5th International GAP MATHEMATICS-ENGINEERING-SCIENCE AND HEALTH SCIENCES , Şanlıurfa, Türkiye, 4 - 06 Aralık 2020, ss.213-225
In this investigation, adaptive neuro-fuzzy interference system (ANFIS) was carried out to estimate the power capacity and rotational speed of a turbine located in a power plant in order to demonstrate that the adaptive neuro-fuzzy interference system is an acceptable algorithm to estimate turbine characteristics parameters. For the development of forecasting models, the total of 743 data records were collected and these data were divided into two subsets such as training and testing data set. While the training data set includes in a total of 527 records, the testing data set consists of 216 records obtained from the turbine. In the current simulation, the fuzzy membership functions (MFs) are set as trapmf and output of MF is adjusted to linear. The optimization method of the Train FIS is the hybrid. Back propagation was also performed but hybrid one demonstrated more accurate results. The values acquired by adaptive-neural fuzzy interference system were compared with real data. The mean absolute percentage error (MAPE) was found as 0.55% for turbine rotational speed and 2.7% for power output of the turbine. On the other hand, while mean absolute deviation (MAD) and root mean squared error (RMSE) were calculated as 0.81% and 10% for the prediction of turbine rotational speed, respectively, they were 4.2% and 5.4% for the estimation of power output of the turbine. The prediction of ANFIS method follows the change of actual values almost exactly. It was stated that the correlation between actual and predicted values are very high. Moreover, it can be said that the performance values of power output and rotational speed such as mean absolute percentage error (MAPE), mean absolute deviation (MAD) and root mean squared error (RMSE) was found to be smaller for training case compared to the testing case. Thus, this study shows that the rotational speed and power output of a turbine can be accurately predicted by using the input data of temperature and speed.