Fuzzy logic controller for the maximum power point tracking in photovoltaic system

Balasubramanian, G.; Singaravelu, S.

doi:10.5120/5594-7840

Cited by 33 publications

(15 citation statements)

References 10 publications

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“…The proposed MPPT technique had provided fast and accurate tracking for every atmospheric condition and results validated by simulation in MATLAB TM / SIMULINK TM (Shahana & Linus, 2016). The FL controller based MPPT is provided better performance as compared to PO and Proportional Integral (PI) based MPPT techniques and is also validated with the experimental setup under variation of irradiation and temperature (Balasubramanian & Singaravelu, 2012). (Nabizadeh, Alizadeh, Afifi & Soltani, 2013) presented an FL controller based MPPT which obtained improved performance as compared to PI-based MPPT controller.…”

Section: Introductionmentioning

confidence: 79%

A Novel Artificial Intelligence based Hybrid Maximum Power Point Tracking Technique for Solar Photovoltaic System

Khan¹

2020

Preprint

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Backgrounds: Solar photo-voltaic (PV) arrays have non-linear characteristics with distinctive maximum power point (MPP) which relies on ecological conditions such as solar radiation and ambient temperature. In order to obtain continuous maximum power (MP) from PV arrays under varying ecological conditions, maximum power point tracking (MPPT) control methods are employed. MPPT is utilized to extract MP from the solar PV array, high-performance soft computing techniques can be used as an MPPT technique. Results: In order to show the feasibility and performance of the proposed Artificial Intelligence based Perturbe and Observe (AIAPO) MPPT controller, a simulation analysis has been carried out using the PV system. Combined results with different MPPT systems for power, voltage and current waveforms are the output values increase to 272.4W, 157V and 1.74A respectively. Using proposed AIAPO MPPT provides more accurate and stable result as compared to Perturbe and Observe (PO), Fuzzy Logic (FL) and Artificial Neural Network (ANN) based MPPT Technique. As per the experimentation performed by various MPPT techniques are carried out for PV system which are clearly indicating that the comparative analysis of power, voltage and current performance of PV system (i.e. have been recorded 272.4W, 157V and 1.74A) using proposed MPPT method which is better than the PO based MPPT (i.e. 169.1W, 127V, 1.43A), FL based MPPT technique (i.e. 256.9W, 152V, 1.69A) and ANN based MPPT technique (i.e. 265W, 154V, 1.71A) correspondingly. Conclusions: The aim of this paper is to track MPP from the solar PV array by the proposed hybrid controller for irradiation changes and comparing results with PO, FL and ANN based MPPT controllers. Different MPPT techniques have been used to compute MPP and improved efficiency of the PV panel. AIAPO, ANN, FL and PO MPPT methods have been chosen to obtain this objective. Simulation results showing that the system in which proposed control method has been used gives better performance and reduce fluctuations of the MPP as compared to PO, FL and ANN based MPPT technique at rapid changes of irradiation. In order to fabricate a reliable and real time hybrid system, there is a massive scope of research to develop multi-input renewable energy systems.

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Section: Introductionmentioning

confidence: 79%

A Novel Artificial Intelligence based Hybrid Maximum Power Point Tracking Technique for Solar Photovoltaic System

Khan¹

2020

Preprint

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“…Hence, many complex systems can be controlled without knowing the exact mathematical model of the system. In addition, fuzzy logic simplifies the design when dealing with nonlinearities in systems [15,16] [24,25].Equation 12-14Fuzzy controller design includes the following elements:…”

Section: Fuzzy Logic Control Based Mpptmentioning

confidence: 99%

“…The purpose of the adaptive mechanism is to modify the duty cycle of the defuzzification of FLC, so it makes the PV system to provide a better response time and a higher output power [23]. The adaptive mechanism comprises three parts which is discussed as follows: First, in order to eliminate the high-frequency noise, we adopt the moving average filter to compute PPV as: Equation (16)(17)(18) P pv (n) = [P pv (n − 1) + P pv (n − 2)]/2…”

Section: Adaptive Mechanismmentioning

confidence: 99%

Comparative analysis of Maximum Power Point Tracking Algorithms for Photovoltaic Applications

Ammaiyappan

Seyezhai

2020

Wseas Transactions on Power Systems

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Currently, photovoltaic system has gained importance in electrical power generation system, since it is a clean and renewable energy source. An important characteristic of photovoltaic panels is that the available maximum power is provided only in a single operating point called maximum power point. But the position of maximum power point is not fixed and it moves according to the varying irradiance, varying temperature and the load. This requires a mechanism called maximum power point tracking (MPPT) so that maximum power is obtained effectively. In the literature, many classical methods have been developed and implemented to track the maximum power point. The main objective of this paper is to study and analyze the classical techniques such as Perturb and Observe (P&O), Incremental Conductance (IC) and Fuzzy Logic Control (FLC) algorithms and propose an Adaptive Fuzzy Logic Control (AFLC) for MPPT. The performance of AFLC is compared with the conventional MPPT techniques for varying irradiance. The significance of these methods are studied by implementing the algorithm in MATLAB. The experimental results show the effectiveness and feasibility of the proposed method and the results are verified. The results reveal that the adaptive FLC can quickly track change of MPP for various light intensity and delivers higher power compared to the classical algorithms.

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“…But the disadvantage is that the results may be unsatisfactory due to its unstable behavior at low insolation levels [9][10].The constant voltage (CV) algorithm is simple but it cannot locate the exact MPP practically but preferred for low levels of insolation [11][12].The feedback voltage or current method employs a feedback control loop but it cannot consider the effect of variations in insolation and temperature [13].In order to track MPPT accurately, neural network is employed but the performance of the PV system is entirely based on how well a neural network has been initially trained [14].To further improve the tracking of PV power, a fuzzy logic controller is reported in the literature which does not require the mathematical model of PV [15]. But the effectiveness of this method depends on user knowledge and skill in choosing the correct rule base table which depends on the chosen membership functions [16]. To overcome the drawbacks of all the above mentioned methods, an adaptive fuzzy logic controller is proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Adaptive Fuzzy Logic Control Based MPPT for Photovoltaic System

Sankar¹,

Seyezhai²

2016

IJUNESST

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Fuzzy logic controller for the maximum power point tracking in photovoltaic system

Cited by 33 publications

References 10 publications

A Novel Artificial Intelligence based Hybrid Maximum Power Point Tracking Technique for Solar Photovoltaic System

A Novel Artificial Intelligence based Hybrid Maximum Power Point Tracking Technique for Solar Photovoltaic System

Comparative analysis of Maximum Power Point Tracking Algorithms for Photovoltaic Applications

Implementation of Adaptive Fuzzy Logic Control Based MPPT for Photovoltaic System

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