Implementation of Maximum Power Point Tracking (MPPT) Technique on Solar Tracking System Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

Abadi, Imam; Imron, Choirul; Noriyati, Ronny Dwi

doi:10.1051/e3sconf/20184301014

Cited by 16 publications

(9 citation statements)

References 8 publications

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“…The fuzzy membership function parameters are adjusted by utilizing a hybrid learning method, including backpropagation and least square algorithms [71]. ANFIS-based MPPT is proven to improve the conversion efficiency of the solar power system [72]. The fuzzy neural network is also capable of bit error correction in predicting and forecasting weather data for solar power system [73].…”

Section: Hybrid Mpptmentioning

confidence: 99%

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Yap

Sarimuthu

Lim

2020

Journal of Modern Power Systems and Clean Energy

199

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In the last decade, artificial intelligence (AI) techniques have been extensively used for maximum power point tracking (MPPT) in the solar power system. This is because conventional MPPT techniques are incapable of tracking the global maximum power point (GMPP) under partial shading condition (PSC). The output curve of the power versus voltage for a solar panel has only one GMPP and multiple local maximum power points (MPPs). The integration of AI in MPPT is crucial to guarantee the tracking of GMPP while increasing the overall efficiency and performance of MPPT. The selection of AI-based MPPT techniques is complicated because each technique has its own merits and demerits. In general, all of the AI-based MPPT techniques exhibit fast convergence speed, less steady-state oscillation and high efficiency, compared with the conventional MPPT techniques. However, the AI-based MPPT techniques are computationally intensive and costly to realize. Overall, the hybrid MPPT is favorable in terms of the balance between performance and complexity, and it combines the advantages of conventional and AI-based MPPT techniques. In this paper, a detailed comparison of classification and performance between 6 major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results. The merits, open issues and technical implementations of AI-based MPPT techniques are evaluated. We intend to provide new insights into the choice of optimal AI-based MPPT techniques. Index Terms-Maximum power point tracking (MPPT), artificial intelligence (AI), fuzzy logic control (FLC), artificial neural network (ANN), genetic algorithm (GA), swarm intelligence (SI), machine learning (ML).

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Section: Hybrid Mpptmentioning

confidence: 99%

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Yap

Sarimuthu

Lim

2020

Journal of Modern Power Systems and Clean Energy

199

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“…The combined fuzzy logic with neural network, ANFIS is a hybrid algorithm. Neuro-Fuzzy MPPT shows an increase of 46.19% in output efficiency matched with a PV system with different algorithm [20]. Amongst various MPPT techniques applied, Neuro-Fuzzy showed the lowest convergence rate of 0.07 seconds with decreased fluctuations and additionally producing the greatest output power [21].…”

Section: 2neuro-fuzzy Systemmentioning

confidence: 99%

Neuro-Fuzzy based MPPT for Solar PV Panel

Pagatpatan¹

2020

IJETER

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Various atmospheric elements like irradiance and temperature, affect the power production and nonlinearity of I-V qualities of solar photovoltaic system. A solution is developed to mitigate the said negative effect, which is the application of maximum power point tracking (MPPT) for charge controllers. MPPT is integrated in buck-boost circuit to instantaneously boost the produced energy of the panel towards the battery by regulating its current or voltage of the panel using pulse width modulated signal (PWM). The proposed algorithm utilized is Adaptive Neuro-Fuzzy Inference System (ANFIS) and inputs are collected with voltage, current, lux and temperature sensors. The neural network trained the data first while the fuzzy logic generated the rules to guide the system to generate the appropriate value of duty cycle as the output. The fuzzy inference system (FIS) training phase showed that the trimf produced the least inaccuracy of 0.0015666%. meanwhile the testing phase showed an average testing error of 0.086933%.

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“…But then of course system complexity becomes a major issue. Abadi et al [21] proposed a Neuro-Fuzzy based adaptive MPPT algorithm which has improved the conversion efficiency and can be effective for all conditions. But the problem associated with ANN technique is that its computational time and memory requirement is large which significantly increases the complexity.…”

Section: Introductionmentioning

confidence: 99%

Enhanced model based algorithm to reinforce PV system with dynamic MPPT capability

Ehtesham

2021

IJAAS

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<p>Photovoltaic (PV) power has emerged as the most attractive resource in form of a clean and green energy. However, one major challenge associated with PV interfacing is its intermittent output characteristic which varies dramatically with the operating conditions. Thus designing an effective maximum power point tracking (MPPT) algorithm is a key aspect for optimizing PV system performance. Numerous MPPT algorithms have been proposed earlier having their own specific advantages. However, these are found to have two major limitations which have to be essentially addressed. Firstly, they become ineffective in the dynamic conditions where there is rapid change in environmental parameters like insolation and temperature. Secondly, they fail to discriminate between global and local peaks under partial shading conditions. Therefore, to achieve a reliable and efficient system operation, this paper presents an enhanced model-based (MB) algorithm that overcomes both these deficiencies. Based on new governing equations and precised estimation technique, it predetermines the MPP analytically. First simulated results are obtained where it is tested for dynamic variations of all the three parameters. Then the experimental validation is carried out on a 2 KW installed panel where real time data is recorded through CR1000 data logger and environmental parameters are sensed with elements like pyranometer and humidity sensor. A large number of experimental results are obtained for tracked MPP in the dynamic conditions, which are then summarized in tabular forms. These are finally plotted and compared with simulated results to illustrate the effectiveness of proposed MB algorithm.</p>

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Implementation of Maximum Power Point Tracking (MPPT) Technique on Solar Tracking System Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

Cited by 16 publications

References 8 publications

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Neuro-Fuzzy based MPPT for Solar PV Panel

Enhanced model based algorithm to reinforce PV system with dynamic MPPT capability

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