Multi-Objective Economic Scheduling of a Shipboard Microgrid Based on Self-Adaptive Collective Intelligence DE Algorithm

Feng, Jinhong; Zhang, Jundong; Wang, Chuan; Jiang, Ruizheng; Xu, M. Y.

doi:10.1109/access.2020.2988530

Cited by 8 publications

(3 citation statements)

References 72 publications

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“…The PSO algorithm is introduced into the DE algorithm. (30) On the one hand, it improves the convergence speed; on the other hand, it improves the probability of convergence to the global optimal value and ensures the accuracy and stability of the detection results. This intelligent optimization algorithm has the advantages of fast convergence, high probability, and good robustness.…”

Section: Mathematical Model Of Flatnessmentioning

confidence: 99%

An Intelligent Vision Mobile Measurement Approach for High-speed Inspection of Large-size Flatness

Qiu¹,

Xiao²,

Wan³

et al. 2022

Sensors and Materials

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In current science and engineering, the demand for large-size surface detection has increased considerably. However, large-size surface detection presents some challenges, such as very large detection target area, discontinuous detection surface, and low detection accuracy. The current detection methods are mainly based on in-position detection and cannot meet the requirements of detection speed and accuracy for large-size surface detection. In this paper, we propose a fast detection method for large-scale flatness based on an intelligent vision mobile platform (IVMP). Specifically, by establishing the path optimization model, beam adjustment model, and largescale flatness calculation model for the IVMP, the binocular vision acquisition of large-scale target information and fast large-scale shape detection are realized. The rigidly fixed position relation of binocular vision is considered, and the parameters of the main camera can be obtained through an error equation, then the parameters of the assistant camera can be acquired quickly to calculate the three-dimensional coordinates of space points. The particle swarm optimization algorithm is integrated into the differential evolution algorithm to improve the detection speed. The IVMP is applied to the flatness detection of a satellite antenna. The experimental results show that the detection precision and efficiency of the IVMP are clearly higher than those of the laser tracking and theodolite systems.

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Section: Mathematical Model Of Flatnessmentioning

confidence: 99%

An Intelligent Vision Mobile Measurement Approach for High-speed Inspection of Large-size Flatness

Qiu¹,

Xiao²,

Wan³

et al. 2022

Sensors and Materials

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show abstract

“…PSO [29] is a kind of swarm intelligence optimization algorithm, which imitates the birds' foraging. It has been widely used in various applications, such as controller design [30], [31], optimal scheduling [32], [33], to name a few. It first initializes a group of particles in the solution space, each of which represents a potential optimal solution.…”

Section: B Particle Swarm Optimizationmentioning

confidence: 99%

Detecting and Characterizing Multiple Oscillations in Process Control Loop Based on PSO-NCMD

et al. 2020

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Oscillations are a common abnormal phenomenon in the process control system. They can degrade control performance or even cause plant shutdown. It is crucial to accurately detect and characterize the process oscillations. In this paper, a novel oscillation detector is proposed by combining the particle swarm optimization (PSO) and nonlinear chirp mode decomposition (NCMD). Because the performance of NCMD relies on the selection of mode number Q and bandwidth parameter α, PSO is utilized to search the optimal parameter pairs. Then, the multiple oscillations contained in the process variables (PV) can be extracted by NCMD with the optimal parameters. The normalized correlation index and sparseness index are used to discarding the spurious modes and quantifying the degree of oscillations, respectively. After detecting, by utilizing the time-frequency information provided by the oscillating modes, multiple oscillation types can be accurately characterized. Comparisons are provided to show the advantages of the proposed method. The effectiveness and utility are validated by simulations as well as various industrial cases. INDEX TERMS Oscillation detection, nonlinear chirp mode decomposition, particle swarm optimization, control performance assessment.

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“…The Multi-objective optimization dispatch (MOOD) is a basic problem in MG system operation. And various objective functions and models have been discussed and developed in the application of the MG such as: minimize the operating cost, minimize the pollutant gas emissions, minimize the cycle degradation of the battery storage system (Feng et al 2020), minimize the power loss of the system (Zhang et al 2020), maximize the comfort and the economic benefit of the end users by including demand side management approach (Lokeshgupta and Sivasubramani. 2018) in the formulated problem.…”

Section: Introductionmentioning

confidence: 99%

Solving Multi-Objective Energy Management of a DC Microgrid using Multi-Objective Multiverse Optimization

Lagouir

Badri

Sayouti

2021

Int. J. Renew. Energy Dev.

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This paper deals with the multi-objective optimization dispatch (MOOD) problem in a DC microgrid. The aim is to formulate the MOOD to simultaneously minimize the operating cost, pollutant emission level of (NOx, SO2 and CO2) and the power loss of conversion devices. Taking into account the equality and inequality constraints of the system. Two approaches have been adopted to solve the MOOD issue. The scalarization approach is first introduced, which combines the weighted sum method with price penalty factor to aggregate objective functions and obtain Pareto optimal solutions. Whilst, the Pareto approach is based on the implementation of evolutionary multi-objective optimization solution. Single and multi-objective versions of multi-verse optimizer algorithm are, respectively, employed in both approaches to handle the MOOD. For each time step, a fuzzy set theory is selected to find the best compromise solution in the Pareto optimal set. The simulation results reveal that the Pareto approach achieves the best performances with a considerable decrease of 28.96 $/day in the daily operating cost, a slight reduction in the power loss of conversion devices from 419.79 kWh to 419.29 kWh, and in less computational time. While, it is noticing a small increment in the pollutant emission level from 11.54 kg/day to 12.21 kg/day, for the daily microgrid operation. This deviation can be fully covered when comparing the cost related to the treatment of these pollutants, which is only 5.55 $/day, to the significant reduction in the operating cost obtained using the Pareto approach.

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Multi-Objective Economic Scheduling of a Shipboard Microgrid Based on Self-Adaptive Collective Intelligence DE Algorithm

Cited by 8 publications

References 72 publications

An Intelligent Vision Mobile Measurement Approach for High-speed Inspection of Large-size Flatness

An Intelligent Vision Mobile Measurement Approach for High-speed Inspection of Large-size Flatness

Detecting and Characterizing Multiple Oscillations in Process Control Loop Based on PSO-NCMD

Solving Multi-Objective Energy Management of a DC Microgrid using Multi-Objective Multiverse Optimization

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