Maximum power point tracking using Hill Climbing and ANFIS techniques for PV applications: A review and a novel hybrid approach

Lasheen, Mohamed; Abdel‐Salam, M.

doi:10.1016/j.enconman.2018.06.003

Cited by 110 publications

(38 citation statements)

References 96 publications

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“…Hence, several types of ANFIS-MPPTs have been designed using different types of training data. Among them, in [22], the authors developed an MPPT technique based on adaptive ANFIS and hill climbing (HC) techniques to increase the generated energy from a PV system. This proposed technique is a combination of two stages to adjust the property duty cycle of a boost converter for MPP tracking.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, the second order polynomial of last squares is used to get a best fit. To draw those fitting curves, MATLAB code is generated based on Equations (22) and (12). As seen in Figure 7, the power generation of a PV array increases as solar irradiance increases; conversely, it is better for a low temperature operation than a raised one.…”

Section: Curve Fitting Techniquementioning

confidence: 99%

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Design of an Efficient Maximum Power Point Tracker Based on ANFIS Using an Experimental Photovoltaic System Data

2019

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Maximum power point tracking (MPPT) techniques are a fundamental part in photovoltaic system design for increasing the generated output power of a photovoltaic array. Whilst varying techniques have been proposed, the adaptive neural-fuzzy inference system (ANFIS) is the most powerful method for an MPPT because of its fast response and less oscillation. However, accurate training data are a big challenge for designing an efficient ANFIS-MPPT. In this paper, an ANFIS-MPPT method based on a large experimental training data is designed to avoid the system from experiencing a high training error. Those data are collected throughout the whole of 2018 from experimental tests of a photovoltaic array installed at Brunel University, London, United Kingdom. Normally, data from experimental tests include errors and therefore are analyzed using a curve fitting technique to optimize the tuning of ANFIS model. To evaluate the performance, the proposed ANFIS-MPPT method is simulated using a MATLAB/Simulink model for a photovoltaic system. A real measurement test of a semi-cloudy day is used to calculate the average efficiency of the proposed method under varying climatic conditions. The results reveal that the proposed method accurately tracks the optimized maximum power point whilst achieving efficiencies of more than 99.3%.

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Section: Related Workmentioning

confidence: 99%

Section: Curve Fitting Techniquementioning

confidence: 99%

Design of an Efficient Maximum Power Point Tracker Based on ANFIS Using an Experimental Photovoltaic System Data

2019

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“…In the above relations, I 0 is the reverse saturation current value, with changes in temperature, by the following Here, I 0⋅ref is the reverse saturation current in the standard conditions and E g = 1.121 1 − 0.0002677 T − T n is the distance of the power strips of silicon at 1 eV. Other variables of the above equations are tabulated in Table 1 [15].…”

Section: The Pv Modulementioning

confidence: 99%

“…Innovation of the proposed method is to use a search algorithm that receives the power and current of the PV array, and general maximum is estimated in its output. This algorithm improves the capability of existing one-step methods and increases their efficiency [11][12][13][14][15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Maximum power point tracking of the solar power plants in shadow mode through artificial neural network

Zandi

Mazinan

2019

Complex Intell. Syst.

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The use of solar cells despite being free of contamination and unlimited in terms of the amount of energy is considered as a costly way to generate energy. Two main factors may be enumerated as follows. First of all, the amount of sunlight and ambient temperature affected the amount of energy received from sunlight by solar panels, as long as the amount of sunlight changes overnight in line with changing weather conditions and the second one is the low efficiency of the energy conversion. The main reason for the low electrical efficiency is the nonlinear variation of the output voltage and current along with the change of the amount of radiation, the change of the temperature of the operating environment and the change of the electric charge, respectively. To address this concern, the maximum point of the photovoltaic system can be tracked through an appropriate algorithm and pushes the system point to the optimal point. In a word, the key goal of the investigation presented here is to provide an approach that in the high speed and precision of convergence to the maximum power point is well considered. So far, a large number of available methods have been used to increase the efficiency of solar cells. Some of these are associated with problems in the tracking process or they respond slowly. It should be noted that a set of them are depended on the types and structures of solar cells and also their implementation is very complex and costly. Therefore, this study has focused on intelligence-based techniques such as artificial neural networks to solve all the problems mentioned. The investigated outcomes verify the effectiveness of the approach performance proposed.

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“…A PV module's output power is, however, plagued by irradiance levels, temperature, loads, and so on, which undoubtedly impair conversion efficiency. A number of studies in the literature have addressed such problems and improved the overall conversion efficiency of a PV system [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]. Ahmed et al proposed a design strategy of perturbation parameters based on the HC algorithm for PV MPPT control [5].…”

Section: Introductionmentioning

confidence: 99%

A high-performance MPPT algorithm combining advanced three-point weight comparison and temporary stopped running strategy for PV systems

Liu

2019

IEICE Electron. Express

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This paper proposes a new maximum power point tracking (MPPT) control strategy, including a boost converter temporary stopped running (TSR) strategy and an advanced three-point weight comparison method (ATPWC). The proposed ATPWC was used to detect three power points of a photovoltaic (PV) module output, and a microcontroller unit (MCU) was used to calculate the slope of the three power points and to perform MPPT so as to improve the system's conversion efficiency. The proposed method was successfully applied to an independent solar power generation system, in which the PV module was connected to a boost converter and then connected in series with an inverter to a single-phase 110 VAC/60 Hz output and connected to the power grid. The measured results showed that, in terms of the TSR control strategy, when the system output met a load of 110 Vrms, TSR could reduce switching loss and conduction loss and result in a 10% higher overall system conversion efficiency than that of traditional control. In terms of the MPPT algorithm, actual measurements were carried out under an irradiance level of 100 W/m 2 ∼700 W/m 2. Versus traditional hill climbing (HC) algorithm, the efficiency of the proposed ATPWC was better.

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Maximum power point tracking using Hill Climbing and ANFIS techniques for PV applications: A review and a novel hybrid approach

Cited by 110 publications

References 96 publications

Design of an Efficient Maximum Power Point Tracker Based on ANFIS Using an Experimental Photovoltaic System Data

Design of an Efficient Maximum Power Point Tracker Based on ANFIS Using an Experimental Photovoltaic System Data

Maximum power point tracking of the solar power plants in shadow mode through artificial neural network

A high-performance MPPT algorithm combining advanced three-point weight comparison and temporary stopped running strategy for PV systems

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