An intellectual procurement innovation of smart grid power system with wireless communication networks based on machine learning

Document Type : Research Paper


1 Department of CSE, Islamiah Institute of Technology, Bangalore, India.

2 Department of EEE, R. M. K College of Engineering and Technology, Chennai, India.

3 Department of ECE, JSS Academy of Technical Education, Bengaluru, India.

4 Department of Mechanical Engineering, Vignan’s Foundation for Science Technology and Research, Andra Pradesh, India.

5 Department of ECE, B V Raju Institute of Technology, Telangana, India.

6 Department of CSE, St.Xavier’s Catholic College of Engineering, Kanyakumari, India.



The phased array antenna is one of the most significant applications in fifth-generation mobile networks. It is one of the most important applications in fifth-generation networks. An electric power source that powers the whole application, including the antenna's root, is required. Even with the most outstanding design, if the programme does not have a sound power supply system with minimal packet loss and can't Path find performance, it will be rendered ineffective. When seen from the perspective of the multiplex information, Machine Learning comprises a communication network based on the Internet that transmits information to the control centre via the objects (IOT). To put it another way, the proposed communication infrastructure, via the provision of, and the chance micro-grid state to collect, analyze, and two-way communication link control information, offers the chance to resolve the voltage regulation issues. This cutting-edge communication infrastructure, as well as a suggested state estimation filter focused on improving speed and performance in renewable energy production, are both examples of creative communication infrastructure. Current research is focused on analyzing and enumerating a range of energy abundances in the context of smart grids, which are now in their fifth generation. Rather than concentrating on the future development plan, which should be a problem of illusion, it should concentrate on the composition of the future potential of smart grid communications framework. An in-depth investigation to give evidence to the Machine learning will help intelligent networks in the future conduct a thorough evaluation.