Lifetime Prediction of Mica Paper Capacitors Based on an Improved Iterative Grey–Markov Chain Model

Liu, Shifei; Zhang, Jiande; Zhang, Zicheng

doi:10.1109/jestpe.2022.3175457

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…The Grey-Markov (GM) model for train operation information based on the time series is proposed to analyze the power flow in TPSS in advance [35,36], which helps to search for the optimal solution to ξ t [i] at a discrete time. The Grey-Markov model takes as input a train history dataset obtained from ATS, with a time length of T seconds.…”

Section: The Fast Prediction Model Of Train Operation Informationmentioning

confidence: 99%

Section: Analysis Of Prediction Model Accuracymentioning

confidence: 99%

“…Here, the posterior error test method is used to analyze the feasibility of the current prediction [35,36]; the historical train current for a certain period on Guangzhou Metro Line 14 is shown in Figure 5. A 30 s period is used for the sliding predicted window for I0 t (t = 1:30), and the pre diction of traction current and braking current is shown in Figure 6.…”

Section: Analysis Of Prediction Model Accuracymentioning

confidence: 99%

See 2 more Smart Citations

Hierarchical Operation Optimization for Regenerative Braking Energy Utilizing in Urban Rail Traction Power Supply System

Zhang,

Zhou

et al. 2023

Energies

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The energy feedback system (EFS) is widely accepted to utilize the regenerative braking energy (RBE) in an urban rail traction power supply system (TPSS). However, the sharing relationship of RBE between EFS, traction trains and on-board braking resistors is not clear. In addition, the impact of EFS operation on the sharing of RBE has been rarely studied. This paper proposed a hierarchical operation optimization method for improving the utilization of shared RBE in TPSS through the EFS. An optimizing model for the dynamic start-up voltage threshold of EFS is established, with the objective of minimizing TPSS power consumption. A fast prediction model of train operation information is developed to analyze the steady-state power flow in advance. The optimal solution is searched using a salp swarm algorithm (SSA) on a per second basis. A microsystem of three traction stations and two trains is analyzed. Compared to the conventional constant voltage operation scheme, the optimal solution achieves a maximum additional energy-saving efficiency improvement of 2.44%. Efficient sharing of RBE is identified as the key to achieving energy savings. Regarding the local control part, system stability analysis is verified. Real-time simulation results indicate that the dynamic operating mode of EFS efficiently distributes RBE.

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Section: The Fast Prediction Model Of Train Operation Informationmentioning

confidence: 99%

Section: Analysis Of Prediction Model Accuracymentioning

confidence: 99%

Section: Analysis Of Prediction Model Accuracymentioning

confidence: 99%

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Hierarchical Operation Optimization for Regenerative Braking Energy Utilizing in Urban Rail Traction Power Supply System

Zhang,

Zhou

et al. 2023

Energies

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Enriched nonlinear grey compositional model for analyzing multi-trend mixed data and practical applications

Li,

Xie,

2024

Applied Mathematical Modelling

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Evolutionary Optimization for the Belief-Rule-Based System: Method and Applications

Qi¹,

Chang

Fan

et al. 2022

Symmetry

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Evolutionary optimization (EO) has been proven to be highly effective computation means in solving asymmetry problems in engineering practices. In this study, a novel evolutionary optimization approach for the belief rule base (BRB) system is proposed, namely EO-BRB, by constructing an optimization model and employing the Differential Evolutionary (DE) algorithm as its optimization engine due to its ability to locate an optimal solution for problems with nonlinear complexity. In the EO-BRB approach, the most representative referenced values of the attributes which are pre-determined in traditional learning approaches are to be optimized. In the optimization model, the mean squared error (MSE) between the actual and observed data is taken as the objective, while the initial weights of all the rules, the beliefs of the scales in the conclusion part, and the referenced values of the attributes are taken as the restraints. Compared with the traditional learning approaches for the BRB system, the EO-BRB approach (1) does not require transforming the numerical referenced values of the attributes into linguistic terms; (2) does not require identifying any initial solution; (3) does not require any mathematical deduction and/or case-specific information which verifies it as a general approach; and (4) can help downsize the BRB system while producing superior performances. Thus, the proposed EO-BRB approach can make the best use of the nonlinear modeling ability of BRB and the optimization superiority of the EO algorithms. Three asymmetry numerical and practical cases are studied to validate the efficiency of the proposed EO-BRB approach.

show abstract

Lifetime Prediction of Mica Paper Capacitors Based on an Improved Iterative Grey–Markov Chain Model

Cited by 3 publications

References 15 publications

Hierarchical Operation Optimization for Regenerative Braking Energy Utilizing in Urban Rail Traction Power Supply System

Hierarchical Operation Optimization for Regenerative Braking Energy Utilizing in Urban Rail Traction Power Supply System

Enriched nonlinear grey compositional model for analyzing multi-trend mixed data and practical applications

Evolutionary Optimization for the Belief-Rule-Based System: Method and Applications

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