Hybrid NSGA III/dual simplex approach to generation and transmission maintenance scheduling

Martin, Luis Salinas San; Yang, Jin; Liu, Ying

doi:10.1016/j.ijepes.2021.107498

Cited by 11 publications

(5 citation statements)

References 31 publications

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“…This algorithm does not need additional parameters when applied to practical issues, dramatically reducing the development difficulty. Moreover, NSGA-III can still achieve solution diversity preservation, and solution set convergence near the optimal Pareto front for three or more goals (San Martin et al, 2022). These verify that NSGA-III can achieve solution diversity preservation and solution set convergence near the optimal Pareto front for multiple objectives.…”

mentioning

confidence: 56%

“…(2) For different failure modes of EMAs, the crossover probability pc is 70.0%, and the mutation probability pm is 2.0%. The values of these two parameters are set according to (San Martin et al, 2022).…”

Section: Solving the Multi-objective Optimization Configuration Probl...mentioning

confidence: 99%

“…For different failure modes of EMAs, the step sizes of i j , S , and α are 4.0, g 0 /5, and π /8, respectively.For different failure modes of EMAs, the crossover probability pc is 70.0%, and the mutation probability pm is 2.0%. The values of these two parameters are set according to (San Martin et al. , 2022).…”

Section: Solving the Multi-objective Optimization Configuration Probl...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-objective optimization of bias current coefficient based on NSGA- III for active magnetic bearing with redundant electromagnetic actuators

Deng,

Cheng,

et al. 2024

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PurposeThe multi-objective optimization configuration strategy is proposed due to the configuration of EMAs in fault-tolerant control of active magnetic bearing with redundant electromagnetic actuators involving high-dimensional, nonlinear, conflicting goals.Design/methodology/approachA multi-objective optimization model for bias current coefficients is established based on the nonlinear model of active magnetic bearings with redundant electromagnetic actuators. Based on the non-dominated sorting genetic algorithm III, a numerical method is used to obtain feasible and non-inferior sets for the bias current coefficient.Findings(1) The conflicting relationship among the three optimization objectives was analyzed for various failure modes of EAMs. (2) For different EMAs' failure modes, the multi-objective optimization configuration strategy can simultaneously achieve the optimal or sub-optimal effective EMF, flux margins, and stability of EMF. Moreover, the characteristics of the optimal Pareto front are consistent with the physical properties of the AMB. (3) Compared with the feasible configuration of C0, the non-inferior configurations can significantly improve the performance of AMB, and the advantages of the multi-objective optimization configuration strategy become more prominent as the asymmetry of the residual supporting structure intensifies.Originality/valuei) Considering the variation of the rotor displacement during the support reconstruction, a decision-making model that can accurately characterize the dynamic performance of AMB is presented. (ii) The interaction law between AMB and rotor under different failure modes of EMAs is analyzed, and the configuration principles for redundant EMAs are proposed. (iii) Based on the dynamic characteristics of AMB during the support reconstruction, effective EMF, energy consumption, and the Pearson correlation coefficient between the desired EMFs and the decoupled control currents are used as objective functions. iv. The NSGA-III is combined with the decision-making model to address the multi-objective optimization configuration problem of C0.

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confidence: 56%

Section: Solving the Multi-objective Optimization Configuration Probl...mentioning

confidence: 99%

Section: Solving the Multi-objective Optimization Configuration Probl...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-objective optimization of bias current coefficient based on NSGA- III for active magnetic bearing with redundant electromagnetic actuators

Deng,

Cheng,

et al. 2024

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“…The generation and transmission maintenance scheduling was formulated as a multi-objective optimization model to maximize the system adequacy index, minimize the production costs for electricity generation, and maximize the profit for several GenCos. The problem was solved by the hybrid NSGA III/dual simplex approach [28]. GMS with the uncertainty output of a wind farm was proposed with the objectives of minimizing operation and maintenance costs, minimizing the reserve margin, and maximizing the system flexibility index.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Generation Maintenance Scheduling Considering Emission Factors

Prukpanit,

Kaewprapha,

Leeprechanon

2023

Energies

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Conventional generation maintenance scheduling (GMS) is a solution to increase the reliability of power systems and minimize the operation and maintenance costs paid by generation companies (GenCos). Nonetheless, environmental aspects, such as zero carbon emissions, have attracted global attention, leading to emission costs being paid by electricity generators. Therefore, to obtain GMS plans that consider these factors, this paper proposes multi-objective GMS models to minimize operation, maintenance, and emission costs by using lexicographic optimization as a mathematical tool. A demand response program (DRP) is also adapted to decrease emission generation and operational expenditures. The probability that no generation unit (GU) fails unexpectedly and the average net reserve value, comprising the system reliability with and without considering the GU failure rate, are demonstrated. Numerical examples are implemented for the IEEE 24-bus reliability test system. A GMS algorithm presented in a published work is run and compared to verify the robustness of the proposed GMS models. Our results indicate that this paper provides comprehensive approaches to the multi-objective GMS problem focusing on operation, maintenance, carbon, and DRP costs in consideration of technical and environmental aspects. The use of lexicographic optimization allows for the systematic and hierarchical consideration of these objectives, leading to significant benefits for GenCos.

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