It has been noted that the spin-valve sensor exhibits lower sensitivity with higher temperature because of the variation of GMR ratio, which could lead to the measurement…
It has been noted that the spin-valve sensor exhibits lower sensitivity with higher temperature because of the variation of GMR ratio, which could lead to the measurement error in applications where working temperature changes largely over seasons or times. This paper aims to investigate and compensate the temperature effect of the spin-valve sensor.
A spin-valve sensor is fabricated based on microelectronic process, and its temperature relevant properties are investigated, in which the transfer curves are acquired within a temperature range of −50°C to 125°C with a Helmholtz coil and temperature chamber.
It is found that the sensitivity of spin-valve sensor decreases with temperature linearly, where the temperature coefficient is calculated at −0.25 %/°C. The relationship between sensitivity of spin-valve sensor and temperature is well-modeled.
The temperature drift model of the spin-valve sensor’s sensitivity is highly correlated with tested results, which could be used to compensate the temperature influence on the sensor output. A self-compensation sensor system is proposed and built based on the expression modeled for the temperature dependence of the sensor, which exhibits a great improvement on temperature stability.
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…
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.
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.
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.
Accessibility and availability of data are the main limitations which model will be applied.
This paper provides useful advice for power transformer fault diagnosis method based on DGA data.
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.