Fault Current Estimation in Multi-Terminal HVdc Grids Considering MMC Control


For multi-terminal HVdc protection systems, DC faults are crucial events, and knowing the critical fault time is essential in the safeguards design. Critical fault time is studied using simulations or sophisticated analysis, which requires a substantial computer effort and is not resilient to parameter modifications in the literature. The usual technique relies on simple modular multilevel converter (MMC) models that ignore the dynamic influence of converter control loops. Node voltages and dc current measurements become progressively imprecise as one moves away from the fault location. For multi-terminal dc (MTdc) grids built on MMC, this study presents a method for estimating fault current that is more accurate than the existing differential equation-based approaches, including those used to model power and dc voltage controller dynamics. It is possible to analyse fault currents in different grid circumstances using the proposed method (e.g., fault resistance and limiting inductor value). For bipolar systems with pole-to-pole and pole-to-ground faults, this method can be applied, and it is tested using PSCAD simulations on the standard CIGRE MTdc grid.

Did you like this research project?

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

PROJECT TITLE : Smartphone based Indoor Path Estimation and Localization without Human Intervention ABSTRACT: Many different kinds of indoor positioning systems have been developed as a result of the growing market interest in
PROJECT TITLE : Parallel Fractional Hot-Deck Imputation and Variance Estimation for Big Incomplete Data Curing ABSTRACT: The fractional hot-deck imputation, also known as FHDI, is a method for handling multivariate missing data
PROJECT TITLE : SDN-based Traffic Matrix Estimation in Data Center Networks Through Large Size Flow Identification ABSTRACT: In data center networks, software-defined networking (SDN), which has a control plane that is distinct
PROJECT TITLE : Physics-Informed Deep Learning for Traffic State Estimation ABSTRACT: The lack of observed traffic data, in addition to the sensor noise that is present in the data, is the source of the difficulty associated
PROJECT TITLE : Key Points Estimation and Point Instance Segmentation Approach for Lane Detection ABSTRACT: Techniques of perception used in autonomous vehicles should be adaptable to the various environments they encounter.

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

Project Enquiry