Flexible Generator Maintenance Scheduling in a Practical System Using Fuzzy Logic and Genetic Algorithm

2002 ◽  
pp. 395-413 ◽  
Author(s):  
Dipti Srinivasan ◽  
Irfan Mulyawan Malik
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Maintenance Scheduling ◽  
Practical System

scholarly journals Selection of Scholarship Recipient by Implementing Genetic Algorithm and Fuzzy Logic

2021 ◽  
Vol 1933 (1) ◽  
pp. 012069
Author(s):  
Yohanssen Pratama ◽  
Monalisa Pasaribu ◽  
Joni Nababan ◽  
Dayani Sihombing ◽  
Dicky Gultom
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Selection Of

Application of Improved Genetic Algorithm in Maintenance Decision for Turbine-Generator Unit

2010 ◽  
Vol 44-47 ◽  
pp. 2940-2944
Author(s):  
Qing He ◽  
Jian Ding Zhang
Keyword(s):  
Genetic Algorithm ◽  
Heuristic Algorithms ◽  
Maintenance Scheduling ◽  
Improved Genetic Algorithm ◽  
Turbine Generator ◽  
Executive Process ◽  
Generator Unit ◽  
Maintenance Decision ◽  
Constrained Conditions ◽  
Operating Unit

The complicated function relations are more prone to appear in the maintenance scheduling of steam-turbine generator unit. Many constrained conditions are often attendant with these function relations. In these situations, the traditional method often can not obtain the exact value. The genetic algorithm (GA), a kind of the heuristic algorithms, does not need the function own good analytic properties. In addition, as the operating unit of GA is the group, so it applies to the parallel computing process. In GA executive process, the offspring continually inherit the genes from the parents, so it is more prone to be involved in the local convergence. An improved genetic algorithm is proposed and used in the model of maintenance decision of turbine-generator unit under. The goal of the model is to seek to the rational maintenance scheduling of the generator unit, so as to minimize the sum of the maintenance expense, the loss of the profit on the generated energy, and the loss of the penalty. It is proved by the example that IGA is highly efficient.

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