PROJECT TITLE :

Dynamic Grid Power Routing Using Controllable Network Transformers (CNTs) With Decoupled Closed-Loop Controller

ABSTRACT:

Will increase in system hundreds and in levels of penetration of renewable energy, along with limited investment in transmission infrastructure, are fostering the requirement for a better and more dynamically controllable grid. Versatile ac transmission systems devices will be used to dynamically control the grid and more efficiently route power and therefore mitigate these stresses, but such devices are either too difficult and expensive for implementation or incapable of independently controlling active and reactive powers. A controllable network transformer (CNT) features a fractionally rated direct ac/ac converter and was introduced as a less complicated and more price-effective solution to realize dynamic power control between 2 areas. The CNT utilizes the twin virtual quadrature supply (DVQS) technique to alter both the road voltage amplitude and phase angle, therefore enabling a dynamic power control; however, the management variables outlined in this technique have a cross-coupling effect between active and reactive powers. During this paper, the CNT operating ranges with and without considering line resistance are analyzed; then, a decoupled closed-loop controller is meant to attain freelance active and reactive power control based mostly on a reference power control command. To handle the likelihood of power overshoot in an exceedingly CNT with DVQS, a hybrid open-loop/closed-loop proportional–integral controller is additionally proposed. Simulations and experimental results are given to verify the controller style.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : Unsupervised Spectral Feature Selection with Dynamic Hyper-graph Learning ABSTRACT: In order to produce interpretable and discriminative results from unsupervised spectral feature selection (USFS) methods, an embedding
PROJECT TITLE : GloDyNE: Global Topology Preserving Dynamic Network Embedding ABSTRACT: Due to the time-evolving nature of many real-world networks, learning low-dimensional topological representations of networks in dynamic environments
PROJECT TITLE : Fully Dynamic kk-Center Clustering With Improved Memory Efficiency ABSTRACT: Any machine learning library worth its salt will include both static and dynamic clustering algorithms as core components. The sliding
PROJECT TITLE : Exploring Temporal Information for Dynamic Network Embedding ABSTRACT: The task of analyzing complex networks is a challenging one that is attracting an increasing amount of attention. One way to make the analysis
PROJECT TITLE : MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers ABSTRACT: Two of the most significant challenges for effective resource management in large-scale

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry