Search

Integrating solar photovoltaic (PV) systems into the modern power grid introduces a variety of new problems. The accurate modelling of PV is required to strengthen the system characteristics in simulation environments. Modelling such PV systems is reflected by a nonlinear I–V characteristic curve behaviour with numerous unknown parameters because there is insufficient data in the cells’ datasheet. As a result, it is always a priority to identify these unknown parameters. To extract features of solar modules and build high-accuracy models for modelling, control, and optimization of PV systems, current–voltage data is required. A hybrid evolutionary algorithm is proposed in this paper for precise and effective parameter estimation from experimental data of various PV models. The proposed algorithm is named as hybrid flower grey differential (HFGD) algorithm and is based on the hybridization of flower pollination algorithm (FPA), grey wolf optimizer (GWO), and differential evolution (DE) algorithm. For performance evaluation, CEC 2019 benchmark data set is used. To increase the accuracy of the output solutions, we also combined the Newton–Raphson approach with the proposed algorithm. Four PV cells/modules with diverse characteristics, including RTC France Single Diode Model (SDM), RTC France Double DM (DDM), Amorphous Silicon aSi:H, and PVM 752 GaAs Thin-Film, are used to validate the effectiveness as well as the feasibility of the proposed algorithm. The parameter results obtained through the utilization of HFGD algorithm have been compared with other evolutionary algorithms through aspects of precision, reliability, and convergence. Based on the outcomes of the comparison, it has been seen that the HFGD algorithm obtained the lowest root-mean-square error (RMSE) value. Friedman’s rank and Wilcoxon test are carried out for the statistical analysis of the proposed work. The I–V and P–V characteristics are drawn along with the box plot for different PV cells/modules. Statistical and experimental results show the superiority of the proposed algorithm with respect to its counterpart.

Electric vehicles (EVs) are widespread, and their usage is increasing as a result of air pollution and rising fuel costs. EVs are quickly gaining popularity as a green means of transportation. By 2030, most cars will probably be battery-powered EVs. However, the development of EV power transmission is packed with important challenges and is an active topic of research. In EVs, the battery serves to store electrical energy. The DC-DC converter provides a direct current (DC) link between the battery and the inverter. A motor provides the transmission for the vehicle’s motion. Hence, this state-of-the-art provides exhaustive information about battery management systems (BMS), power electronics converters, and motors. Lithium-ion batteries are more efficient for EV applications, and boost converters and full bridge converters are commonly used in EVs. EVs use permanent magnet synchronous motors (PMSM) and induction motors (IM). The renewable energy-based charging station and the fast charging specifications are also clearly addressed for EV applications.

Using multiple input power sources increases the reliability of electric vehicles compared to a single source. However, the inclusion of other sources exhibits complexity in the controller system, such as computing time, program difficulty, and switching speed to connect or disconnect the input power to load. To ensure optimal performance and avoid overloading issues, the EV system needs sophisticated control. This work introduces a machine-learning-based controller using an artificial neural network to solve these problems. This paper describes the detailed power management control methodology using multiple sources like solar PV, fuel cells, and batteries. Novel control with an instantaneous reference current scheme is used to manage the input power sources to satisfy the power demand of electric vehicles. The proposed work executes the power split-up operation with standard and actual drive cycles and maximum power point tracking for PV panels using MATLAB Simulink. Finally, power management with a machine learning technique is implemented in an experimental analysis with the LabVIEW software, and an FPGA controller is used to control a 48 V, 1 kW permanent-magnet synchronous machine. [ABSTRACT FROM AUTHOR]

Pulse width modulation (PWM) methods are used to control the switching sequence operations of conventional multilevel inverters (MLIs) and reduced switch multilevel inverters (RSMLIs). Many researchers proposed various RSMLIs with their switching sequence operation and PWM control techniques. However, the switching operations of RSMLIs are not similar to conventional MLIs, which are a major problem of switch control. Logical equations are proposed for the operation of RSMLIs with the multi-carrier PWM methods like alternative phase opposition disposition (APOD), phase opposition disposition (POD), and phase disposition (PD). To operate the individual switch in symmetrical and asymmetrical RSMLI, logical operators are used to produce required pulse sequence from the sequence of PWM method and their analysis is not present in previous works. The proposed methodology can be applied to PV systems for efficient operation. The proposed methodology and binary representation of PWM method are analyzed on various RSMLIs for seven-level output voltage to operate each individual switch. Control of individual switching sequence and the operation of RSMLIs are simulated using MATLAB/Simulink. THD comparison is presented for RSMLI, DCL MLI, and SCSD MLI with various PWM methods.