Search

With a single-switched DC-DC converter, a hybrid approach is proposed for a fast-charging (FC) dc to dc converter with the controller for an electric vehicle charging station (EVCS) that combines solar photovoltaic (PV), fuel cells (FC), and a battery energy storage system (BESS). The proposed hybrid technique integrates a radial-basis-function-neural-network (RBFNN) and a student-psychology-optimization-algorithm (SPOA); commonly known as the RBFNN-SPOA technique. The PV-FC hybrid electric vehicle systems, like controllers, electric vehicle, and the vehicle’s power source, are displayed. The proposed converter architectures have less voltage waveform changes, a low voltage stress on semiconductor power sources, and a high-voltage ratio of conversion. The proposed method is to use FC successfully at battery charge states and varied radiation levels. The FC in the controller of SPOA may change the inputs and outputs depending on the battery state of charge (SoC) and PV irradiation. Similarly, the needed input-torque for the motor is forecasted by the recalling recurrent neural network controller using several manually produced torque signals. The performance of the proposed technique is evaluated in MATLAB and is compared to ant colony optimization (ACO), crow optimization algorithm (COA), and fuzzy logic controller (FLC) approaches. According to the results, the efficiency of the proposed technique is 99.17%. [ABSTRACT FROM AUTHOR]

Summary: This article proposes a hybrid POA‐RBFNN approach for PV (photovoltaic) based grid tied intelligent controlled water pump system. The proposed method is the hybrid wrapper of Pelican Optimization Algorithm (POA) and Radial Basis Function Neural Network (RBFNN) and later it is termed as POA‐RBFNN method. On the basis of bidirectional power grids, this research presents bidirectional power flow control for PV systems. The water pump is driven through a three‐phase synchronous reluctance motor (RSM). A novel Cuk integrated Landsman converter can operate at low duty cycles to provide larger DC voltages with negligible switching losses is used. In addition, to enhance the pumping system reliability, solar panels and electric pumps can be used to their maximum potential. Despite fluctuating weather conditions, the system supplies nominal displacement all over the day and surplus energy to the grid. Furthermore, if one of the sources fails, the pump performance is unaffected. Switching losses are minimized since the Voltage Source Inverter (VSI) operates at a fundamental frequency. In all operational modes, this control delivers unique grid‐side power quality and unity power factor functioning. Here, the performance of the proposed hybrid system is implemented in the MATLAB and is compared with several existing methods. [ABSTRACT FROM AUTHOR]

With the increase in the amount of data captured during the manufacturing process, surveillance systems are the most important decision making decisions. Current technologies such as Internet of Things (IoT) can be considered a solution to provide efficient monitoring of productivity. In this study, it has suggested a real-time monitoring system that uses an IoT, big data processing and an Offshore Wind Farm (OWF) model is proposed. The Offshore Wind Farm (OWF) is an extended level invasion in modern power electronics systems, in this proposed work Doubly Fed Induction Generator (DFIG) based multi machined OWF was designed, and power stability was analyzed using Substantial Transformative Optimization Algorithm (STOA). The Voltage Source Converter (VSC) and High Voltage Direct Current (HVDC) system was combined with onshore network. The terminal voltage of onshore network was controlled through Onshore Side Converter (OSC), active and reactive power was regulated separately using VSC. The performance of the onshore network was evaluated under renewable network errors (Total Harmonics distortion and steady state error) beside with OWF. The OWF - DFIG active and reactive power was controlled smoothly with in the limit of HVDC, and the power framework security can be updated by controlling the active power of the OSC to help its terminal voltage using STOA methodology. From the voltage control mode, the electrical faults are recovered rapidly with minimum fluctuation. The dynamic simulation comes about additionally demonstrate that onshore network fault can't impact OWF behind HVDC transmission system. Because of the specialized favorable circumstance, VSC-HVDC innovation, the constancy in OWF is very much ensured against the onshore grid faults. The proposed STOA based system has validated through simulation in Matlab Simulink environment. General, 97% effectiveness, accomplished at full load condition in light of the proposed system. The results showed that the IoT system and the proposed large data processing system were sufficiently competent to monitor the manufacturing process. [ABSTRACT FROM AUTHOR]