Application of Rough Set and Neural Network in Water Energy Utilization

被引：0

作者：

Wei, Minghua ^{[1
]}

Zheng, Zhihong ^{[1
,2
]}

Bai, Xiao ^{[3
]}

Lin, Ji ^{[3
]}

Taghizadeh-Hesary, Farhad ^{[4
]}

机构：

[1] North China University of Water Resources and Electric Power, Henan,Zhengzhou, China

[2] Henan Vocational College of Water Conservancy and Environment, Zhengzhou, China

[3] School of Finance, Zhejiang University of Finance and Economics, Hangzhou, China

[4] Social Science Research Institute, Tokai University, Hiratsuka-Shi, Japan

来源：

Frontiers in Energy Research | 2021年 / 9卷

关键词：

Hydraulic motors - Backpropagation - Failure analysis - Hydraulic turbines - Decision making - Decision theory - Fault detection - Approximation algorithms - Fuzzy neural networks - Rough set theory - Multilayer neural networks - Fuzzy inference;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

In water energy utilization, the damage of fault occurring in the power unit operational process to equipment directly affects the safety of the unit and efficiency of water power conversion and utilization, so fault diagnosis of water power unit equipment is especially important. This work combines a rough set and artificial neural network and uses it in fault diagnosis of hydraulic turbine conversion, puts forward rough set theory based on the tolerance relation and defines similarity relation between samples for the decision-making system whose attribute values are consecutive real numbers, and provides an attribute-reducing algorithm by making use of the condition that approximation classified quality will not change. The diagnostic rate of artificial neural networks based on a rough set is higher than that of the general three-layer back-propagation(BP) neural network, and the training time is also shortened. But, the network topology of an adaptive neural-fuzzy inference system is simpler than that of a neural network based on the rough set, the diagnostic accuracy is also higher, and the training time required under the same error condition is shorter. This algorithm processes consecutive failure data of the hydraulic turbine set, which has avoided data discretization, and this indicates that the algorithm is effective and reliable. © Copyright © 2021 Wei, Zheng, Bai, Lin and Taghizadeh-Hesary.

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