Optimisation of power quality in solar/wind power stations using developed artificial bee/ant hybrid heuristic algorithm

Zile, Mehmet

doi:10.23917/arstech.v2i2.442

Cited by 6 publications

(4 citation statements)

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“…In ref. [128], the authors attempt an experiment to eliminate the problems of discontinuous and unstable power generation through an NN energy management system which stores energy based on the difference between the energy generated and the energy required.…”

Section: Algorithmsmentioning

confidence: 99%

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

Kucova,

Prauzek,

Konecny

et al. 2023

CAAI Trans on Intel Tech

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Initiatives to minimise battery use, address sustainability, and reduce regular maintenance have driven the challenge to use alternative power sources to supply energy to devices deployed in Internet of Things (IoT) networks. As a key pillar of fifth generation (5G) and beyond 5G networks,IoT is estimated to reach 42 billion devices by the year 2025. Thermoelectric generators (TEGs) are solid state energy harvesters which reliably and renewably convert thermal energy into electrical energy. These devices are able to recover lost thermal energy, produce energy in extreme environments, generate electric power in remote areas, and power micro‐sensors. Applying the state of the art, the authorspresent a comprehensive review of machine learning (ML) approaches applied in combination with TEG‐powered IoT devices to manage and predict available energy. The application areas of TEG‐driven IoT devices that exploit as a heat source the temperature differences found in the environment, biological structures, machines, and other technologies are summarised. Based on detailed research of the state of the art in TEG‐powered devices, the authors investigated the research challenges, applied algorithms and application areas of this technology. The aims of the research were to devise new energy prediction and energy management systems based on ML methods, create supervised algorithms which better estimate incoming energy, and develop unsupervised and semi‐supervised approaches which provide adaptive and dynamic operation. The review results indicate that TEGs are a suitable energy harvesting technology for low‐power applications through their scalability, usability in ubiquitous temperature difference scenarios, and long operating lifetime. However, TEGs also have low energy efficiency (around 10%) and require a relatively constant heat source.

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Section: Algorithmsmentioning

confidence: 99%

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

Kucova,

Prauzek,

Konecny

et al. 2023

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

show abstract

“…Iron and copper losses must be equal to each other in order to ensure maximum efficiency. If the loss of copper, which is variable, is equal to the loss of constant iron at which load rate, the yield will be maximum at that load [41][42][43][44][45].…”

Section: Power Transformer Efficiency Optimization Using Developed Hy...mentioning

confidence: 99%

“…If it is not, iterations will continue from the beginning. In the algorithm, two parallel arms are combined below to find the best solution [41][42][43][44][45]. In this study, efficiency optimization in different loading conditions for power transformers was realized with the developed cuckoo and flower pollination hybrid algorithm.…”

Section: Power Transformer Efficiency Optimization Using Developed Hy...mentioning

confidence: 99%

Definition of Faults and Efficiency in Power Transformers Using the Algorithm and Computer Program Created by the Heuristic Optimization Methods Developed

Zile

2022

Journal of Engineering Technology and Applied Sciences

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The real-life optimization problems are too complex to be solved by developing a mathematical formula. Heuristic methods are the methods defined to decide the best of a variety of solution actions to solve a problem. Furthermore, it is preferred that heuristic methods are short in solution time and can be applied to different problems. Heuristic methods were developed while trying to find the best solution. The created algorithm, which was based on herd intelligence, was used to solve optimization problems based on the behaviors of bees moving in nature in the process of finding nutrients. By the studies, transformer failures allow us to know without any measurements and tests. The software was developed in C++ programming language by using the created artificial algorithms. The transformer analyses programs have been created by using Microsoft SQL Server 2017 database.

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“…Despite these efforts, limitations in harnessing available power persist, primarily due to environmental conditions, inverter efficiency, and the algorithms that control the direct current to direct current (DC-DC) converters tasked with tracking the maximum power point (MPP) of the system. [4]. The non-linear and dynamic nature of the power-voltage characteristic in PV systems requires sophisticated maximum power point tracking (MPPT) algorithms.…”

mentioning

confidence: 99%

MPPT command enhancement based on an ameliorated grey wolf optimization algorithm for a standalone PV system

2023

IJATEE

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Photovoltaic (PV) systems represent a promising renewable energy technology with the potential to decrease greenhouse gas emissions and mitigate climate change impacts. These systems produce electrical energy by converting solar irradiance. However, despite significant technological advancements, the conversion efficiency of these electrical generators remains relatively low, even under optimal environmental conditions [1-3]. Extensive research has been conducted to minimize losses across all components of PV systems. Despite these efforts, limitations in harnessing available power persist, primarily due to environmental conditions, inverter efficiency, and the algorithms that control the direct current to direct current (DC-DC) converters tasked with tracking the maximum power point (MPP) of the system. [4]. The non-linear and dynamic nature of the power-voltage characteristic in PV systems requires sophisticated maximum power point tracking (MPPT) algorithms. *Author for correspondenceThese algorithms vary based on factors like cost, efficiency, response time, required information, and the ability to track the global maximum power point (GMPP) during partial shading or rapidly changing environmental conditions, as well as the complexity of implementation. Traditional methods, such as Perturb and Observe (P&O), face limitations in convergence speed, oscillations around the MPP, and accuracy, especially when environmental conditions fluctuate [5,6].The current research paper is motivated by the inefficiencies and challenges identified in the existing literature. While various MPPT algorithms have been proposed, they differ significantly in terms of their efficiency, convergence time, complexity, and adaptability to changing environmental conditions. Most notably, they struggle with oscillations and prolonged time to converge to the GMPP, especially in scenarios like partial shading [7]. While the grey wolf optimization (GWO) algorithm demonstrates a promising exploratory nature and quick convergence capabilities, it is susceptible to persistent oscillations Re search Article

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Optimisation of power quality in solar/wind power stations using developed artificial bee/ant hybrid heuristic algorithm

Cited by 6 publications

References 0 publications

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

Thermoelectric energy harvesting for internet of things devices using machine learning: A review

Definition of Faults and Efficiency in Power Transformers Using the Algorithm and Computer Program Created by the Heuristic Optimization Methods Developed

MPPT command enhancement based on an ameliorated grey wolf optimization algorithm for a standalone PV system

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