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

doi:10.1007/s11708-015-0362-x

Front. Energy

2015, Vol. 9

Issue (3) : 322-334 https://doi.org/10.1007/s11708-015-0362-x

RESEARCH ARTICLE

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

Saeed VAFAEI(

),Alireza REZVANI,Majid GANDOMKAR,Maziar IZADBAKHSH

Department of Electrical Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran

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Abstract

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 Author(s): 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.

URL:

https://academic.hep.com.cn/fie/EN/10.1007/s11708-015-0362-x
https://academic.hep.com.cn/fie/EN/Y2015/V9/I3/322

Fig.1 Equivalent circuit of the photovoltaic array

Tab.1 Red sun 90 W module

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) V_MPP 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 V_MPP with the amount of target data

Fig.11 Total error percentage of V_MPP after training data

Fig.12 Output of ANFIS test with the amount of target data

Fig.13 Output of ANFIS test of V_MPP with the amount of target data

Fig.14 Error percentage of test data of V_MPP 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

Tab.3 Characteristics of different MPPT techniques

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