ANFIS-Based Control of Multi-objective Grid Connected Inverter and Energy Management

Gupta, Shikha; Garg, Rachana; Singh, Alka

doi:10.1007/s40031-020-00425-0

Cited by 19 publications

(2 citation statements)

References 21 publications

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“…Researchers have improved EMSs by using artificial intelligence techniques such as ANFIS and neural networks (NNs) [1,10,11]. This research aims to minimize hydrogen consumption in fuel cell hybrid electric vehicles through the implementation of intelligent techniques, specifically ANFIS artificial intelligence, in energy management [1,10,12].…”

Section: Introductionmentioning

confidence: 99%

Impact of Artificial Intelligence in Renewable Energy Management of Hybrid Systems

Benhammou

Hamza

Guettaf

et al. 2023

The 1st International Conference on Physics of Semiconductor Devices, Renewable Energies and Environment

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Section: Introductionmentioning

confidence: 99%

Impact of Artificial Intelligence in Renewable Energy Management of Hybrid Systems

Benhammou

Hamza

Guettaf

et al. 2023

The 1st International Conference on Physics of Semiconductor Devices, Renewable Energies and Environment

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“…proposed a PI current controller optimized by ANFIS, which can improve the control of DG. Ref (Gupta et al, 2020) designed a multi-objective inverter based on an ANFIS controller and an overall power management strategy to manage the power transmission between RES, loads, and the grid.…”

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confidence: 99%

Administration strategy of energy management in smart grid: system view and optimization path

Liu,

Wang,

Wei

2023

Front. Energy Res.

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Power generation and transmission infrastructure is vulnerable to the interaction of various Distributed Generations (DG), which leads to the imbalance of power system operation, frequent voltage drops or spikes, and even power outages. This phenomenon not only wastes energy, but also affects grid security. The main reason is a delayed feedback of circuit failure and load changes, and the optimization of energy management system and path is an effective way to solve the above problems. In this paper, a method of multi-objective optimization based on ANFIS algorithm is proposed which can help to improve the demand response, energy storage and management of smart power grid, reduce the volatility of DGs, reducing electricity costs and improving energy efficiency. Firstly, based on the ANFIS algorithm, the distributed power generation control mode, inverter control, real-time electricity price calculation method, energy transfer and storage scheme are improved, and the optimization path of the energy management system is defined. Secondly, the advantages of ANFIS algorithm in response speed and running stability are verified by comparing with other algorithms. Finally, a distributed energy microgrid is constructed for simulation verification. The results show that :(1) ANFIS optimization algorithm has good adaptability in smart grid, and has advantages in large amount of data processing and information transmission; (2) The verification model based on ANFIS has strong elasticity and efficient response speed. The research results will help solve various problems in the smart grid, including establishing a clear energy management system path, maintaining the stable operation of the power system, providing users with more reasonable power plans and the lowest cost of electricity.

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Model and design of an efficient controller for microgrid connected HRES system with integrated DC–DC converters: ATLA-GBDT approach

Vechalapu,

Bhaskara Reddy

2024

Analog Integr Circ Sig Process

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ANFIS-Based Control of Multi-objective Grid Connected Inverter and Energy Management

Cited by 19 publications

References 21 publications

Impact of Artificial Intelligence in Renewable Energy Management of Hybrid Systems

Impact of Artificial Intelligence in Renewable Energy Management of Hybrid Systems

Administration strategy of energy management in smart grid: system view and optimization path

Model and design of an efficient controller for microgrid connected HRES system with integrated DC–DC converters: ATLA-GBDT approach

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