Performance monitoring of kaplan turbine based hydropower plant under variable operating conditions using machine learning approach

IF 3.8 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems Pub Date : 2024-01-11 DOI:10.1016/j.suscom.2024.100958

Krishna Kumar , Aman Kumar , Gaurav Saini , Mazin Abed Mohammed , Rachna Shah , Jan Nedoma , Radek Martinek , Seifedine Kadry

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引用次数: 0

Abstract

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.

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利用机器学习方法监测基于卡普兰水轮机的水电站在可变运行条件下的性能

在水力发电过程中，淤泥是造成水轮机水下部件侵蚀的主要原因。这种侵蚀会降低机器的效率。本研究旨在为预测基于卡普兰水轮机的水电站效率开发统计相关性。该模型采用了基于卡普兰水轮机的水电站的历史数据。曲线拟合、多线性回归（MLR）和人工神经网络（ANN）技术被用来开发预测机器效率的模型。结果表明，在预测机器效率方面，ANN 方法优于 MLR 和曲线拟合方法。其 R2 值为 0.99966，MAPE 为 0.0239%，RMSPE 为 0.1785%。设备制造商、工厂业主和研究人员可以利用建立的相关性实时评估机器的状况。此外，它还有助于制定有效的运营和维护 (O&M) 策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Sustainable Computing-Informatics & Systems COMPUTER SCIENCE, HARDWARE & ARCHITECTUREC-COMPUTER SCIENCE, INFORMATION SYSTEMS

CiteScore

10.70

自引率

4.40%

发文量

142

期刊介绍： Sustainable computing is a rapidly expanding research area spanning the fields of computer science and engineering, electrical engineering as well as other engineering disciplines. The aim of Sustainable Computing: Informatics and Systems (SUSCOM) is to publish the myriad research findings related to energy-aware and thermal-aware management of computing resource. Equally important is a spectrum of related research issues such as applications of computing that can have ecological and societal impacts. SUSCOM publishes original and timely research papers and survey articles in current areas of power, energy, temperature, and environment related research areas of current importance to readers. SUSCOM has an editorial board comprising prominent researchers from around the world and selects competitively evaluated peer-reviewed papers.