A Hybrid Control Method for Maximum Power Point Tracking (MPPT) in Photovoltaic Systems

Vincheh, Mahdi Rajabi; Kargar, Abbas; Markadeh, Gholamreza Arab

doi:10.1007/s13369-014-1056-0

Cited by 39 publications

(29 citation statements)

References 12 publications

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“…Besides, genetic algorithm is used for optimum values and then optimum values are used for training ANFIS [39,40]. The procedure employed for implementing genetic algorithm is as follows [41]: 1) defining the objective function and recognizing the design parameters, 2) defining the initial production population, 3) evaluating the population using the objective function, and 4. conducting convergence test stop if convergence is provided.…”

Section: The Steps In Implementing Genetic Algorithmmentioning

confidence: 99%

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Improvement of Microgrid Dynamic Performance under Fault Circumstances using ANFIS for Fast Varying Solar Radiation and Fuzzy Logic Controller for Wind System

Izadbakhsh¹,

Rezvani²,

Gandomkar³

2014

Archives of Electrical Engineering

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Abstract:The microgrid (MG) technology integrates distributed generations, energy storage elements and loads. In this paper, dynamic performance enhancement of an MG consisting of wind turbine was investigated using permanent magnet synchronous generation (PMSG), photovoltaic (PV), microturbine generation (MTG) systems and flywheel under different circumstances. In order to maximize the output of solar arrays, maximum power point tracking (MPPT) technique was used by an adaptive neuro-fuzzy inference system (ANFIS); also, control of turbine output power in high speed winds was achieved using pitch angle control technic by fuzzy logic. For tracking the maximum point, the proposed ANFIS was trained by the optimum values. The simulation results showed that the ANFIS controller of grid-connected mode could easily meet the load demand with less fluctuation around the maximum power point. Moreover, pitch angle controller, which was based on fuzzy logic with wind speed and active power as the inputs, could have faster responses, thereby leading to flatter power curves, enhancement of the dynamic performance of wind turbine and prevention of both frazzle and mechanical damages to PMSG. The thorough wind power generation system, PV system, MTG, flywheel and power electronic converter interface were proposed by using Mat-lab/Simulink.

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Section: The Steps In Implementing Genetic Algorithmmentioning

confidence: 99%

“…X is the design variable equal to array current and also, F (X) is the array output power which should be maximized [39]. To determine the objective function, the power should be arranged based on the current of array (I X ).…”

Section: The Steps In Implementing Genetic Algorithmmentioning

confidence: 99%

Improvement of Microgrid Dynamic Performance under Fault Circumstances using ANFIS for Fast Varying Solar Radiation and Fuzzy Logic Controller for Wind System

Izadbakhsh¹,

Rezvani²,

Gandomkar³

2014

Archives of Electrical Engineering

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“…Besides, GA is used for optimum values and then, optimum values are used for training ANN [10,22]. The procedure employed for implementing GA is as follows [23,37]: 1) Defining the objective function and recognizing the design parameters, 2) Defining the initial production population, 3) Evaluating the population using the objective function, 4) Conducting convergence test stop, if convergence is provided. The objective function of GA is used for its optimization (using Matlab software) by the following: finding the optimum X = (X 1 , X 2 , X 3 ,..., X n ) to put the F(X) in the maximum value, where the number of design variables are considered as 1.…”

Section: Introductionmentioning

confidence: 99%

“…The objective function of GA is used for its optimization (using Matlab software) by the following: finding the optimum X = (X 1 , X 2 , X 3 ,..., X n ) to put the F(X) in the maximum value, where the number of design variables are considered as 1. X is the design variable equal to array current and also, F(X) is the array output power which should be maximized [22,23]. To determine the objective function, the power should be arranged based on the current of array (I X ).…”

Section: Introductionmentioning

confidence: 99%

Dynamic response improvement of hybrid system by implementing ANN-GA for fast variation of photovoltaic irradiation and FLC for wind turbine

Izadbakhsh¹,

Rezvani²,

Gandomkar³

2015

Archives of Electrical Engineering

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Abstract:In this paper, dynamic response improvement of the grid connected hybrid system comprising of the wind power generation system (WPGS) and the photovoltaic (PV) are investigated under some critical circumstances. In order to maximize the output of solar arrays, a maximum power point tracking (MPPT) technique is presented. In this paper, an intelligent control technique using the artificial neural network (ANN) and the genetic algorithm (GA) are proposed to control the MPPT for a PV system under varying irradiation and temperature conditions. The ANN-GA control method is compared with the perturb and observe (P&O), the incremental conductance (IC) and the fuzzy logic methods. In other words, the data is optimized by GA and then, these optimum values are used in ANN. The results are indicated the ANN-GA is better and more reliable method in comparison with the conventional algorithms. The allocation of a pitch angle strategy based on the fuzzy logic controller (FLC) and comparison with conventional PI controller in high rated wind speed areas are carried out. Moreover, the pitch angle based on FLC with the wind speed and active power as the inputs can have faster response that lead to smoother power curves, improving the dynamic performance of the wind turbine and prevent the mechanical fatigues of the generator.

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“…GA is used for data optimization and then, the optimum values are utilized for training neural networks and the results show that the GA technique has less fluctuation in comparison with the conventional methods [22][23][24]. However, one of the major drawbacks in the papers mentioned that they are not practically connected to the grid in order to ensure the analysis of PV system performance.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of grid-connected photovoltaic system using ANFIS-GA under different circumstances

et al. 2015

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In recent years, many different techniques are applied in order to draw maximum power from photovoltaic (PV) modules for changing solar irradiance and temperature conditions. Generally, the output power generation of the PV system depends on the intermittent solar insolation, cell temperature, efficiency of the PV panel and its output voltage level. Consequently, it is essential to track the generated power of the PV system and utilize the collected solar energy optimally. The aim of this paper is to simulate and control a grid-connected PV source by using an adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) controller. The data are optimized by GA and then, these optimum values are used in network training. The simulation results indicate that the ANFIS-GA controller can meet the need of load easily with less fluctuation around the maximum power point (MPP) and can increase the convergence speed to achieve the MPP rather than the conventional method. Moreover, to control both line voltage and current, a grid side P/Q controller has been applied. A dynamic modeling, control and simulation study of the PV system is performed with the Matlab/Simulink program.Keywords photovoltaic system, maximum power point (MPP), adaptive neuro-fuzzy inference system (ANFIS), genetic algorithm (GA)

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A Hybrid Control Method for Maximum Power Point Tracking (MPPT) in Photovoltaic Systems

Cited by 39 publications

References 12 publications

Improvement of Microgrid Dynamic Performance under Fault Circumstances using ANFIS for Fast Varying Solar Radiation and Fuzzy Logic Controller for Wind System

Improvement of Microgrid Dynamic Performance under Fault Circumstances using ANFIS for Fast Varying Solar Radiation and Fuzzy Logic Controller for Wind System

Dynamic response improvement of hybrid system by implementing ANN-GA for fast variation of photovoltaic irradiation and FLC for wind turbine

Enhancement of grid-connected photovoltaic system using ANFIS-GA under different circumstances

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