Application of Rough Set and Neural Network in Water Energy Utilization
Application of Rough Set and Neural Network in Water Energy Utilization
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DOI：
10.3389/fenrg.2021.604660
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Minghua Wei;Z. Zheng;Xiao Bai;Ji Lin;Farhad Taghizadeh‐Hesary
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文献类型：
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作者：
Minghua Wei;Z. Zheng;Xiao Bai;Ji Lin;Farhad Taghizadeh‐Hesary
在水能利用中，机组运行过程中发生的故障对设备的损害直接影响到机组的安全和水电转换利用的效率，因此对水电机组设备的故障诊断尤为重要。将粗糙集与人工神经网络相结合，应用于水轮机涡轮机转换故障诊断中，提出了基于容差关系的粗糙集理论，定义了属性值为连续真实的数的决策系统样本间的相似关系，并利用近似分类质量不变的条件，给出了一种属性约简算法。基于粗糙集的人工神经网络比一般的三层BP神经网络具有更高的诊断率，并且训练时间也缩短了。但是，自适应神经模糊推理系统的网络拓扑结构比基于粗糙集的神经网络更简单，诊断精度也更高，并且在相同的错误条件下所需的训练时间更短。该算法处理了水轮机涡轮机组连续故障数据，避免了数据离散化，表明该算法是有效可靠的。
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.