reducing the total cost of operation. This idea provides the use of neural fuzzy technique in order to better predict oil conditions of a transformer. The preliminary phase is the first and most important step of a neural fuzzy modeling process. It aims to collect a set of data, which is expected to be a representative sample of the system to be modeled. In this phase, known as data processing, data are cleaned to make learning easier. This involves incorporation of all relevant domain knowledge at the level of an initial data analysis, including any sort of preliminary filtering of the observed data such as missing data treatment or feature selection. The preprocessing phase returns the data set in a structured input-output form, commonly called a training set. Once this preliminary phase is completed, the learning phase begins. This paper will focus exclusively on this second phase assuming that data have already been preprocessed. The learning phase is essentially a search, in a space of possible model configurations, of the model that best represents the power transformer testing values. As in any other search task, the learning procedure requires a search space, where the solution is to be found, and some assessment criterion to measure the quality of the solution.