DETERMINATION OF SENSORLESS INPUT PARAMETERS OF SOLAR PANEL WITH ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) METHOD

The paper aims to benefit the artificial neural network by means of the adaptive neuro-fuzzy inference system (ANFIS) method to determine the input parameters of solar panel without using any sensors. In this respect, the input parameters are the irradiance in W/m(2) and the cell temperature in degree Celsius. Normally, these two parameters are measured with pyranometer and temperature sensors which are expensive and giving the complexity of the solar panel systems. In this research, the parameters of irradiance and cell temperature are obtained with taking the voltage and current of one cell of solar panel as the input signals. These signals are given to ANFIS network through the training and validation process. As the ANFIS network is the multi input and single output network, there will be two developed ANFIS networks which indicate the estimated irradiance and cell temperature. The ANFIS networks are confirmed with the sum of square error regarding the type of membership function and the number of nodes structure.