ANN and ANFIS Performance Prediction models for Francis type Turbines

Abstract

Turbines can be operated under partial loading conditions due to the seasonal precipitation fluctuations and due to the needed electrical demand over time. According to this partial working need, designers generate hill chart diagrams to observe the system behavior under different flow rates and head values. In order to generate a hill chart, several numerical or experimental studies have been performed at different guide vane openings and head values which are very time consuming and expensive. In this study, the efficiency prediction of Francis turbines has been performed with ANN and ANFIS methods under different operating conditions and compared with simulation results. The obtained results indicate that it is possible to obtain a hill chart using ANFIS method instead of a costly experimental or numerical tests. ANN and ANFIS parameters which effect the output, have been optimized with trying 100 different cases. 75% of the numerical data set is used for training and 25 % is used for validation as testing data. To asses and compare the performance of multiple ANN and ANFIS models several statistical indicators have been used. Insight to the performance evaluation, it is seen that ANFIS can predict the efficiency distribution with higher accuracy than the ANN model. The developed ANFIS model predicts the efficiency with 1.41% mean average percentage error and 0.999 R-2 value. To the best of the author's knowledge, this is the first study in the literature that ANN and ANFIS are used in order to predict the efficiency distribution of the turbines at different loading conditions.