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…
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.
Details
Keywords
Aiyu Dou, Ru Bai, Huachen Zhu and Zhenghong Qian
The noise measurement on magnetoresistive (MR) sensors is generally conducted by techniques including single-channel data sampling and fast Fourier transform (FFT) analysis as…
Abstract
Purpose
The noise measurement on magnetoresistive (MR) sensors is generally conducted by techniques including single-channel data sampling and fast Fourier transform (FFT) analysis as well as two-channel cross-correlation. The single-channel method is easy to implement and is widely used in the noise measurement on MR sensors, whereas the two-channel method can only eliminate part of the system noise. This study aims to address two key issues affecting measurement accuracy: calibration of the measurement system and the elimination of system noise.
Design/methodology/approach
The system is calibrated by using a low-noise metal film resistor in that the system noise is eliminated through power spectrum subtraction. Noise measurement and analysis are conducted for both thermal noise and detectivity of magnetic tunnel junction (MTJ) sensor.
Findings
The thermal noise measurement error is less than 2%. The detectivity of the MTJ sensor reaches 27 pT/Hz1/2 at 2 kHz.
Originality/value
This study provides a more practical solution for noise measurement and system calibration on MR sensors with a bias voltage and magnetic field.