水力发电
多线性映射
涡轮机
人工神经网络
计算机科学
人工智能
机器学习
可靠性工程
工程类
数学
机械工程
纯数学
电气工程
作者
Krishna Kumar,Aman Kumar,Gaurav Saini,Mazin Abed Mohammed,Rachna Shah,Jan Nedoma,Radek Martínek,Seifedine Kadry
标识
DOI:10.1016/j.suscom.2024.100958
摘要
Silt is the leading cause of the erosion of the turbine's underwater components during hydropower generation. This erosion subsequently decreases the machine's efficiency. The present study aims to develop statistical correlations for predicting the efficiency of a hydropower plant based on the Kaplan turbine. Historical data from a Kaplan turbine-based hydropower plant was employed to create the model. Curve fitting, multilinear regression (MLR), and artificial neural network (ANN) techniques were used to develop models for predicting the machine's efficiency. The results show that the ANN method is better at predicting the machine's efficiency than the MLR and curve fitting methods. It got an R2-value of 0.99966, a MAPE of 0.0239%, and an RMSPE of 0.1785%. Equipment manufacturers, plant owners, and researchers can use the established correlation to evaluate the machine's condition in real-time. Additionally, it offers utility in formulating effective operations and maintenance (O&M) strategies.
科研通智能强力驱动
Strongly Powered by AbleSci AI