The silicon carbide (SiC) MOSFET is characterized by high operating voltage, temperature, switching frequency and efficiency which enables a converter to achieve high power density. However, at high switching frequency, the crosstalk phenomenon occurs when the gate voltage spike introduced by high dv/dt and voltage ringing forces false turn-on of SiC MOSFET which causes a crow-bar current thereby increasing switching losses. In order to increase the immunity against the crosstalk phenomenon in a half-bridge configuration, this paper presents a gate driver for SiC MOSFET capable of generating the negative turn-off voltage without using a negative power supply. In addition, the effect of parasitic inductances on the switching response is analyzed and an RC snubber is designed using high-frequency based circuit reduction technique to dampen the switching ringing. The performance of the proposed gate driver and the designed RC snubber is validated using simulation and experiment at the 1 MHz switching frequency. The results show that the proposed gate driver with RC snubber eliminates crosstalk by maintaining any spurious gate spike below the gate threshold voltage.

With the evolution of the More Electric Aircraft (MEA) concept, high pulse converters have gained the attention of researchers due to their higher power quality. Among the high pulse converters, 18-pulse autotransformer rectifier unit (ATRU) offers better power quality level with small size, weight and medium complexity. The conventional topologies of autotransformers that require the use of extra elements such as Inter Phase Transformers (IPT) or Zero Sequence Blocking Transformers (ZSBT), adding to the complexity, weight and size of the overall system, are not considered in the analysis. For 18-pulse rectification, only those topologies of autotransformers which have the intrinsic current harmonic cancellation capabilities are presented here for comparison. These topologies offer current harmonic levels within limits specified by IEEE 519 with reduced weight and size as compared to the conventional multi-pulse converters. A comparison of different differential delta/fork configured 18-pulse autotransformer rectifier units is presented so as to come up with the best among available topologies with respect to weight, size and power quality. Experimental prototypes of each topology were designed and their results are displayed along with the simulation results for comparison.

With the increase in energy demand, renewable energy has become a need of almost every country. Solar Energy is an important constituent of it and contributes a large portion in it. Forecasting the output power of a Photovoltaic (PV) system has always been a challenging problem in the power sector from the last few decades. The output power of a PV system depends upon several environmental factors such as irradiance (G), temperature (T), humidity (H), wind speed (W), provided the tilt angle is kept constant, among which the vital role is played by irradiance. Researchers have utilized several techniques to accurately predict the output power of PV module but every method has various pros and cons. In this paper, an experimental measurement dataset of 28296 samples with all the environmental parameters mentioned above are taken as the inputs and power as its output, of a Poly-Silicon (Poly-Si) PV module, is trained through Artificial Neural Network (ANN), to predict the output power accurately. The proposed ANN contains a layer size of 15 and training algorithm used is Levenberg-Marquardt. A detailed analysis and preprocessing of the data is carried out through Pearson’s correlation method prior to training. The hyperparameters of Neural Network tuning are selected through heuristic method. The data division is done randomly with 70 % dataset used for training, 15 % dataset used for each validation and testing. The statistical results show that ANN accurately predicted the power output of PV module. The regression analysis values acquired are 98 % and the MSE of all the three phases is 0.0604.

A system identification of a two-wheeled robot (TWR) using a data-driven approach from its fundamental nonlinear kinematics is investigated. The fundamental model of the TWR is implemented in a Simulink environment and tested at various input/output operating conditions. The testing outcome of TWR’s fundamental dynamics generated 12 datasets. These datasets are used for system identification using simple autoregressive exogenous (ARX) and non-linear auto-regressive exogenous (NLARX) models. Initially the ARX structure is heuristically selected and estimated through a single operating condition. We conclude that the single ARX model does not satisfy TWR dynamics for all datasets in term of fitness. However, NLARX fitted the 12 estimated datasets and 2 validation datasets using sigmoid nonlinearity. The obtained results are compared with TWR’s fundamental dynamics and predicted outputs of the NLARX showed more than 98% accuracy at various operating conditions.

Efficient coordination of directional overcurrent relays (DOCRs) is vital for maintaining the stability and reliability of electrical power systems (EPSs). The task of optimizing DOCR coordination in complex power networks is modeled as an optimization problem. This study aims to enhance the performance of protection systems by minimizing the cumulative operating time of DOCRs. This is achieved by effectively synchronizing primary and backup relays while ensuring that coordination time intervals (CTIs) remain within predefined limits (0.2 to 0.5 s). A novel optimization strategy, the fractional-order derivative war optimizer (FODWO), is proposed to address this challenge. This innovative approach integrates the principles of fractional calculus (FC) into the conventional war optimization (WO) algorithm, significantly improving its optimization properties. The incorporation of fractional-order derivatives (FODs) enhances the algorithm’s ability to navigate complex optimization landscapes, avoiding local minima and achieving globally optimal solutions more efficiently. This leads to the reduced cumulative operating time of DOCRs and improved reliability of the protection system. The FODWO method was rigorously tested on standard EPSs, including IEEE three, eight, and fifteen bus systems, as well as on eleven benchmark optimization functions, encompassing unimodal and multimodal problems. The comparative analysis demonstrates that incorporating fractional-order derivatives (FODs) into the WO enhances its efficiency, enabling it to achieve globally optimal solutions and reduce the cumulative operating time of DOCRs by 3%, 6%, and 3% in the case of a three, eight, and fifteen bus system, respectively, compared to the traditional WO algorithm. To validate the effectiveness of FODWO, comprehensive statistical analyses were conducted, including box plots, quantile–quantile (QQ) plots, the empirical cumulative distribution function (ECDF), and minimal fitness evolution across simulations. These analyses confirm the robustness, reliability, and consistency of the FODWO approach. Comparative evaluations reveal that FODWO outperforms other state-of-the-art nature-inspired algorithms and traditional optimization methods, making it a highly effective tool for DOCR coordination in EPSs.

In modern distributed energy systems (DES), focus is shifting from the conventional centralized approach towards distributed architectures. However, modeling and analysis of these systems is more complex, as it involves the interface of multiple energy sources with many different type of loads through power electronics converters. The integration of power electronics converters allows distributed renewable energy sources to become part of modern electronics power distribution systems (EPDS). It will also facilitate the ongoing research towards DC-based DES which is mostly composed of commercial DC-DC converters whose internal structure and parameters are unknown. For the system level analysis, the behavioral modeling technique is the only choice. Since most power electronics converters are non-linear systems and linear models can’t model their dynamics to a desired level of accuracy, hence non-linear modeling is required for accurate modeling. The non-linear modeling approach presented here aims to develop behavioral models that can predict the response of the system over the entire operating range. In this work, either a lookup table or a polytopic structure-based modeling technique is used. The technique is further applied to cascade and parallel connected converters, being two DES scenarios. First the procedure is verified via application to switching models in a simulation and then validated for commercial converters via experiments. The results show that the developed behavioral models accurately predict both the transient and steady state response.