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To generate electricity, solar photovoltaic (PV) systems are among the best, most eco-friendly and most cost-effective solutions available. Extraction of maximum possible electricity from the solar PV system is complicated by a number of factors brought on by the ever-changing weather conditions under which it must operate. Many conventional and evolutionary algorithm-based maximum power point tracking (MPPT) techniques have the limitation of not being able to extract maximum power under partial shade and rapidly varying irradiance. Hence, the purpose of this paper is to propose a novel hybrid slime mould assisted with perturb and observe (P&O) global MPPT technique (HSMO) for the hybrid bridge link-honey comb (BL-HC) configured PV system to enhance the better maximum power during dynamic and steady state operations within less time.

In this method, a hybridization of two algorithms is proposed to track the true with faster convergence under PSCs. Initially, the slime mould optimization (SMO) algorithm is initiated for exploration of optimum duty cycles and later P&O algorithm is initiated for exploitation of global duty cycle for the DC–DC converter to operate at GMPP and for fast convergence.

The effectiveness of the proposed HSMO MPPT is compared with adaptive coefficient particle swarm optimization (ACPSO), flower pollination algorithm and SMO MPPT techniques in terms of tracked GMPP, convergence time/tracking speed and efficacy under six complex partial shading conditions. From the results, it is noticed that the proposed algorithm tracks the true GMPP under most of the shading conditions with less tracking time when compared to other MPPT techniques.

This paper proposes a novel hybrid slime mould assisted with perturb and observe (P&O) global MPPT technique (HSMO) for the hybrid BL-HC configured PV system enhance the better maximum power under partial shading conditions (PSCs). This method operated in two stages as SMO for exploration and P&O for exploitation for faster convergence and to track true GMPP under PSCs. The proposed approach largely improves the performance of the MPP tracking of the PV systems. Initially, the proposed MPPT technique is simulated in MATLAB/Simulink environment. Furthermore, an experimental setup has been designed and implemented. Simulation results obtained are validated through experimental results which prove the viability of the proposed technique for an efficient green energy solution.

The solar photovoltaic (PV) system is one of the outstanding, clean and green energy options available for electrical power generation. The varying meteorological operating conditions impose various challenges in extracting maximum available power from the solar PV system. The drawbacks of conventional and evolutionary algorithms-based maximum power point tracking (MPPT) approaches are its inability to extract maximum power during partial shading conditions and quickly changing irradiations. Hence, the purpose of this paper is to propose a modified elephant herding optimization (MEHO) based MPPT approach to track global maximum power point (GMPP) proficiently during dynamic and steady state operations within less time.

A MEHO-based MPPT approach is proposed in this paper by incorporating Gaussian mutation (GM) in the original elephant herding optimization (EHO) to enhance the optimizing capability of determining the optimal value of DC–DC converter’s duty cycle (D) to operate at GMPP.

The effectiveness of the proposed system is compared with EHO based MPPT, Firefly Algorithm (FA) MPPT and particle swarm optimization (PSO) MPPT during uniform irradiation condition (UIC) and partial shading situation (PSS) using simulation results. An experimental setup has been designed and implemented. Simulation results obtained are validated through experimental results which prove the viability of the proposed technique for an efficient green energy solution.

With the proposed MEHO MPPT, it has been noted that the settling period is lowered by 3.1 times in comparison of FA MPPT, 1.86 times when compared to PSO based MPPT and 1.29 times when compared to EHO based MPPT with augmented efficiency of 99.27%.

The purpose of this paper is to propose a novel maximum power point track (MPPT) controller for a type of solar quad-copter to solve the problem of tracking the maximum power point (MPP) when it works in nonuniform environment conditions.

The influence of uniform and nonuniform illumination and different temperatures results in the output characteristics of the solar array arising multiple local MPPs. To track the global MPP of the solar array on the designed solar quadcopter, a type of MPPT controller based on an improved pigeon-inspired optimization (PIO) algorithm is proposed.

A novel type of MPPT controller based on extended search PIO (ESPIO) algorithm, called ESPIO–MPPT controller, is introduced emphatically, which is used to extend the solar quadcopter’s flight time. The simulation experiments show that the ESPIO–MPPT controller can find the global MPP (GMPP) with smaller amplitudes of oscillation and less time cost.

The proposed solar quadcopter with ESPIO–MPPT controller has satisfactory flight performance which can greatly broaden its mission scope.

A type of efficient MPPT algorithm based on ESPIO is proposed for GMPP tracking of solar quadcopters in nonuniform environment conditions.

Partial shading causes significant power decreases in the PV systems. The purpose of this paper is to address this problem, connectivity regulation is designed to reduce partial shading problems.

In this approach, the partial shading was estimated and dispersed evenly on the whole array by global shade dispersion technique (GSD). The grey wolf algorithm was implemented for the interconnection of arrays by an efficient switching matrix.

After the implementation of the GSD technique using a grey wolf algorithm, the performance under different shading conditions was analyzed using the MatLab simulation tool. The results were compared with total cross-tied (TCT), Su Do Ku and the proposed method of reconfiguration, where the proposed method improves the maximum power of the PV system appropriately.

This methodology uses any size of PV systems.

Replacement of conventional energy systems with renewable energy systems such as solar helps the environment clean and green.

The GSD interconnection scheme using the grey wolf optimization algorithm has proved an improved output performance compared with the existing TCT and Sudoku based reconfiguration techniques. By comparing with existing techniques in literature, the proposed method is more advantageous for reducing mismatch losses between the modules of any size of the PV array with less operating time.

Increased investment of a photovoltaic (PV) array makes it essential for the client to attain better results from the PV system. The nonlinearity of the PV array and the revolution and rotation of the earth require the appliance of maximum power point tracking (MPPT) to the system. Accordingly, grid connected PV systems have turn out to be renowned, because they do not require battery back-ups to accomplish MPPT. Stand-alone systems could also attain MPPT; however, they require appropriate battery back-ups for this function.

This survey intends to formulate a review on the PV-based microgrid (MG) systems. Here, the literature analyses on diverse techniques associated with PV-based MG systems. It reviews 65 research papers and states the significant analysis. Initially, the analysis depicts various controllers that are contributed in different papers. Subsequently, the analysis also focuses on various features such as PV capacity and inverter topology, and it also analyses the renewable grid source that are exploited in each paper. Furthermore, this paper provides the detailed study regarding the chronological review and performance achievements in each contribution. Finally, it extends the various research issues which can be useful for the researchers to accomplish further research on PV-based MG systems.

This paper has presented a detailed review on PV-based MG systems that were enumerated in the above sections. Here, various controllers along with their better achievements were analyzed and described. From the review, it was known that several PV-based MG systems were really at the point for enabling better power output and conversion efficiency. In conclusion, this paper reviewed about 65 research papers and declared the significant analysis. Initially, the analysis also focused on various controller classifications in PV-based MG systems that were reviewed in this paper. Subsequently, the analysis also focused on various features, such as PV capacity and inverter topology. The analysis also reviewed the performance achievements and renewable gird source that were exploited in PV-based MG systems. At last, this paper has presented various research issues which can be useful for the researchers to accomplish further research on the features of PV-based MG systems.

This paper presents a brief analysis of PV-based MG systems. This is the first work that uses PV-based MG systems for better regulation of MPPT.

By means of the massive environmental and financial reimbursements, wind turbine (WT) has turned out to be a satisfactory substitute for the production of electricity by nuclear or fossil power plants. Numerous research studies are nowadays concerning the scheme to develop the performance of the WT into a doubly fed induction generator-low voltage ride-through (DFIG-LVRT) system, with utmost gain and flexibility. To overcome the nonlinear characteristics of WT, a photovoltaic (PV) array is included along with the WT to enhance the system’s performance.

This paper intends to simulate the control system (CS) for the DFIG-LVRT system with PV array operated by the MPPT algorithm and the WT that plays a major role in the simulation of controllers to rectify the error signals. This paper implements a novel method called self-adaptive whale with fuzzified error (SWFE) design to simulate the optimized CS. In addition, it distinguishes the SWFE-based LVRT system with standard LVRT system and the system with minimum and maximum constant gain.

Through the performance analysis, the value of gain with respect to the number of iterations, it was noted that at 20th iteration, the implemented method was 45.23% better than genetic algorithm (GA), 50% better than particle swarm optimization (PSO), 2.3% better than ant bee colony (ABC) and 28.5% better than gray wolf optimization (GWO) techniques. The investigational analysis has authenticated that the implemented SWFE-dependent CS was effectual for DFIG-LVRT, when distinguished with the aforementioned techniques.

This paper presents a technique for simulating the CS for DFIG-LVRT system using the SWFE algorithm. This is the first work that utilizes SWFE-based optimization for simulating the CS for the DFIG-LVRT system with PV array and WT.

Silicon photovoltaics technology has drawbacks of high cost and power conversion efficiency. In order to extract the maximum output power of the module, maximum power point (MPP) is used by implying the nonlinear behavior of I-V characteristics. Different techniques are used regarding maximum power point tracking (MPPT). The paper aims to review the techniques of MPPT used in PV systems and review the comparison between Perturb and Observe (P&O) method and incremental conductance (IC) method that are used to track the maximum power and gives a comparative review of all those techniques.

A study of MPPT techniques for photovoltaic (PV) systems is presented. Matlab Simulink is used to find the MPP using P&O simulation along with IC simulation at a steady temperature and irradiance.

MATLAB simulations are used to implement the P&O method and IC method, which includes a PV cell connected to an MPPT-controlled boost converter. The simulation results demonstrate the accuracy of the PV model as well as the functional value of the algorithms, which has improved tracking efficiency and dynamic characteristics. P&O solution gave 94% performance when configured. P&O controller has a better time response process. As compared to the P&O method of tracking, the incremental conductance response rate was significantly slower.

In PV systems, MPPT techniques are used to optimize the PV array output power by continuously tracking the MPP under a variety of operating conditions, including cell temperature and irradiation level.

Switching power converters for photovoltaic (PV) applications with high gain are rapidly expanding. To obtain better voltage gain, low switch stress, low ripple and cost-effective converters, researchers are developing several topologies.

It was decided to use the particle swarm optimization approach for this system in order to compute the precise PI controller gain parameters under steady state and dynamic changing circumstances. A high-gain q- ZS boost converter is used as an intermittent converter between a PV and brushless direct current (BLDC) motor to attain maximum power point tracking, which also reduces the torque ripples. A MATLAB/Simulink environment has been used to build and test the positive output quadratic boost high gain converters (PQBHGC)-1, PQBHGC-8, PQBHGC-4 and PQBHGC-3 topologies to analyse their effectiveness in PV-driven BLDC motor applications. The simulation results show that the PQBHGC-3 topology is effective in comparison with other HG cell DC–DC converters in terms of efficiency, reduced ripples, etc. which is most suitable for PV-driven BLDC applications.

The simulation results have showed that the PQBHGC-3 gives better performance with minimum voltage ripple of 2V and current ripple of 0.4A which eventually reduces the ripples in the torque in a BLDC motor. Also, the efficiency for the suggested PQBHGC-3 for PV-based BLDC applications is the best with 99%.

This study is the first of its kind comparing the different topologies of PQBHGC-1, PQBHGC-8, PQBHGC-4 and PQBHGC-3 topologies to analyse their effectiveness in PV-driven BLDC motor applications. This study suggests that the PQBHGC-3 topology is most suitable in PV-driven BLDC applications.