Certain investigations of ANFIS assisted CPHO algorithm tuned MPPT controller for PV arrays under partial shading conditions

Pachaivannan, Nammalvar; Ramkumar, S.; Padmanaban, Meganathan; Subramanian, Annapoorani

doi:10.1007/s12652-020-02738-w

Cited by 13 publications

(7 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the chick is true, the algorithm chooses the duty cycle value that located between the previous two highest stored power values 𝑑𝑐(𝑃 𝑚𝑎𝑥1 ), 𝑎𝑛𝑑 𝑑𝑐(𝑃 𝑚𝑎𝑥2 ) respectively. That can be achieved by using equation (7).…”

Section: The Proposed Improved Pso (Ipso) Controller Performancementioning

confidence: 99%

“…These controllers such as Fractional Short Circuit Current (FSC), Fractional Open-Circuit voltage (FOC), Incremental Conductance method (IC), and Perturb & Observe method (P&O) [6]. trollers [7]. The MPPT controllers that are based on AI algorithms operate effectively than the traditional ones, but it needed extra sensors to sense the radiation and temperature variation, also it requires significant memory to store a great amount of data and requires previous knowledge about the PV system.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fast Convergence Modified Particle Swarm Optimization Al-gorithm to Follow up String PV Modules Maximum Power Point under Different Climatic Conditions

2021

International Journal of Telecommunications

View full text Add to dashboard Cite

Photovoltaics are extensively used as a premium device for generating electricity from solar planet's power. Under uniform radiation, the photovoltaics' output characteristics have a non-linear frame with a unique maximum power point. This point changes its position when a sudden change in solar radiation occurs. Otherwise, when the PV string is subjected to partial shading conditions, several power peaks have appeared with only one Global maximum power. The classical maximum power point tracking strategies always fails to deal with these dynamic difficulties, especially, under unexpected climatic changes. Many soft computing methods are previously constructed to deal with these problems, but the main hurdles remain how to manage this tracking with the fastest time, the smallest power oscillations, and the greatest efficiency. In this paper, a modified Particle Swarm Optimization algorithm was proposed that capable of excluding or promoting certain parts of the solution search area until reach and track the max power point. The suggested method has been simulated and practically tested and the results indicate that the suggested method outperforms the typical Particle Swarm Optimization algorithm with regards to the speed of Global maximum power point tracking with the lowest oscillation and highest efficiency.

show abstract

Section: The Proposed Improved Pso (Ipso) Controller Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast Convergence Modified Particle Swarm Optimization Al-gorithm to Follow up String PV Modules Maximum Power Point under Different Climatic Conditions

2021

International Journal of Telecommunications

View full text Add to dashboard Cite

show abstract

“…Basha et al [14] studied on five MPPT techniques such as modified Variable Step Size-Radial Basis Functional Network (MVSS-RBFN), modified Hill-Climb with Fuzzy Logic Controller (MHC-FLC), ANFIS, P&O with PSO and adaptive CS. Pachaivannan et al [15] proposed a MPPT technique by using crowded plant height optimization algorithm and ANFIS. Apart from these, there are also MPPT studies based on ANFIS [16,17].…”

Section: Introductionmentioning

confidence: 99%

Training Neuro-Fuzzy by Using Meta-Heuristic Algorithms for MPPT

Kaya¹,

Kaya²,

Gökkuş³

2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

It is one of the topics that have been studied extensively on maximum power point tracking (MPPT) recently. Traditional or soft computing methods are used for MPPT. Since soft computing approaches are more effective than traditional approaches, studies on MPPT have shifted in this direction. This study aims comparison of performance of seven meta-heuristic training algorithms in the neuro-fuzzy training for MPPT. The meta-heuristic training algorithms used are particle swarm optimization (PSO), harmony search (HS), cuckoo search (CS), artificial bee colony (ABC) algorithm, bee algorithm (BA), differential evolution (DE) and flower pollination algorithm (FPA). The antecedent and conclusion parameters of neuro-fuzzy are determined by these algorithms. The data of a 250 W photovoltaic (PV) is used in the applications. For effective MPPT, different neuro-fuzzy structures, different membership functions and different control parameter values are evaluated in detail. Related training algorithms are compared in terms of solution quality and convergence speed. The strengths and weaknesses of these algorithms are revealed. It is seen that the type and number of membership function, colony size, number of generations affect the solution quality and convergence speed of the training algorithms. As a result, it has been observed that CS and ABC algorithm are more effective than other algorithms in terms of solution quality and convergence in solving the related problem.

show abstract

“…The effect of areas used for horticultural purposes is changing with innovation. Fixed electrical properties of PV, wind turbine (WT), biomass, tidal energy, geothermal energy and hydropower and many more [3,4]. Nowadays, due to the increase in energy interest and controlled petroleum derivative sources, the experiment for sustainable power has expanded considerably [5].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Black Widow Optimization Based MPPT Controller in High-Gain Non-Isolated DC-DC Converter for PV Applications

Shobanadevi¹,

PheminaSelvi²

2023

Preprint

View full text Add to dashboard Cite

Now a days, the DC-DC power converters has been considered as main topic in pulse width modulated in different applications such as renewable energy resources. Hence, a novel non-isolated converter is developed to excerpt maximum power from photovoltaic (PV) system. The maximum power is extracting during environmental conditions in an essential requirement to manage the different load conditions in the PV system. To manage the PV maximum power, the Fractional Order Proportional-Integral-Derivative (FOPID) with Black Widow Optimization (BWO) can be developed. The projected converter is achieved different advantages such as high voltage gain and output voltage. This proposed converter is working with two mode of operation. The maximum power is extract by using the proposed converter and controller. The proposed system is developed using the MATLAB/Simulink and analyzed with performance metrics of converter parameters and PV parameters. The proposed method is analyzed with two mode of operation such as constant irradiance and variable irradiance conditions. The proposed technique is contrasted with the conventional techniques as Particle Swarm Optimization (PSO),Flower Pollination Algorithm (FPA) and Gravitational Search Algorithm (GSA) respectively.

show abstract

Certain investigations of ANFIS assisted CPHO algorithm tuned MPPT controller for PV arrays under partial shading conditions

Cited by 13 publications

References 33 publications

Fast Convergence Modified Particle Swarm Optimization Al-gorithm to Follow up String PV Modules Maximum Power Point under Different Climatic Conditions

Fast Convergence Modified Particle Swarm Optimization Al-gorithm to Follow up String PV Modules Maximum Power Point under Different Climatic Conditions

Training Neuro-Fuzzy by Using Meta-Heuristic Algorithms for MPPT

Adaptive Black Widow Optimization Based MPPT Controller in High-Gain Non-Isolated DC-DC Converter for PV Applications

Contact Info

Product

Resources

About