Control strategy review for hydrogen-renewable energy power system

Hossain, Md. Arafat; Islam, Md. Rashidul; Hossain, Md. Alamgir; Hossain, M. J.

doi:10.1016/j.est.2023.108170

Cited by 39 publications

(5 citation statements)

References 207 publications

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“…These operations require the implementation of various systems that facilitate the connection of fuel cells and electrolysers to the power system. For this purpose, the authors of [12] present the use of machine learning in the form of the adaptive neuro-fuzzy inference system (ANFIS). It is also important to ensure the proper operation and efficiency of energy storage systems for their proper use within the power system.…”

Section: Antecedent Structurementioning

confidence: 99%

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Pijarski,

Belowski

2024

Energies

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The challenges currently faced by network operators are difficult and complex. Presently, various types of energy sources with random generation, energy storage units operating in charging or discharging mode and consumers with different operating characteristics are connected to the power grid. The network is being expanded and modernised. This contributes to the occurrence of various types of network operating states in practice. The appearance of a significant number of objects with random generation in the power system complicates the process of planning and controlling the operation of the power system. It is therefore necessary to constantly search for new methods and algorithms that allow operators to adapt to the changing operating conditions of the power grid. There are many different types of method in the literature, with varying effectiveness, that have been or are used in practice. So far, however, no one ideal, universal method or methodology has been invented that would enable (with equal effectiveness) all problems faced by the power system to be solved. This article presents an overview and a short description of research works available in the literature in which the authors have used modern methods to solve various problems in the field of power engineering. The article is an introduction to the special issue entitled Advances in the Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering. It is an overview of various current problems and the various methods used to solve them, which are used to cope with difficult situations. The authors also pointed out potential research gaps that can be treated as areas for further research.

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Section: Antecedent Structurementioning

confidence: 99%

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Pijarski,

Belowski

2024

Energies

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“…As global consumption of fossil fuels is increasing every day, the reserves of all popular fossil fuels are declining rapidly [1,2]. As a viable and environmentally friendly solution, the popularity of sustainable energy sources, such as wind, solar, hydro, geothermal, and so forth, is steadily increasing [3,4]. Out of all the green solutions available, wind energy stands out as a prominent form of renewable energy due to its abundant availability in nature and its emission-free generation [5,6].…”

Section: Introductionmentioning

confidence: 99%

Transient performance improvement of DFIG‐based wind farm by H‐bridge fault current limiter

Hossain,

Hasan,

Upadhay

et al. 2024

Applied Research

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Despite the unique advantages a doubly fed induction generator (DFIG) offers to the grid‐integrated renewable energy systems, they have a limitation of being susceptible to grid fault as their stator windings are directly connected to the grid. The fault current limiters (FCLs) provide a sustainable solution by enhancing the fault ride‐through capability and thus it improve the transient performance of a DFIG. In this work, a multi‐inductor‐based H‐bridge fault current limiter (HBFCL) is proposed to augment the transient performance of a DFIG. The operational efficacy of the HBFCL is evaluated through the administration of both symmetrical and asymmetrical fault scenarios. The effectiveness of the HBFCL is further investigated by comparing the performance of the HBFCL with that of the bridge‐type series dynamic braking resistor (BSDBR). Both the graphical and numerical interpretations of the simulation result assert that the HBFCL improves the transient performance of a DFIG‐based wind farm and outweighs the performance of the BSDBR in all aspects.

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“…In this context, optimizing the control system becomes paramount becomes very important to ensure that the SES can achieve efficient, stable, and sustainable operation under changing conditions [9][10][11]. With the wide application of renewable energy technology, people have witnessed the vigorous development of solar photovoltaic and wind power generation [12,13]. Nonetheless, the characteristics of these technologies, particularly the influence of weather changes on solar and wind energy productivity, have greatly reduced the predictability of energy, thus introducing new complexity and uncertainty [14].…”

Section: Introductionmentioning

confidence: 99%

Application of Fuzzy Control and Neural Network Control in the Commercial Development of Sustainable Energy System

Xie,

Guan,

Peng

et al. 2024

Sustainability

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Sustainable energy systems (SESs) occupy a prominent position in the modern global energy landscape. The purpose of this study is to explore the application of fuzzy control and neural network control in photovoltaic systems to improve the power generation efficiency and stability of the system. By establishing the mathematical model of a photovoltaic system, the nonlinear and uncertain characteristics of photovoltaic system are considered. Fuzzy control and neural network control are used to control the system, and their performance is verified by experiments. The experimental results show that under the conditions of low light and moderate temperature, the fuzzy neural network control achieves a 3.33% improvement in power generation efficiency compared with the single control strategy. Meanwhile, the system can still maintain relatively stable operation under different environmental conditions under this comprehensive control. This shows that fuzzy neural network control has significant advantages in improving power generation efficiency and provides beneficial technical support and guidance for the commercial development of SESs.

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Control strategy review for hydrogen-renewable energy power system

Cited by 39 publications

References 207 publications

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Transient performance improvement of DFIG‐based wind farm by H‐bridge fault current limiter

Application of Fuzzy Control and Neural Network Control in the Commercial Development of Sustainable Energy System

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