Modelling and estimation of PLC channel for smart grid systems

Kumar, K Ravi (2014) Modelling and estimation of PLC channel for smart grid systems. MTech thesis.



Today’s power grid system has been from the years and has become old and unable to meet the future needs of this generation. In about a decade, our surroundings been digitalized progressively and we are more reliant on electricity than before.Our power grid ought to advance rapidly to adjust the changes that are occurring in our undeniably computerized society. The best answer for this is Smart Grid.They give a more effective, dependable, environment and secure option to our current grid system. Smart grid will be equipped to restore itself after a power outage or a climate related blackout.Smart grids will depend on several new and different innovative technologies. These new technologies will join together with the current grid to make a more productive, efficient and intelligent grid system. Smart grids will depend incredibly on two-way communications. Employing communication technologies into smart grid is difficult task. There is ongoing research for deciding what should be the best communication for smart grid and PLC creates a great interest because, power lines are everywhere and there is no need of installation cost. Power lines will reach to the last mile but problem with it is the noise and this can be reduced by using techniques like OFDM and allows high data rates of data. This model is a combination of communication model, power line model and noise model. The communication model is realized as the OFDM system, power lines are modelled from the transfer function of multipath signal environment and noise model are modelled as white noise. Radial Basis Function (RBF) networks are used to estimate the channel by using gradient decent method.

Item Type:Thesis (MTech)
Uncontrolled Keywords:Power Line Communication, Smart Grid, OFDM, Radial Basis Function (RBF), Multi Layer Perceptron (MLP), neural networks.
Subjects:Engineering and Technology > Electronics and Communication Engineering > Artificial Neural Networks
Divisions: Engineering and Technology > Department of Electronics and Communication Engineering
ID Code:6331
Deposited By:Hemanta Biswal
Deposited On:09 Sep 2014 15:59
Last Modified:09 Sep 2014 15:59
Supervisor(s):Singh, P

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