review · Neural Computing and Applications
Hybrid alternating current (AC) and direct current (DC) distribution networks represent an important evolution in smart grid and decentralised power systems. Operating these hybrid systems effectively requires targeted approaches to energy management, control, system stability, and protection. Neural network optimisation methods are central to these systems, enabling the integration of multiple energy sources, dynamic energy management, energy storage coordination, and online fault detection and diagnosis. Implementing these computational techniques can lower costs and increase the uptake of renewable energy generation. Compared to conventional microgrid designs, AC-DC coupled configurations provide distinct performance benefits. Interlinking converters play a critical role in this architecture by facilitating efficient power transmission between the AC and DC microgrid sections and hosting the necessary control and optimisation routines.
Modern electrical power systems increasingly rely on combining alternating and direct current sources, especially with the growth of renewable power. Understanding the protection, control, and optimisation of hybrid AC/DC networks helps grid designers minimise energy conversion losses, prevent disruptive system faults, and improve the overall reliability and affordability of decentralised electricity supplies.
The insights apply to microgrid developers, power conversion equipment manufacturers, and utilities seeking to improve hybrid AC/DC grid operations. Because this is a review synthesising network topologies, converter roles, and neural network algorithms, it represents early-stage conceptual and analytical guidance rather than a market-ready product, serving as a framework for future engineering trials and control software development.
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Abstract The introduction of hybrid alternating current (AC)/direct current (DC) distribution networks led to several developments in smart grid and decentralized power system technology. The paper concentrates on several topics related to the operation of hybrid AC/DC networks. Such as optimization methods, control strategies, energy management, protection issues, and proposed solutions. The implementation of neural network optimization methods has great importance for the successful integration of multiple energy sources, dynamic energy management, establishment of system stability and reliability, power distribution optimization, management of energy storage, and online fault detection and diagnosis in hybrid networks like the hybrid AC–DC microgrids (MG). Taking advantage of renewable energy generation and cost-cutting through the neural network optimization technique holds the key to these progressions. Besides identifying the challenges in the operation of a hybrid system, the paper also compares this system to conventional MGs and shows the benefits of this type of system over different MG structures. This review compares the different topologies, particularly looking at the AC–DC coupled hybrid MGs, and shows the important role of the interlinking of converters that are used for efficient transmission between AC and DC MGs and generally used to implement the different control and optimization techniques. Overall, this review paper can be regarded as a reference, pointing out the pros and cons of integrating hybrid AC/DC distribution networks for future study and improvement paths in this developing area .
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DOI: 10.1007/s00521-024-10264-5
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