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Standalone low-voltage DC (LVDC) microgrids have emerged as potential alternatives in the context of effective rural electrification. The factors of reduction in conversion costs, paradigm shift in voltage levels of domestic loads made LVDC Microgrids more preferable. Being driven by intermittent renewable sources and dynamic loading, the voltage control is quite crucial and challenging to ensure a stable LVDC microgrid. Furthermore, delivering the control objectives becomes very difficult in the presence of sensor faults. In order to address this issue of sensor faults, this work proposes an Extended Luenberger Observer-based Active Sensor Fault-Tolerant Control (ELO-ASFTC) scheme. The proposed control scheme consists of the aspects of fault detection and isolation (FDI), and control reconfiguration. The proposed control scheme achieved the voltage tracking objective of the LVDC microgrid even under the presence of a faulty DC bus voltage sensor. The performance of the proposed scheme is observed in handling different sensor fault scenarios, loading conditions and irradiance changes. The effectiveness of the proposed control scheme is studied analytically, through the simulation studies in MATLAB, and hardware experimentation on the in house LVDC microgrid test bed. [ABSTRACT FROM AUTHOR]

A prior prediction of oxides of nitrogen, i.e., NOX (sum of NO and NO2), in diesel exhaust while treating with electric discharge-based nonthermal plasma (NTP) technique, would assist the researchers in planning the resources required for the treatment. In this present study, the performance of different regression-based models, i.e., linear, support vector regression and Gaussian process regression (GPR), has been analyzed for predicting the NOX concentrations based on the values of five dominating parameters of the NTP treatment. Experiments have been conducted and collected a dataset of 4032 number of input–output pairs to be used for training and testing of the regression models. The performances of these models have been assessed while testing them for the unseen set of data. A comparison of root-mean-square error (RMSE) has been made, where Matern 3/2 type of GPR model has been found to be the best among all the considered models with an RMSE of 1.86 ppm for a test data of 1210 sets. The model is shown to perform consistently well even when the test data are increased to 50% of total data. Regression analysis shows that the NOX can be predicted with very good accuracy using the Matern 3/2 type of GPR model. [ABSTRACT FROM AUTHOR]

A prior prediction of voltage required for the nonthermal plasma (NTP)-based diesel exhaust treatment toward removal of nitrogen oxides, i.e., NOX (sum of NO and NO2) would help the researchers to plan for the resources required for the treatment. In this present study, experimental data set of 4032 number of input-output pairs have been collected by taking flow rate (Fr), temperature (T), discharge gap (Dg), initial NOX concentration (NOXi), and NOX removal efficiency (ηr) as the 5 inputs and the voltage required for the treatment (Vrt) as the output. Then, an attempt has been made to predict Vrt for achieving particular ηr, while other input parameters have fixed values, using multivariable linear and quadratic polynomial regression techniques. The performances of these two predictive models have been assessed during training and testing. The root mean square error (RMSE) between experimental and predicted values of Vrt with both the models has been compared, where quadratic polynomial regression-based model has been found to be performing well with an RMSE of 0.85 kV for a test data of 1210 sets. This study shows that the voltage required for the treatment, to achieve a particular amount of NOX removal from the exhaust, can be predicted with very good accuracy so that the necessary arrangements for the treatment can be made by the researchers. [ABSTRACT FROM AUTHOR]