Development of an Online Detection Setup for Dissolved Gas in Transformer Insulating Oil

The type and concentration of dissolved gases in transformer insulating oil are used to assess transformer conditions. In this paper, an online detection setup for measuring the concentration of multicomponent gases dissolved in transformer insulating oil is developed, which consists of an oil-gas separation system and an optical system for acquiring the transformer status in real time. The oil-gas separation system uses low pressure, constant temperature, and low-frequency stirring as working conditions for degassing large-volume oil samples based on modified headspace degassing. The optical system uses tunable diode laser absorption spectroscopy (TDLAS) to determine the gas concentration. Six target gases (methane, ethylene, ethane, acetylene, carbon monoxide, and carbon dioxide) were detected by three near-infrared lasers (1569, 1684, and 1532 nm). The stability of the optical system was improved by the common optical path formed by time-division multiplexing (TDM) technology. The calibration experiments show that the second harmonics and the concentrations of the six gases are linear. A comparison experiment with gas chromatography (GC) demonstrates that the error of acetylene reaches the nL/L level, while the other gases reach the μL/L level. The data conforms to the power industry testing standards, and the state of the transformer is analyzed by the detected six characteristic gases. The setup provides an effective basis for the online detection of dissolved gas in transformer insulating oil.

Online Detection of Watercore Apples by Vis/NIR Full-Transmittance Spectroscopy Coupled with ANOVA Method

Watercore is an internal physiological disorder affecting the quality and price of apples. Rapid and non-destructive detection of watercore is of great significance to improve the commercial value of apples. In this study, the visible and near infrared (Vis/NIR) full-transmittance spectroscopy combined with analysis of variance (ANOVA) method was used for online detection of watercore apples. At the speed of 0.5 m/s, the effects of three different orientations (O1, O2, and O3) on the discrimination results of watercore apples were evaluated, respectively. It was found that O3 orientation was the most suitable for detecting watercore apples. One-way ANOVA was used to select the characteristic wavelengths. The least squares-support vector machine (LS-SVM) model with two characteristic wavelengths obtained good performance with the success rates of 96.87% and 100% for watercore and healthy apples, respectively. In addition, full-spectrum data was also utilized to determine the optimal two-band ratio for the discrimination of watercore apples by ANOVA method. Study showed that the threshold discrimination model established based on O3 orientation had the same detection accuracy as the optimal LS-SVM model for samples in the prediction set. Overall, full-transmittance spectroscopy combined with the ANOVA method was feasible to online detect watercore apples, and the threshold discrimination model based on two-band ratio showed great potential for detection of watercore apples.

Research on On-Line Detection Method of Transformer Winding Deformation Based on VFTO

At present, the detection of transformer winding deformation faults is carried out in an offline state, which requires the transformer to cooperate with the implementation of planned power outages, or it takes place after the sudden failure of the transformer when it is out of operation. It is difficult to obtain the status information of the windings online in time. Since the transformer will suffer very fast transient overvoltage (VFTO) impact during operation, combined with the principle of the frequency response method, an online detection method of transformer winding deformation based on VFTO is proposed. In order to study the frequency response characteristics of transformer winding under the impact of VFTO, the generation process of VFTO is simulated by simulation software, and the equivalent circuit model of transformer winding before and after deformation is established. The VFTO signal is injected into the transformer circuit model as an excitation source, and the changes of resonant frequencies of frequency response curve under different deformation types and different deformation degrees of winding are analyzed. The simulation results show that the frequency response curves of different winding deformation types are different. Different deformation degrees are simulated by increasing the radial capacitance by 4%, 13%, and 23%, series inductance by 2%, 4%, and 6%, and longitudinal capacitance by 3%, 6%, and 9%, and the change of resonance frequencies can comprehensively reflect the deformation information of winding. At the same time, the tests of different deformation types and deformation degrees of the simulated winding are carried out. The results show that with the deepening of the change degree of the simulated fault inductance value, the frequency response curve shifts to the low-frequency direction, confirming the feasibility of the online detection method of transformer winding deformation based on VFTO.