A novel auto‐tuned adaptive frequency and adaptive step‐size incremental conductance
            <scp>MPPT</scp>
            algorithm for photovoltaic system

Mishra, Jyotismita; Das, Subhadip; Kumar, Deepak; Pattnaik, Monalisa

doi:10.1002/2050-7038.12813

Cited by 16 publications

(6 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, the direction of the disturbance depends on the increase or decrease in power. Incremental conductance (INC) [8,9] utilizes the slope of the P-V curve Eng 2023, 4 965 of the PV array characteristics to determine the MPP. This slope is equal to zero at the MPP, positive to its left and negative to its right.…”

Section: Introductionmentioning

confidence: 99%

A Novel MPPT Technique Based on Combination between the Incremental Conductance and Hysteresis Control Applied in a Standalone PV System

Ouardi

Gadari

Mokhlis

et al. 2023

Eng

View full text Add to dashboard Cite

A new Maximum Power Point Tracking (MPPT) method, consisting in combining the Incremental Conductance (INC) algorithm with the Hysteresis control, was developed and applied to a standalone photovoltaic (PV) system to generate the maximum power of the PV array. The INC allows one to search for the Maximum Power Point (MPP). The hysteresis improves the accuracy of tracking the MPP very fast even after severe changes in weather conditions and has no oscillations around the MPP. The five-level S-Packed U Cells (SPUC5) inverter is used to transform the produced DC voltage to AC voltage; it generates five-level output voltage with a small number of switches and only DC source voltage. The capacitors of the SPUC5 are controlled by the Pulse Width Modulation (PWM) in order to balance their voltages. The proposed PV system was established and trained in the MATLAB/Simulink environment under various irradiation conditions. A comparison between different MPPT methods, INC-PWM and INC-PI, was investigated in order to examine the effectiveness of the developed MPPT technique in particular, and of all the PV system components. The results of the simulation validate the effectiveness of the suggested MPPT algorithm as well as the used SPUC5 inverter.

show abstract

Section: Introductionmentioning

confidence: 99%

A Novel MPPT Technique Based on Combination between the Incremental Conductance and Hysteresis Control Applied in a Standalone PV System

Ouardi

Gadari

Mokhlis

et al. 2023

Eng

View full text Add to dashboard Cite

show abstract

“…[1][2] Traditional MPPT algorithms, such as constant voltage method, perturbation and observation method(P&O) and incremental conductivity method(INC), are simple and easy to implement, but they are greatly influenced by the environment, so it is difficult to ensure the maximum power generation efficiency of the system. [3][4][5][6] The use of intelligent algorithms makes up for the shortcomings of traditional algorithms to a great extent, including particle swarm optimization algorithm(PSO), genetic algorithm(GA), artificial neural network algorithm(ANN), grey wolf optimization algorithm(GWO),glowworm swarm optimization algorithm(GSO),fuzzy logic control algorithm(FLC), etc. When these algorithms are applied to solar controller, the performance of tracking maximum power is improved.…”

Section: Introductionmentioning

confidence: 99%

“…When these algorithms are applied to solar controller, the performance of tracking maximum power is improved. [5] When PSO is used to track the maximum power point, there are shortcomings such as long optimization time and large power fluctuation. [7] GA search has the problems of premature algorithm and large fluctuation near the maximum power point, which leads to the inability to output the maximum power accurately and stably.…”

Section: Introductionmentioning

confidence: 99%

Multi-peak MPPT Control Based on Variable Step Disturbance Observation Method and Butterfly Optimization Algorithm

2023

jeis

View full text Add to dashboard Cite

Photovoltaic power generation has attracted more and more attention in the field of new energy applications, and the maximum power point tracking technology is the critical link of the photovoltaic power generation system. In the case of partial shading, the output power curve of the photovoltaic array presents a multi-peak phenomenon, and the traditional MPPT algorithm is easy to fall into the optimal local solution when tracking the maximum power, while the traditional butterfly algorithm has slow convergence and low optimization accuracy in the tracking process. In order to reduce the loss of output power of photovoltaic system, an MPPT control method combining butterfly algorithm with adaptive inertia weight and variable step size disturbance observation method is proposed. In the butterfly algorithm, the population randomly generates the initial solution, and the crossover mutation operation is carried out on the population. The inertia weight is constantly updated with the increase of iteration times, which can reduce the oscillation amplitude in the tracking process, increase the robustness of the algorithm search, and achieve the purpose of global search. Then, the perturbation observation method with variable step size is used to accelerate the convergence speed and accuracy. The simulation results show that compared with the traditional perturbation observation method and butterfly optimization algorithm, the proposed algorithm can find the maximum power point stably, quickly, and accurately in the case of sudden illumination change, which significantly improves the performance of MPPT.

show abstract

“…To solve the MPPT tracking problem, the 3 of 26 work in [30] exercises an enhanced IC algorithm in coordination with a combined pattern search and crow search algorithm to tune neuro-fuzzy controller gains. Another study in [31] suggests a fast-tuning IC-MPPT for a PV system. The MPPT approach behavior is a combination of the effect of both the MPPT frequency interference and the step size caused by variation in the PV voltage.…”

Section: Introductionmentioning

confidence: 99%

Enhanced MPPT-Based Fractional-Order PID for PV Systems Using Aquila Optimizer

Tadj,

Chaib,

Choucha

et al. 2023

MCA

View full text Add to dashboard Cite

This paper proposes a controller to track the maximum power point (MPP) of a photovoltaic (PV) system using a fractional-order proportional integral derivative (FOPID) controller. The employed MPPT is operated based on a dp/dv feedback approach. The designed FOPID-MPPT method includes a differentiator of order (μ) and integrator of order (λ), meaning it is an extension of the conventional PID controller. FOPID has more flexibility and achieves dynamical tuning, which leads to an efficient control system. The contribution of our paper lies is optimizing FOPID-MPPT parameters using Aquila optimizer (AO). The obtained results with the proposed AO-based FOPID-MPPT are contrasted with those acquired with moth flame optimizer (MFO). The performance of our FOPID-MPPT controller with the conventional technique perturb and observe (P&O) and the classical PID controller is analyzed. In addition, a robustness test is used to assess the performance of the FOPID-MPPT controller under load variations, providing valuable insights into its practical applicability and robustness. The simulation results clearly prove the superiority and high performance of the proposed control system to track the MPP of PV systems.

show abstract

A novel auto‐tuned adaptive frequency and adaptive step‐size incremental conductance MPPT algorithm for photovoltaic system

Cited by 16 publications

References 39 publications

A Novel MPPT Technique Based on Combination between the Incremental Conductance and Hysteresis Control Applied in a Standalone PV System

A Novel MPPT Technique Based on Combination between the Incremental Conductance and Hysteresis Control Applied in a Standalone PV System

Multi-peak MPPT Control Based on Variable Step Disturbance Observation Method and Butterfly Optimization Algorithm

Enhanced MPPT-Based Fractional-Order PID for PV Systems Using Aquila Optimizer

Contact Info

Product

Resources

About