Intelligent Control System for Real Time Reservoir Operation
Resource
農業工程學報45(4),18-30
Journal
農業工程學報
Pages
18-30
Date Issued
1999-12
Date
1999-12
Author(s)
Chang, Li-Chiu
Abstract
A common strategy in the traditional reservoir operation is to search rule curves through system analysis according to historical inflow data, then, step by step, to adjust rule curves with coming data. This strategy is easy and convenient for reservoir operation; however, the ranges between rule curves are too large to precisely operate outflow of reservoir for water usage. Moreover, the information of inflow and extreme events are not taken into account. Consequently, the rule curves, in general, could not work very well for the prevention of flood or drought. From the aforementioned points, we propose an adaptive network-based fuzzy inference system (ANFIS) to enhance the efficiency of reservoir operation. By combining fuzzy inference systems and neural networks, ANFIS not only can handle both quantitative (numerical) and qualitative (linguistic) knowledge, but also can successfully deal with the control laws for a complex system. Two main procedures are performed in order to implement the model to reservoir operation system. First, the genetic algorithm is used to search the optimal reservoir operating histogram which is recognized as the training pattern is the next step. Second, the ANFIS model is built to create the fuzzy inference system, to propose the suitable parameters, and to estimate the optimal water release. The proposed model is intended to investigate its practicability and efficiency by using the Shihmen reservoir. The M-5 rule curves are also performed for the purpose of comparison. The results show that the ANFIS model has better performance than the M-5 rule curves.
Subjects
Adaptive network-based fuzzy inference system
Reservoir operation
Fuzzy inference systems
Genetic algorithm
Neural networks
Type
journal article
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