A Tri-Objective Model for Generator Maintenance Scheduling

Moyo, Learnmore; Nwulu, Nnamdi; Ekpenyong, Uduakobong Edet; Bansal, Ramesh C.

doi:10.1109/access.2021.3112157

Cited by 9 publications

(5 citation statements)

References 33 publications

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“…In addition, there is an environmental criterion, which has recently received attention when solving GMS problems [ 26 , 27 ]. Risk is another criterion that is also considered [ 28 , 29 ]. These criteria can be considered as objective functions or constraints.…”

Section: Related Literaturementioning

confidence: 99%

“…In addition [ 27 ] consider environmental criteria through minimizing CO 2 emissions. [ 28 ] consider three objectives, i.e., reliability maximization by minimizing the sum of squared reserves, minimizing operational cost consisting of maintenance cost, start-up cost and cost of generation and, lastly, minimizing the risk of generating unit fails before scheduled maintenance periods. [ 39 ] consider two objectives including expected total maintenance cost and risk criteria which are considered by lowering failure intensity.…”

Section: Related Literaturementioning

confidence: 99%

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Tri-objective generator maintenance scheduling model based on sequential strategy

Muthana¹,

Ku-Mahamud

2022

PLoS ONE

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A multi-objective modeling approach is required in the context of generator maintenance scheduling (GMS) for power generation systems. Most multi-objective modeling approaches in practice are modeled using a periodic system approach that caters for a fixed maintenance window. This approach is not suitable for different types of generating units and cannot extend the generator lifespan. To address this issue, this study proposes a tri-objective GMS model with three conflicting objectives based on the sequential system approach that accounts for operating hours and start-up times. The GMS model’s objectives are to minimize the total operation cost, maximize system reliability and minimize violation. The main difference between the proposed tri-objective GMS model and other multi-objective GMS models, is that the proposed model uses a sequential strategy based on operating hours and start-up times. In addition, the proposed model has considered the most important criteria in scheduling the generator maintenance, and this reflects the real-life requirements in electrical power systems. A multi-objective graph model is also developed to generate the maintenance units scheduling and used in developing the proposed Pareto ant colony system (PACS) algorithm. A PACS algorithm is proposed to implement the model and obtain solution for GMS. The performance of the proposed model was evaluated using the IEEE RTS 26, 32, and 36-unit systems dataset. The performance metrics used comprise the GMS model objectives. The experimental results showed that the obtained solution from the proposed tri-objective GMS model was a robust solution by considering the different initial operational hours of the units.

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Section: Related Literaturementioning

confidence: 99%

Section: Related Literaturementioning

confidence: 99%

Tri-objective generator maintenance scheduling model based on sequential strategy

Muthana¹,

Ku-Mahamud

2022

PLoS ONE

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“…In terms of theoretical research, in order to solve the problem of incomplete maintenance cycle of storage unit and lack of data and theoretical support for repair, quite a number of scholars have done a lot of research combined with modern maintenance theory. Literature [1] established a unit maintenance optimization model with 3 objective functions on the basis of considering constraints such as load, maintenance time window and maintenance duration. The minimum square variance of unit maintenance cost, failure risk and spare is considered, but it is not suitable for unit maintenance in largescale power system.…”

Section: Introductionmentioning

confidence: 99%

Maintenance strategy of storage unit based on timing measurement and fault cost fusion

Gong,

Cheng,

et al. 2024

International Conference on Optoelectronic Materials and Devices (ICOMD 2023)

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“…The reliability requirement has been defined in a variety of ways, including the sum of squared reserves which is the most common reliability criterion and loss of load probability predicted energy not supplied. The objective function is formulated in equation (1) [8]:…”

Section: Introductionmentioning

confidence: 99%

Single Objective Optimization Methods in Electrical Power Systems: A Review

Al-kharaz¹,

A.Wahhab²,

Ibrahim³

et al. 2023

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Although the scheduling of maintenance tasks for generators is not a new issue, it has recently attracted new attention due to the significant rise in demand for expanding power system size in modern power systems. Generator Maintenance Scheduling (GMS) is a nonlinear optimization problem, highly dimensional and constrained, and determines when power-producing units must undertake well-planned preventative maintenance. The objective function includes binary variables to indicate whether a generator is undergoing maintenance at a given time and is subject to several restrictions described in this paper. However, the biggest concern of GMS is to produce a precise timetable for preventive maintenance of generating units with low cost and high reliability. Despite that, regrettably, a large volume of research works has accomplished solutions towards a model of GMS with the consideration of either maximizing system reliability or minimizing operation costs as an objective of their research work. This is called Single-Objective Problem (SOP), which involves one objective function that needs to be optimized. SOP is solved by Single-Objective Optimization Method (SOOM). The primary purpose of the research is to present a review of SOOM methods used in solving GMS problems.

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A Tri-Objective Model for Generator Maintenance Scheduling

Cited by 9 publications

References 33 publications

Tri-objective generator maintenance scheduling model based on sequential strategy

Tri-objective generator maintenance scheduling model based on sequential strategy

Maintenance strategy of storage unit based on timing measurement and fault cost fusion

Single Objective Optimization Methods in Electrical Power Systems: A Review

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