The Method to Determine the Turns Ratio Correction of the Inductive Current Transformer

This paper presents the method for evaluation of the turns ratio correction of the inductive current transformer using the magnetization curves determined at the non-load state and in the load conditions. The presented method may be applied to determine even a fractional winding correction factor. The standard IEC 61869-2 provides the method to determine the turns ratio correction of the tested CT from the measured rms values of voltages on its primary and secondary winding in the non-load state. However, this approach is limited in determining the significant changes in the number of turns of the secondary winding. Moreover, the paper presents the influence of the applied turns ratio correction on the frequency characteristics of the current error and phase displacement of the inductive current transformers evaluated for the transformation of the distorted current.

The quantification of NO_<i>x</i> and SO₂ point source emission flux errors of mobile differential optical absorption spectroscopy on the basis of the Gaussian dispersion model: a simulation study

Mobile differential optical absorption spectroscopy (mobile DOAS) has become an important tool for the quantification of emission sources, including point sources (e.g., individual power plants) and area emitters (e.g., entire cities). In this study, we focused on the error budget of mobile DOAS measurements from point sources, and we also offered recommendations for the optimum settings of such measurements via a simulation with a modified Gaussian plume model. Following the analysis, we conclude that (1) the proper sampling resolution should be between 5 and 50 m. (2) When measuring far from the source, undetectable flux (measured slant column densities (SCDs) are under the detection limit) resulting from wind dispersion is the main error source. The threshold for the undetectable flux can be lowered by larger integration time. When measuring close to the source, low sampling frequency results in large errors, and wind field uncertainty becomes the main error source of SO2 flux (for NOx this error also increases, but other error sources dominate). More measurement times can lower the flux error that results from wind field uncertainty. The proper wind speed for mobile DOAS measurements is between 1 and 4 m s−1. (3) The remaining errors by [NOx] ∕ [NO2] ratio correction can be significant when measuring very close. To minimize the [NOx] ∕ [NO2] ratio correction error, we recommend minimum distances from the source, at which 5 % of the NO2 maximum reaction rate is reached and thus NOx steady state can be assumed. (4) Our study suggests that emission rates < 30 g s−1 for NOx and < 50 g s−1 for SO2 are not recommended for mobile DOAS measurements. Based on the model simulations, our study indicates that mobile DOAS measurements are a very well-suited tool to quantify point source emissions. The results of our sensitivity studies are important to make optimum use of such measurements.

Assessing the Resilience of Agricultural Reservoirs in Ungauged Catchments under Climate Change Using a Ratio Correction Factors-Based Calibration and Run Theory

This study applied ratio correction factor (RCF) optimization to calibrate the daily storage of agricultural reservoirs located in ungauged catchments that lack stream flow data. Using Run theory, we then assessed the impacts of climate change on the resilience of agricultural reservoir operations during reservoir drought conditions. First, we optimized the RCFs of inflow and outflow in three agricultural reservoirs in Korea using limited measurement data from 2008 to 2017; the results showed high performance regarding the simulation of daily reservoir storage. Second, we simulated daily storage volume in reservoirs from 2018 to 2099, using future climate change data, and analyzed the duration and intensity of reservoir drought conditions, which indicated that the storage capacity is under the critical value. Without calibration, the correlation between the simulated and measured reservoir water volumes was very low, but the correlation increased after calibration of the simulated water volumes. A linear relationship between the simulated and measured volumes was observed with a correlation coefficient value of 0.9, indicating that the simulated reservoir values after calibration closely match the measured values. In addition, the maximum intensity of reservoir drought in the Kicheon reservoir was determined to be 486,000 m3 before calibration but 506,000 m3 after calibration. The duration results showed that long-term reservoir drought conditions will be observed more often in the future owing to climate change, and this could be a negative factor affecting the resilience of reservoir operations.

The quantification of NO_<i>x</i> and SO₂ point source emission flux errors of mobile DOAS on the basis of the Gaussian dispersion model: A simulation study

Mobile differential optical absorption spectroscopy (mobile DOAS) has become an important tool for the quantification of emission sources, including point sources (e.g., individual power plants) and area emitters (e.g., entire cities). In this study, we focused on the error budget of mobile DOAS measurements from point sources, and we also offered recommendations for the optimum settings of such measurements. First we established a Gaussian plume model from which the NOx and SO2 distribution from the point source was determined. In a second step the simulated distributions are converted into vertical column densities of NOx and SO2 according to the mobile DOAS measurement technique. With assumed parameters, we then drove the forward model in order to simulate the emissions, after which we performed the analysis. Following this analysis, we conclude that: (1) Larger sampling resolution clearly results in larger flux error. The proper resolution we suggest is between 5 m and 50 m. Even larger resolutions may also be viable, but > 100 m is not recommended. (2) Error effects vary with measurement distance from the source. We found that undetectable flux (measured VCDs are under the detection limit) is the main error source when measuring far from the source, for both NOx and SO2. When measuring close to the source, low sampling frequency results in large flux error. (3) The wind field primarily affects 2 aspects of the flux measurement error. When measuring far from the source, dispersion results in more undetectable flux, which is the main error source. When measuring close to the source, wind field uncertainty becomes the main error source of SO2 flux, but not of NOx. We suggested that the proper wind speed for mobile DOAS measurements is between 1 m/s and 4 m/s. (4) The study of NOx atmospheric chemistry reactions indicated that a [NOx]/[NO2] ratio correction has to be applied when measuring very close to the emission source. But even when such a correction is applied, the remaining errors can be significant. To minimize the [NOx]/[NO2] ratio correction error, we recommended 0.05 NO2 maximum reaction rate as the accepted NOx steady-state thus to determine the proper starting measurement distance. (5) The error of the spectral retrieval is not a main emission flux error source and its error budget varies with the measuring distance. (6) Increasing the number of measurements can lower the flux error that results from wind field uncertainty and retrieval error. This directly indicates that SO2 flux error could be lowered if the measurements are repeated when not too far from the emission source. With regard to NOx, more measurement times can only work effectively when not very close or too far from the source. (7) Also the effects of the temporal and spatial sampling are investigated. When the sampling resolution is prescribed, the integration depends on the driving speed and the corresponding flux error is mainly determined by the undetectable flux. When the car speed is prescribed, the integration time is determined by the sampling resolution for measuring near the source, while undetectable flux predominates when far away. (8) As a general recommendation, our study suggests that emission rates < 30 g/s for NOx and < 50 g/s for SO2 are not recommended for mobile DOAS measurements. The source height affects the undetectable flux, but has little influences on the total error. Based on the model simulations our study indicates that mobile DOAS measurements are very well suited tool to quantify point source emissions. The results of our sensitivity studies are important to make optimum use of such measurements.

Integrated Correction Algorithm for X Band Dual-Polarization Radar Reflectivity Based on CINRAD/SA Radar

The values of ratio a of the linear relationship between specific attenuation and specific differential phase vary significantly in convective storms as a result of resonance scattering. The best-linear-fit ratio a at X band is determined using the modified attenuation correction algorithm based on differential phase and attenuation, as well as the premise that reflectivity is unattenuated in S band radar detection. Meanwhile, the systemic reflectivity bias between the X band radar and S band radar and water layer attenuation (ZW) on the wet antenna cover of the X band radar are also considered. The good performance of the modified correction algorithm is demonstrated in a moderate rainfall event. The data were collected by four X band dual-polarization (X-POL) radar sites, namely, BJXCP, BJXFS, BJXSY, and BJXTZ, and a China’s New Generation Weather Radar (CINRAD/SA radar) site, BJSDX, in Beijing on 20 July 2016. Ratio a is calculated for each volume scan of the X band radar, with a mean value of 0.26 dB deg−1 varying from 0.20 to 0.31 dB deg−1. The average values of systemic reflectivity bias between the X band radar (at BJXCP, BJXFS, BJXSY, and BJXTZ) and S band radar (at BJSDX) are 0, −3, 2, and 0 dB, respectively. The experimentally determined ZW is in substantial agreement with the theoretically calculated ones, and their values are an order of magnitude smaller than rain attenuation. The comparison of the modified attenuation correction algorithm and the empirical-fixed-ratio correction algorithm is further evaluated at the X-POL radar. It is shown that the modified attenuation correction algorithm in the present paper provides higher correction accuracy for rain attenuation than the empirical-fixed-ratio correction algorithm.

Improving the Accuracy of Fault Frequency by Means of Local Mean Decomposition and Ratio Correction Method for Rolling Bearing Failure

The fault frequencies are as they are and cannot be improved. One can only improve its estimation quality. This paper proposes a fault diagnosis method by combining local mean decomposition (LMD) and the ratio correction method to process the short-time signals. Firstly, the vibration signal of rolling bearing is decomposed into a series of product functions (PFs) by LMD. The PF, which contains the richest fault information, is selected to perform envelope spectrum analysis by the Hilbert transform (HT). Secondly, the Hilbert envelope spectrum of the selected PF is corrected with the ratio correction method. Finally, higher precision fault frequencies are extracted from the corrected Hilbert envelope spectrum, and then the fault location is accurately determined. The proposed method of this paper can be used in online real-time monitoring technology of rolling bearing failure.