Aman Kumar

Bio: Aman Kumar is an academic researcher from VIT University. The author has contributed to research in topics: Diesel fuel & Microstrip antenna. The author has an hindex of 1, co-authored 3 publications receiving 14 citations.

Determination of TTC and application of SVM for demarcation of stability limits

Abstract: The increasing demand for electrical power necessitates the expansion of power system, which is constrained by land availability and other resources. This results in the utilization of power system upto its stability limits. The TTC for an instance gives us the load that can be further supplied by the system before it loses stability. This paper aims at computing the stability limits of the power system network employed for the computation of Total Transfer Capability (TTC) using Support Vector Machine (SVM). Computation of voltage stability (using voltage stability index method and P-Q plane method) has been considered on IEEE 30 bus system. Small signal stability limit (Eigen value approach) has been considered on an WSCC 3 machine 9 bus system for which TTC has been calculated by employing SVM. All simulations are carried out in MATLAB 8.0-R 2012 b environment.

Miniaturized chipless RFID tag using multiple microstrip resonators

Abstract: This paper proposes a new design to implement chipless RFID technology using rectangular ring shaped microstrip resonators of various structures to obtain different frequency signatures in the super high frequency range (2–3 GHz). It is observed that the prototype has minimal return loss and maximum resonance level has also been achieved which has been verified with graphical stimulated results and by numerical retrieval of parameters.

Random Forest based Fault Detection and Localization in Microgrid using Simplified Measurements

Abstract: Electrical microgrids are quite vulnerable to fault conditions because of being in close proximity to the load centres. Due to differences in operating dynamics of microgrids as compared to conventional distribution system, designing reliable protection is a major concern for protection engineers. Therefore, detection and localization of fault in microgrids has become difficult and less trustworthy using traditional overcurrent relay based protection schemes. This paper presents a new protection scheme where faulted line number and its exact location from substation is determined using Random forest algorithm. A microgrid model has been created in MATLAB/SIMULINK platform having various distributed generation sources along with the substation grid. Common types of shunt faults have been created at different locations and the root mean square value of post fault one cycle faulted voltage and current signal is collected from each of the source buses. Further various practical cases such as islanded mode, change in DG(distributed generation) penetration level, increment and decrement of loads by 50% has also been taken into consideration while creating the training data. The collected data is then used for designing a machine learning (ML) model. Three types of prediction are being made by this model i.e. (i) type of fault, (ii) line in which fault has occurred and (iii) distance of fault from substation. Two different approaches are used for model, first simply training the model on all the data set that has been collected and the second one is by filtering out the data according to type of fault and then on the filtered data the faulted data set training is done. Random forest method which is implemented using Python coding stands out to be the best algorithm for all the three problem in terms of accuracy and time of execution of model even with simplified measurement.

Online TTC Estimation Using Nonparametric Analytics Considering Wind Power Integration

Abstract: Total transfer capability (TTC) is an effective indicator to evaluate the transmission limit of the interconnected systems. However, due to the large-scale wind power integration, operation conditions of a power system may change rapidly, yielding time-varying characteristics of the TTC. As a result, the traditional time-consuming transient stability constrained TTC model is unable to assess the online transmission margin. In this paper, we propose an online measurement-based TTC estimator using the nonparametric analytics. It consists of three major components: the probabilistic data generation, the composite feature selection, and the group Lasso regression-based training scheme. Specifically, we present a probabilistic data generation approach to take into account the uncertainties of the day-ahead generation scheduling and to reduce the number of redundant or infeasible data. Then, the composite feature selection is used to reduce the dimension of the generated data and identify the features which are highly correlated with TTC. The features are determined by the maximal information coefficients and nonparametric independence screening approach. Finally, these selected features are trained by the group Lasso regression to learn the correlation between the TTC and the online measurements. Once real-time measurements are available, the TTC can be assessed immediately through the learned correlation relationship. Extensive numerical results carried out on the modified New England 39-bus test system demonstrate the feasibility of the proposed TTC estimator for online applications.

A comprehensive study on the effects of multiple injection strategies and exhaust gas recirculation on diesel engine characteristics that utilize waste high density polyethylene oil

Abstract: The present study utilized the catalytic pyrolysis method for extracting oil from waste high-density polyethylene (WHDPE) plastics. Experiments were carried out with D70H30 (diesel-70%, WHDPE oil-3...