A soft-computing technique for modeling and simulation of stand-alone photovoltaic power supply system

作者：Mellit A

来源：International Review of Electrical Engineering, 2007, 2(4): 480-488.

• photovoltaic system
• modeling
• simulation
• ANN
• ANFIS

Due to the increasing need for intelligent systems, the Adaptive Neuro-fuzzy inference System (ANFIS) has recently attracted the attention of researchers in various scientific and engineering areas. The purpose of this work is to present the modelling of a Stand-Alone Photovoltaic Power Supply (SAPVPS) system using ANFIS. For the modelling of the SAPVPS system, it is required to find suitable models for its different components (ANFIS P V-generator, ANFIS battery and ANFIS regulator) under variable climatic conditions. A database of measured weather data (global radiation, temperature and humidity) and electrical signals (photovoltaic, battery and regulator voltage and current) of a SAPVPS system installed in Tahifet (south of Algeria) has been recorded for the period from 1992 to 1997 using a data acquisition system. These data have beet? used for the modelling and simulation of the SAPVPS system. The ANFIS for the PV-generator, battery and regulator have been trained by using 10 signals recorded from the different components of the PVPS system. Each signal is represented by 365*5 values (complete 5-years). A set of data for 4-years have been used for the training of the ANFIS and data for 1-year has beet? used for the testing of the ANFIS. In this way, the ANFIS was trained to accept and handle a number of unusual cases. The comparison between actual and estimated values obtained from the ANFIS gave satisfactory results. The correlation coefficient between measured values and those estimated by the ANFIS gave good prediction accuracy of 98%. In addition., test results show that the ANFIS performed better than the Artificial Neural Networks (ANN). Predicted electrical signals by the ANFIS can be used for several applications in PV systems.

• 出版日期2007-8