Designing an Intelligent Control Philosophy in Reservoirs of Water Transfer Networks in Supervisory Control and Data Acquisition System Stations

作     者：Ali Dolatshahi Zand Kaveh Khalili-Damghani Sadigh Raissi Ali Dolatshahi Zand;Kaveh Khalili-Damghani;Sadigh Raissi

作者机构：Department of Industrial EngineeringSouth Tehran BranchIslamic Azad UniversityTehran ***Iran 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2021年第18卷第5期

页      码：694-717页

摘      要：In this paper, a hybrid neural-genetic fuzzy system is proposed to control the flow and height of water in the reservoirs of water transfer networks. These controls will avoid probable water wastes in the reservoirs and pressure drops in water distribution networks. The proposed approach combines the artificial neural network, genetic algorithm, and fuzzy inference system to improve the performance of the supervisory control and data acquisition stations through a new control philosophy for instruments and control valves in the reservoirs of the water transfer networks. First, a multi-core artificial neural network model, including a multi-layer perceptron and radial based function, is proposed to forecast the daily consumption of the water in a reservoir. A genetic algorithm is proposed to optimize the parameters of the artificial neural networks. Then, the online height of water in the reservoir and the output of artificial neural networks are used as inputs of a fuzzy inference system to estimate the flow rate of the reservoir inlet. Finally, the estimated inlet flow is translated into the input valve position using a transform control unit supported by a nonlinear autoregressive exogenous model. The proposed approach is applied in the Tehran water transfer network. The results of this study show that the usage of the proposed approach significantly reduces the deviation of the reservoir height from the desired levels.

主 题 词：Water demand forecasting water transfer network supervisory control and data acquisition water management multicore artificial neural network fuzzy inference system 

学科分类：12[管理学] 081504[081504] 1201[管理学-管理科学与工程类] 081104[081104] 08[工学] 0815[工学-矿业类] 0835[0835] 0802[工学-机械学] 0811[工学-水利类] 080201[080201] 0812[工学-测绘类] 

核心收录：

D　O　I：10.1007/s11633-021-1284-1

馆 藏 号：203102717...