To enhance the photovoltaic (PV) power-generation conversion, maximum power point tracking (MPPT) is the foremost constituent. This article introduces an adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO)-based hybrid MPPT method to acquire rapid and maximal PV power with zero oscillation tracking. The inverter control strategy is implemented by a space vector modulation hysteresis current controller to get quality inverter current by tracking accurate reference sine-shaped current. The ANFIS-PSO-based MPPT method has no extra sensor requirement for measurement of irradiance and temperature variables. The employed methodology delivers remarkable driving control to enhance PV potential extraction. An ANFIS-PSO-controlled Zeta converter is also modeled as an impedance matching interface with zero output harmonic agreement and kept between PV modules and load regulator power circuit to perform MPPT action. The attainment of recommended hybrid ANFIS-PSO design is equated with perturb and observe, PSO, ant colony optimization, and artificial bee colony MPPT methods for the PV system. The practical validation of the proposed grid-integrated PV system is done through MATLAB interfaced dSPACE interface and the obtained responses accurately justify the proper design of control algorithms employed with superior performance.Index Terms-adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), fuzzy logic control (FLC), maximum power point tracking (MPPT), photovoltaic (PV) system, space vector modulation hysteresis current controller (SVMHCC), Zeta converter. NOMENCLATURE I photPhotocurrent. I RSC Reversed saturating current. V Thm Thermal voltage. G S Sun insolation.

Maximum power point trackers (MPPT) are required in order to obtain optimal photovoltaic power. To achieve this task, an intelligent fuzzy particle swarm optimization (FPSO) MPPT algorithm has been proposed in this paper. Also an inverter control strategy has been gated with a ripple factor compensation-based modified space vector pulse width modulation (SVPWM) method. The proposed system performance is verified under varying sun irradiance, partial shadow, and loading conditions. For load bus voltage regulation, the buck-boost Zeta converter is selected due to least ripple voltage output. The experimental responses verify the efficiency and improved system performance, which is realized through a MATLAB/Simulink interfaced dSPACE DS1104 realtime board. The proposed MPPT and inverter current controller provides high tracking efficiency and anti-islanding protection with superior dynamic control of the system performance by injecting sinusoidal inverter current to the utility grid. The novelty of this paper is experimental implementation and verification of FPSObased hybrid MPPT as well as modified SVPWM inverter control has neither been discussed nor implemented before using dSPACE platform by the author's best review.

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.

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.

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