An improved MVO assisted global MPPT algorithm for partially shaded PV system

Chauhan, Urvashi; Singh, Vijander; Kumar, Bhavnesh; Rani, Asha

doi:10.3233/jifs-179749

Cited by 14 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other algorithms such as GA and ACO are also used, but they are mainly used as the optimizer of traditional MPPT; this method is often called hybrid MPPT. Also, in recent years, evolutionary algorithms such as multiverse optimization (MVO) (46) , improved multi-universe optimization (IMVO) (47) , particle swarm optimization (PSO) (48,49) , and grey wolf optimization (GWO) (50) have become good choices for developing MPPT controllers. The metaheuristic algorithm has a fast convergence speed, reduces steady-state oscillation, and is easy to track the maximum global power.…”

Section: Related Workmentioning

confidence: 99%

Comparative Study of Solar PV System Performance under Partial Shaded Condition Utilizing Different Control Approaches

Al-Wesabi¹,

Fang²,

Ameur³

et al. 2021

IJST

View full text Add to dashboard Cite

Objectives: To improve the reduction of photovoltaic system's power output under various resistance load. Additionally, partial shaded conditions (PSCs) lead to several peaks on photovoltaic (PV) curves, which decrease conventional techniques' efficiency and in these (PSCs), standard equations might not be implemented entirely, therefore, the mathematical model of PV array is compulsory to modify and re-establish as well as it is compulsory to apply some methods based on artificial intelligence to develop the performance of traditional techniques. Methods: This work has modified and re-established the mathematical model of PV array under (PSCs) which are recognized and verified using MATLAB/Simulink environment. Also, heuristic algorithms (Cuckoo Search Algorithm (CSA) and Modified Particle Swarm Optimization (MPSO)) have been suggested and applied with PV system to promote output power under various resistance load, varying weather conditions and (PSCs). Moreover, these suggested algorithms can improve the dynamic response and steady-state PV systems' performance simultaneously and effectively comparing to the Modified Perturb and Observe (MP&O) and Artificial Neural Network (ANN) methods. Findings: The proposed methods are examined under various resistance load, several scenarios for (PSCs) and non-uniform irradiation levels to investigate its effectiveness. The results ensure that proposed tracker based on Cuckoo Search Algorithm (CSA) can distinguish between the global and local maximum peaks of PV system effectively with efficiency of 99% comparing to other MPPT approaches. So, all approaches mentioned above are implemented to improve the output power of PV system in Yemen. Novelty: Modified and re-established the mathematical model of PV array under (PSCs) and also, proposed a heuristic algorithms (CSA) and (MPSO)

show abstract

Section: Related Workmentioning

confidence: 99%

Comparative Study of Solar PV System Performance under Partial Shaded Condition Utilizing Different Control Approaches

Al-Wesabi¹,

Fang²,

Ameur³

et al. 2021

IJST

View full text Add to dashboard Cite

show abstract

“…Other algorithms such as GA and ACO are also used, but they are mainly used as the optimizer of traditional MPPT; this method is often called hybrid MPPT. Also, in recent years, evolutionary algorithms such as multiverse optimization (MVO) [46], improved multi-universe optimization (IMVO) [47], particle swarm optimization (PSO) [48,49], and grey wolf optimization (GWO) [50] have become good choices for developing MPPT controllers. The metaheuristic algorithm has a fast convergence speed, reduces steady-state oscillation, and is easy to track the maximum global power.…”

Section: Related Workmentioning

confidence: 99%

Comparative Study of Solar PV System Performance under Partial Shaded Condition Utilizing Different Control Approaches

Al-Wesabi¹,

Fang²,

Ameur³

et al. 2021

Preprint

View full text Add to dashboard Cite

The solar system characteristics are affected due to few obscure terms, causing a reduction of photovoltaic system's power output. Also, partial shaded conditions (PSCs) lead to several peaks on photovoltaic (PV) curves, which decrease conventional techniques' efficiency Also, in these (PSCs), standard equations might not be implemented entirely. Therefore, this study aims, first to modify and re-establish the mathematical model of PV array under (PSCs). Second, heuristic algorithms (Cuckoo Search Algorithm (CSA) and Modified Particle Swarm Optimization (MPSO)) have been suggested and applied with PV system to promote output power under varying weather conditions and PSCs. Moreover, these algorithms can improve the dynamic response and steady-state PV systems' performance simultaneously and effectively. Later on, the following approaches, modified (MP&O) and (ANN), are also proposed to extract the photovoltaic system's maximum power. Then, MPPT problem is modeled and optimized on MATLAB environment where it is reliable to connect the programmable optimizer with Simulink of photovoltaic cell used to validate results. Finally, proposed methods are examined under several scenarios for (PSCs) to investigate its effectiveness. The results ensure that proposed tracker based on CSA can distinguish between the global and local maximum peaks of PV system effectively comparing to others MPPT approaches.

show abstract

“…A multitude of MPPT control techniques, which make use of swarm optimization, are proposed as a means of enhancing the efficiency of solar energy utilization in PV systems when PSC is employed [8,9]. Among the most commonly utilized intelligent MPPT methodologies are PSO [10], GWO [11], the multiverse algorithm [12], the cuckoo search algorithm [13], black widow optimization [14], and the whale optimization algorithm [15], etc. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Research on Photovoltaic Maximum Power Point Tracking Control Based on Improved Tuna Swarm Algorithm and Adaptive Perturbation Observation Method

Li,

He,

2024

Energies

View full text Add to dashboard Cite

In situations where photovoltaic (PV) systems are exposed to varying light intensities, the conventional maximum power point tracking (MPPT) control algorithm may become trapped in a local optimal state. In order to address this issue, a two-step MPPT control strategy is suggested utilizing an improved tuna swarm optimization (ITSO) algorithm along with an adaptive perturbation and observation (AP&O) technique. For the sake of enhancing population diversity, the ITSO algorithm is initialized by the SPM chaos mapping population. In addition, it also uses the parameters of the spiral feeding strategy of nonlinear processing and the Levy flight strategy adjustment of the weight coefficient to enhance global search ability. In the two-stage MPPT algorithm, the ITSO is applied first to track the vicinity of the global maximum power point (MPP), and then it switches to the AP&O method. The AP&O method’s exceptional local search capability enables the global MPP to be tracked with remarkable speed and precision. To confirm the effectiveness of the suggested algorithm, it is evaluated against fuzzy logic control (FLC), standard tuna swarm optimization (TSO), grey wolf optimization (GWO), particle swarm optimization (PSO), and AP&O. Finally, the proposed MPPT strategy is verified by the MATLAB R2022b and RT-LAB experimental platform. The findings indicate that the suggested method exhibits improved precision and velocity in tracking, efficiently following the global MPP under different shading conditions.

show abstract

An improved MVO assisted global MPPT algorithm for partially shaded PV system

Cited by 14 publications

References 25 publications

Comparative Study of Solar PV System Performance under Partial Shaded Condition Utilizing Different Control Approaches

Comparative Study of Solar PV System Performance under Partial Shaded Condition Utilizing Different Control Approaches

Comparative Study of Solar PV System Performance under Partial Shaded Condition Utilizing Different Control Approaches

Research on Photovoltaic Maximum Power Point Tracking Control Based on Improved Tuna Swarm Algorithm and Adaptive Perturbation Observation Method

Contact Info

Product

Resources

About