This paper presents the design and implementation of a low-cost Supervisory Control and Data Acquisition system based on a Web interface to be applied to a Hybrid Renewable Energy
Control of a microgrid is a complex task and requires sophisticated communication and monitoring for reliable operation. This paper presents a microgrid specific low-cost data acquisition system
The data acquisition module including filter algorithm and signal modulation circuit uses the digital signal processor (DSP) as the main processor, it can realize accurate real-time data
Efficient data acquisition and communication infrastructure are crucial for seamless data exchange. • Microgrids: Microgrids are small-scale power systems that can operate independently or
configuration. However, data delivery for microgrid communication network via the traditional TCP/IP and protocols is inefficiently performed. During the past three decades, Usage in
Microgrid (MG) technologies offer users attractive characteristics such as enhanced power quality, stability, sustainability, and environmentally friendly energy through a control and Energy
Communication of all generation and consumption units in a DC microgrid is very important in terms of system control. Network applications state that DC microgrid and smart grid communication systems must abide by reliability, latency, bandwidth, and security requirements.
A DC microgrid's performance in terms of power-sharing, MPPT, protection, online system monitoring, stability, and reliability will be enhanced by the addition of a communication network. Different control strategies are available for DC microgrid communication systems to enhance performance.
DC microgrids have attracted many researchers’ attention because of their numerous advantages. To fulfill the expectations, a DC microgrid should be designed based on proper architecture and employ an advanced control algorithm and communication network.
Microgrid control strategies, which have a very important effect on the performance of the microgrid system and make the microgrid more stable and reliable, are explained in detail. Emerging communication technologies for DC microgrids are explained, and machine learning techniques in DC microgrids are discussed in light of new developments.
For the energy management system of a microgrid system to be used most effectively and efficiently, all factors such as fuel costs, heat/energy conversion requirements and demand side preferences should be well analyzed, and optimum energy planning of distributed generators should be optimum be realized.
Advancements in DC Microgrids: Integrating Machine Learning and Communication Technologies for a Decentralized Future. In: Appasani, B., Bizon, N. (eds) Smart Grid 3.0.
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