scholarly journals Preventive Maintenance Scheduling for Multicogeneration Plants with Production Constraints Using Genetic Algorithms

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Khaled Alhamad ◽  
Mohsen Alardhi ◽  
Abdulla Almazrouee
Keyword(s):  
Energy Management ◽  
Preventive Maintenance ◽  
Time Horizon ◽  
Maintenance Scheduling ◽  
The Other ◽  
Scheduling Problem ◽  
Decision Tool ◽  
Production Constraints ◽  
Effective Decision ◽  
Maintenance Schedules

This paper describes a method developed to schedule the preventive maintenance tasks of the generation and desalination units in separate and linked cogeneration plants provided that all the necessary maintenance and production constraints are satisfied. The proposed methodology is used to generate two preventing maintenance schedules, one for electricity and the other for distiller. Two types of crossover operators were adopted, 2-point and 4-point. The objective function of the model is to maximize the available number of operational units in each plant. The results obtained were satisfying the problem parameters. However, 4-point slightly produce better solution than 2-point ones for both electricity and water distiller. The performance as well as the effectiveness of the genetic algorithm in solving preventive maintenance scheduling is applied and tested on a real system of 21 units for electricity and 21 units for water. The results presented here show a great potential for utility applications for effective energy management over a time horizon of 52 weeks. The model presented is an effective decision tool that optimizes the solution of the maintenance scheduling problem for cogeneration plants under maintenance and production constraints.

Economic and Reliable Preventive Maintenance Scheduling in Power Systems by Using Binary Crow Search Algorithm

2021 ◽  
Vol 56 ◽  
pp. 182-198
Author(s):  
Adel A. Abou El Ela ◽  
Ragab El-Sehiemy ◽  
Abdullah M. Shaheen ◽  
Ayman S. Shalaby
Keyword(s):  
Power System ◽  
Preventive Maintenance ◽  
Search Algorithm ◽  
Economic Cost ◽  
Economic Benefits ◽  
Test System ◽  
Optimization Techniques ◽  
Maintenance Scheduling ◽  
Scheduling Problem ◽  
Matlab Programming

The generation system is an important part of the power system. The problem of generator maintenance scheduling is provided to construct optimal generators preventive maintenance schedules. It aims to improve economic benefits and achieve reliable operation of the power system while satisfying the system and maintenance constraints. In this paper, the binary crow search algorithm is proposed for solving the scheduling problem. This model would schedule maintenance scheme and commitment status of generating units while the objective functions are achieved. The crow search algorithm is a new meta-heuristic optimizer, which has its implementation very simple and easy compared to other optimization techniques. To verify the robustness and effectiveness of the proposed binary crow search optimizer, three test systems namely 6–unit, 21–unit system, and IEEE reliability test system are considered over the planning horizon of 52 weeks. The proposed optimizer is implemented in the MATLAB programming environment. Techno-economic aspects are considered for the generator's maintenance scheduling problem as reliability enhancement economic cost-minimizing issues. The proposed binary crow search optimizer is developed for single and multi-objective frameworks. The simulation results show the proposed binary crow search technique effectiveness and feasibility compared with previously optimizer in solving the generators maintenance scheduling problem with better convergence rate.

A modularized framework for predictive maintenance scheduling

2011 ◽  
Vol 226 (4) ◽  
pp. 380-391 ◽  
Author(s):  
Ming-Yi You ◽  
Guang Meng
Keyword(s):  
Preventive Maintenance ◽  
Industrial Applications ◽  
Maintenance Scheduling ◽  
Predictive Maintenance ◽  
Scheduling Model ◽  
Component Systems ◽  
Maintenance Policies ◽  
The Individual ◽  
Maintenance Schedules ◽  
Dynamic Updating

This paper presents a modularized, easy-to-implement framework for predictive maintenance scheduling. With a modularization treatment of a maintenance scheduling model, a predictive maintenance scheduling model can be established by integrating components’ real-time, sensory-updated prognostics information with a classical preventive maintenance/condition-based maintenance scheduling model. With the framework, a predictive maintenance scheduling model for multi-component systems is established to illustrate the framework’s use; such a predictive maintenance scheduling model for multi-component systems has not been reported previously in the literature. A numerical example is provided to investigate the individual-orientation and dynamic updating characteristics of the optimal preventive maintenance schedules of the established predictive maintenance scheduling model and to evaluate the performance of these preventive maintenance schedules. It is hoped that the presented framework will facilitate the implementation of predictive maintenance policies in various industrial applications.

scholarly journals Annual preventive maintenance scheduling for thermal units in an electric power system

2010 ◽  
Vol 20 (2) ◽  
pp. 261-273
Author(s):  
Rodoljub Tonic ◽  
Milan Rakic
Keyword(s):  
Power System ◽  
Electric Power ◽  
Preventive Maintenance ◽  
Large Scale ◽  
Simulation Method ◽  
Electric Power System ◽  
Maintenance Scheduling ◽  
Successive Approximations ◽  
Reliability Indices ◽  
Maintenance Schedules

The system approach to the problem of preventive maintenance scheduling for thermal units in a large scale electric power system is considered in this paper. The maintenance scheduling program determines a set of thermal units maintenance switch off for a time period of one year. This paper considers the application of dynamic programming and successive approximations method in determination of annual thermal unit maintenance schedules. The objective function is multiple component and consists of system operation costs and system reliability indices (loss-of-load-probability and expected unserved energy). The evaluation of these costs is performed through a simulation method which uses a cumulant load model. The software package, developed in FORTRAN and integrated with an ORACLE data base, produces many useful outputs.

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