Smart Grid (SG) technology transforms the traditional power network from a physical level into systems that comprise two levels side by side, the physical and the informative. Collecting, transferring and analyzing an enormous amount of data that can be acquired by different nodes in the network, together with the uncertainty caused by distributed power generators, challenges standard methods for safety and monitoring in future SGs.
This thesis presents an effective dynamic solution for SG monitoring by combining concepts related to Compressive Sensing (CS) and Sparse Recovery (SR). Following a preliminary analysis of the stated concepts, a study is proposed to implement a SG model in MATLAB, with the aim to show and finally to compare some applications of CS - SR algorithmic techniques that are going to obtain effective problem solutions.
If you need more information about how the code works and how to build it, feel free to contact me at [email protected].