Applied Research on Distributed Generation Optimal Allocation Based on Improved Estimation of Distribution Algorithm

Yang, Libo; Yang, Xiaohui; Wu, Yue; Liu, Peter

doi:10.3390/en11092363

Cited by 8 publications

(8 citation statements)

References 28 publications

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“…Using the roulette wheel selection technique, the fittest individuals are more likely to be chosen to establish new solutions through crossovers and mutations. GA is a flexible and robust algorithm [45], but it is complex [45,46], sensitive to the value of parameters [45], and dependent on the initial population [46,47]. PSO is an algorithm that simulates bird/fish foraging behavior, and it was developed by Kennedy and Eberhart (1995) [48].…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

“…SA is a flexible algorithm [53] with the capacity to avoid the local optima trap by accepting worse solution [45]. However, slow convergence is required in SA to obtain the actual optimal solution [45,46,54]. In addition, it is a single solution-based algorithm, where only one solution is generated and optimized from a local search [45].…”

Section: Simulated Annealing (Sa)mentioning

confidence: 99%

“…with the capacity to avoid the local optima trap by accepting worse solution [45]. However, slow convergence is required in SA to obtain the actual optimal solution [45,46,54].…”

Section: Simulated Annealing (Sa)mentioning

confidence: 99%

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An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets

Alluhaybi,

Psimoulis,

Remenyte-Prescott

2024

Remote Sensing

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Continuous advancements in GNSS systems have led, apart from the broadly used GPS, to the development of other satellite systems (Galileo, BeiDou, GLONASS), which have significantly increased the number of available satellites for GNSS positioning applications. However, despite GNSS satellites’ redundancy, a potential poor GNSS satellite signal (i.e., low signal-to-noise ratio) can negatively affect the GNSS’s performance and positioning accuracy. On the other hand, selecting high-quality GNSS satellite signals by retaining a sufficient number of GNSS satellites can enhance the GNSS’s positioning performance. Various methods, including optimization algorithms, which are also commonly adopted in artificial intelligence (AI) methods, have been applied for satellite selection. In this study, five optimization algorithms were investigated and assessed in terms of their ability to determine the optimal GNSS satellite constellation, such as Artificial Bee Colony optimization (ABC), Ant Colony Optimization (ACO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). The assessment of the optimization algorithms was based on two criteria, such as the robustness of the solution for the optimal satellite constellation and the time required to find the solution. The selection of the GNSS satellites was based on the weighted geometric dilution of precision (WGDOP) parameter, where the geometric dilution of precision (GDOP) is modified by applying weights based on the quality of the satellites’ signal. The optimization algorithms were tested on the basis of 24 h of tracking data gathered from a permanent GNSS station, for GPS-only and multi-GNSS data (GPS, GLONASS, and Galileo). According to the comparison results, the ABC, ACO, and PSO algorithms were equivalent in terms of selection accuracy and speed. However, ABC was determined to be the most suitable algorithm due it requiring the fewest number of parameters to be set. To further investigate ABC’s performance, the method was applied for the selection of an optimal GNSS satellite subset according to the number of total available tracked GNSS satellites (up to 31 satellites), leading to more than 300 million possible combinations of 15 GNSS satellites. ABC was able to select the optimal satellite subsets with 100% accuracy.

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Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Section: Simulated Annealing (Sa)mentioning

confidence: 99%

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An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets

Alluhaybi,

Psimoulis,

Remenyte-Prescott

2024

Remote Sensing

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“…It is most complex, easy to fail into premature convergence, It always depends on the initial population [19].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Review on Chronic Obstructive Pulmonary Disease Prediction System using Wheeze and Crackle.

Alwis¹

2021

Preprint

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<div> <div> <div> <p>As of today, Chronic Obstructive Pulmonary Disease (COPD) is a common lung disease in the community. Getting an accurate diagnosis is the most important step in treating a patient, allowing doctors to find the best treatment for their condition. Luckily, in today's World, Machine learning is making healthcare smarter. It's a complex process that requires human effort and time. The paper presents a better application design to identify this disease easily. The main aim is to predict that COPD patients can be performed with high accuracy according to respiratory sound reports. The latest technology will be used to achieve this goal through deep learning and Audio/speech processing. With this implementation of this research, it is hoped to make an accurate prediction of COPD and make it readily available to the patient. </p> </div> </div> </div>

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“…However, it has a low convergence rate and a low precision of optimization. The HS algorithm could not directly deal with constraints, thus a variety of constraint-handling approaches should be employed to help with the optimization process [ 13 , 21 ]. Furthermore, the PSO method [ 5 , 6 , 22 ], is utilized to optimize the design parameters of the grounding system, however the method has difficulty with discrete optimizations.…”

Section: Introductionmentioning

confidence: 99%

Optimization of substation grounding grid design for horizontal and vertical multilayer and uniform soil condition using Simulated Annealing method

et al. 2021

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Grounding systems are critical in safeguarding people and equipment from power system failures. A grounding system’s principal goal is to offer the lowest impedance path for undesired fault current. Optimization of the grounding grid designs is important in satisfying the minimum cost of the grounding system and safeguarding those people who work in the surrounding area of the grounded installations. Currently, there is no systematic guidance or standard for grounding grid designs that include two-layer soil and its effects on grounding grid systems, particularly vertically layered soil. Furthermore, while numerous studies have been conducted on optimization, relatively limited study has been done on the problem of optimizing the grounding grid in two-layer soil, particularly in vertical soil structures. This paper presents the results of optimization for substation grounding systems using the Simulated Annealing (SA) algorithm in different soil conditions which conforms to the safety requirements of the grounding system. Practical features of grounding grids in various soil conditions discussed in this paper (uniform soil, two-layer horizontal soil, and two-layer vertical soil) are considered during problem formulation and solution algorithm. The proposed algorithm’s results show that the number of grid conductors in the X and Y directions (Nx and Ny), as well as vertical rods (Nr), can be optimized from initial numbers of 35% for uniform soil, 57% for horizontal two-layer soil for ρ1> ρ2, and 33% for horizontal two-layer soil for ρ1< ρ2, and 29% for vertical two-layer soil structure. In other words, the proposed technique would be able to utilize square and rectangle-shaped grounding grids with a number of grid conductors and vertical rods to be implemented in uniform, two-layer horizontal and vertical soil structure, depending on the resistivity of the soil layer.

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Applied Research on Distributed Generation Optimal Allocation Based on Improved Estimation of Distribution Algorithm

Cited by 8 publications

References 28 publications

An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets

An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets

Review on Chronic Obstructive Pulmonary Disease Prediction System using Wheeze and Crackle.

Optimization of substation grounding grid design for horizontal and vertical multilayer and uniform soil condition using Simulated Annealing method

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