An intelligent management of power flow in the smart grid system using hybrid NPO-ATLA approach

Dsouza, Anil Kumar; Thammaiah, Ananthapadmanabha; Venkatesh, Likith Kumar M.

doi:10.1007/s10462-022-10158-9

Cited by 2 publications

(2 citation statements)

References 53 publications

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“…Therefore, management is future-oriented. It must be managed in combination with scientific prediction, advance quota and other methods to meet the material demand in emergency [11][12].…”

Section: Power Emergency Supplies Reservementioning

confidence: 99%

Construction of electric emergency materials storage system based on BP neural network

Song

Chen

Liu

et al. 2023

International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023)

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The demand forecasting of power supply is an indispensable part of China's energy production and consumption. The scale of power grid construction in China has been expanding and the load growth rate has accelerated. Accidents, equipment failures and personnel unemployment often occur in the power system. BP neural network has certain algorithm advantages. Therefore, in order to ensure the safety and demand of residents and buildings, it is necessary to study the emergency materials storage system of power grid based on BP neural network. This paper mainly uses experimental testing and quantitative analysis methods to study the advantages and methods of BP neural network algorithm in the construction and application of electric emergency materials storage system. The experimental data shows that the learning rate of 0.06 selected by BP neural network is the most appropriate in the construction of storage system.

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“…Therefore, management is future-oriented. It must be managed in combination with scientific prediction, advance quota and other methods to meet the material demand in emergency [11][12].…”

Section: Power Emergency Supplies Reservementioning

confidence: 99%

Construction of electric emergency materials storage system based on BP neural network

Song

Chen

Liu

et al. 2023

International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023)

View full text Add to dashboard Cite

show abstract

“…The complexity of engineering optimization problems is increasing. Classical gradient-based optimization algorithms have a limited ability to solve complex optimization problems using conventional mathematical methods [3][4][5] . Obviously, some traditional methods can be used to solve optimization problems, but they may not yield optimal results.…”

mentioning

confidence: 99%

Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

Chou

Molla

2022

Sci Rep

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The complexity of engineering optimization problems is increasing. Classical gradient-based optimization algorithms are a mathematical means of solving complex problems whose ability to do so is limited. Metaheuristics have become more popular than exact methods for solving optimization problems because of their simplicity and the robustness of the results that they yield. Recently, population-based bio-inspired algorithms have been demonstrated to perform favorably in solving a wide range of optimization problems. The jellyfish search optimizer (JSO) is one such bio-inspired metaheuristic algorithm, which is based on the food-finding behavior of jellyfish in the ocean. According to the literature, JSO outperforms many well-known meta-heuristics in a wide range of benchmark functions and real-world applications. JSO can also be used in conjunction with other artificial intelligence-related techniques. The success of JSO in solving diverse optimization problems motivates the present comprehensive discussion of the latest findings related to JSO. This paper reviews various issues associated with JSO, such as its inspiration, variants, and applications, and will provide the latest developments and research findings concerning JSO. The systematic review contributes to the development of modified versions and the hybridization of JSO to improve upon the original JSO and present variants, and will help researchers to develop superior metaheuristic optimization algorithms with recommendations of add-on intelligent agents.

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An intelligent management of power flow in the smart grid system using hybrid NPO-ATLA approach

Cited by 2 publications

References 53 publications

Construction of electric emergency materials storage system based on BP neural network

Construction of electric emergency materials storage system based on BP neural network

Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

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