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Frontiers in Energy

Front. Energy    2015, Vol. 9 Issue (3) : 322-334
Enhancement of grid-connected photovoltaic system using ANFIS-GA under different circumstances
Department of Electrical Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran
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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)     
Corresponding Authors: Saeed VAFAEI   
Just Accepted Date: 07 May 2015   Online First Date: 11 June 2015    Issue Date: 11 September 2015
 Cite this article:   
Saeed VAFAEI,Alireza REZVANI,Majid GANDOMKAR, et al. Enhancement of grid-connected photovoltaic system using ANFIS-GA under different circumstances[J]. Front. Energy, 2015, 9(3): 322-334.
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Fig.1  Equivalent circuit of the photovoltaic array
Current at maximum power IMP/A Voltage at maximum power VMP/V Maximum power PMAX/ W Open circuit voltage VOC /V Short circuit current ISC/A Total number of parallel cells NP Total number of series cells NS
4.94 18.65 90 22.32 5.24 1 36
Tab.1  Red sun 90 W module
Number of design variable Population size Crossover constant Mutation rate Maximum generations
1 27 75% 13% 24
Tab.2  GA parameters
Fig.2  ANFIS architecture of a 2-input first-order Sugeno fuzzy model with 2 rules
Fig.3  Proposed MPPT scheme
Fig.4  Data

(a) Inputs data of irradiation and temperature; (b) VMPP corresponding to MPP

Fig.5  ANFIS controller structure
Fig.6  Solar irradiance membership function for ANFIS
Fig.7  Temperature membership functions for ANFIS
Fig.8  Fuzzy rules
Fig.9  Output of ANFIS with the amount of target data
Fig.10  Output of ANFIS VMPP with the amount of target data
Fig.11  Total error percentage of VMPP after training data
Fig.12  Output of ANFIS test with the amount of target data
Fig.13  Output of ANFIS test of VMPP with the amount of target data
Fig.14  Error percentage of test data of VMPP after training data
Fig.15  Synchronous reference machine
Fig.16  Inverter control model
Fig.17  Case study system
Fig.18  Structure of P/Q strategy
Fig.19  Simulated results for PV (variation of irradiance) in case 1

(a) Irradiance; (b) inverter output voltage; (c) inverter output current; (d) PV power; (e) grid power

Fig.20  Simulated results for PV (variation of temperature) in case 1

(a) Temperature; (b) grid voltage; (c) inverter output current; (d) PV power; (e) grid power

MPPT techniques Convergence speed Implementation complexity Periodic tuning Sensed parameters
P& O Varies Low No Voltage
IC Varies Medium No Voltage, Current
Fuzzy logic control Fast High Yes Varies
ANIFS+ GA Fast High Yes Varies
Tab.3  Characteristics of different MPPT techniques
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