This research work explains the practical realization of hybrid solar wind-based standalone power system with maximum power point tracker (MPPT) to produce electrical power in rural places (residential applications). The wind inspired Ant Colony Optimization (ACO)-based MPPT algorithm is employed for the purpose of fast and accurate tracking power from wind energy system. Fuzzy Logic Control (FLC) inverter controlling strategy is adopted in this presented work compared to classical proportional-integral (PI) control. Moreover, single Cuk converter is operated as impedance power adapter to execute MPPT functioning. Here, ACO-based MPPT has been implemented with no voltage and current extra circuit requirement compared to existing evolutionary algorithms single cuk converter is employed to improve conversion efficiency of converter by maximizing power stages. DC-link voltage can be regulated by placing Cuk converter Permanent Magnet Synchronous Generator (PMSG) linked rectifier and inverter. The proposed MPPT method is responsible for rapid battery charging and gives power dispersion of battery for hybrid PV-Wind system. ACO-based MPPT provides seven times faster convergence compared to the particle swarm optimization (PSO) algorithm for achievement of maximum power point (MPP) and tracking efficiency. Satisfactory practical results have been realized using the dSPACE (DS1104) platform that justify the superiority of proposed algorithms designed under various operating situations.

Vehicular ad-hoc network (VANET) has been gaining importance due to the fast growing technology as well as its requirements in intelligent transportation systems (ITS) and vehicular social network (VSN). VANET facilitates vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication and improves the ride quality with value added services. The number of connected vehicles is expected to grow to a huge number with enormous exchange of safety and non-safety messages which are susceptible to security and privacy threat. To ensure secured communication, VANET must implement an authentication protocol to resist the attack and preserve the privacy. In this paper, a detailed discussion on the taxonomy for authentication schemes in VANET has been presented. The authentication schemes have been compared with security, privacy and scalability requirement. The use of recent technologies such as 5G, 5G-SDN, and Blockchain to design authentication schemes with low cost, and low communication, computational overhead has been discussed. Finally, the paper concludes with open challenges in VANET authentication. This paper is expected to open new avenues for researchers working in the domain of VANETs.

This article presents a single-stage hybrid photovoltaic (PV)-fuel cell (FC)-based grid-integrated system with Lyapunov function-based controller design to obtain optimal power extraction from hybrid renewable sources without maximum power point tracking (MPPT) application. The proposed Lyapunov controller performs MPPT function, improves power quality, and forces inverter to inject sinusoidal current to the utility grid. In this proposed approach, the higher switching frequency has been reduced by employing LCL filter inclusion compared to the two-stage hybrid power system. This proposed single-stage system has low cost and improved power quality at the point of common coupling and employed controller injects stable power to the utility grid. In this article, a hybrid overall distributed-particle swarm optimization-based MPPT is employed with FCs and integrated CUK converter. The effectiveness of the employed Lyapunov function-based controller has been tested with dSPACE (DS1104) real-time platform for single-stage hybrid grid-connected power system under varying operating conditions that have high efficiency, reduced harmonic distortion in grid current with the simpler employed power converter. Experimental responses confirm the effectiveness of the proposed controller, which transfers the hybrid power from PV and FC to the utility grid through a single stage.

In this research paper, a hybrid Artificial Neural Network (ANN)-Fuzzy Logic Control (FLC) tuned Flower Pollination Algorithm (FPA) as a Maximum Power Point Tracker (MPPT) is employed to amend root mean square error (RMSE) of photovoltaic (PV) modeling. Moreover, Gaussian membership functions have been considered for fuzzy controller design. This paper interprets the Luo converter occupied brushless DC motor (BLDC)-directed PV water pump application. Experimental responses certify the effectiveness of the suggested motor-pump system supporting diverse operating states. The Luo converter, a newly developed DC-DC converter, has high power density, better voltage gain transfer and superior output waveform and can track optimal power from PV modules. For BLDC speed control there is no extra circuitry, and phase current sensors are enforced for this scheme. The most recent attempt using adaptive neuro-fuzzy inference system (ANFIS)-FPA-operated BLDC directed PV pump with advanced Luo converter, has not been formerly conferred.

This research work deals with a hybrid control system based integrated Cuk converter fed brushless DC motor (BLDCM) for power factor correction. In this work, moth-flame optimization (MFO) and a fuzzy logic controller (FLC) have been combined and a moth-flame fuzzy logic controller (MFOFLC) has been proposed. Firstly, the BLDC motor modeling is composed with the power factor correction (PFC) based integrated Cuk converter and BLDC speed is regulated using variable DC-Link inverter voltage which results in a low switching operation with fewer switched losses. Here, with the use of a switched inductor, the task and execution of the proposed converter is redesigned. The DBR (diode bridge rectifier) trailed by a proposed PFC based integrated Cuk converter operates in discontinuous inductor conduction mode (DICM) for achievement of better power factor. MFO is exhibited for gathering of a dataset from the input voltage signal. At that point, separated datasets are sent to the FLC to improve the updating function and minimization of torque ripple. However, our main objective is to assess adequacy of the proposed method, but the power factor broke down. The execution of the proposed control methodology is executed in the MATLAB/Simulink working platform and the display is assessed with the existing techniques.

This research work deals with the modeling and control of a hybrid photovoltaic (PV)-Wind micro-grid using Quasi Z-source inverter (QZsi). This inverter has major benefits as it provides better buck/boost characteristics, can regulate the phase angle output, has less harmonic contents, does not require the filter and has high power performance characteristics over the conventional inverter. A single ended primary inductance converter (SEPIC) module used as DC-DC switched power apparatus is employed for maximum power point tracking (MPPT) functions which provide high voltage gain throughout the process. Moreover, a modified power ratio variable step (MPRVS) based perturb & observe (P&O) method has been proposed, as part of the PV MPPT action, which forces the operating point close to the maximum power point (MPP). The proposed controller effectively correlates with the hybrid PV, Wind and battery system and provides integration of distributed generation (DG) with loads under varying operating conditions. The proposed standalone micro grid system is applicable specifically in rural places. The dSPACE real-time hardware platform has been employed to test the proposed micro grid system under varying wind speed, solar irradiation, load cutting and removing conditions etc. The experimental results based on a real-time digital platform, under dynamic conditions, justify the performance of a hybrid PV-Wind micro-grid with Quasi Z-Source inverter topology.