J. J. Curto

Country flagSpain

Research Interests

Explore related searches

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

Articles (11)

Year:

2023

Year:

2022

Rapid magnetic variations on Earth are intimately linked with solar activity and this is one of the main topics in Space Weather research. Modelling and forecasting these phenomena are vital in our technological society. The Service of Rapid Magnetic Variations provides lists of these remarkable magnetic events in a continuous way in what constitutes a long geophysical series that began in the late 19th century. Although the aim of this Service remains unchanged, methods have changed with time. Here, we describe the recent evolution of the Service, its database and the latest works carried out to improve the products delivered to the scientific society.

Year:

2022

Space Weather phenomena pose a major socio‐economic threat due, in part, to the vulnerability of power network transformers to geomagnetically induced currents (GIC). In 1985, Lehtinen and Pirjola (LP) provided a method to calculate GICs in a single‐voltage‐level network. The need to account for lower voltages has caused the proliferation of GIC risk assessments applied to power grids with multiple voltages. We hereby present a tool to systematically generate the direct currents (DC)‐equivalent of a multiple‐voltage‐level network. The LP and Nodal Admittance Matrix methods are the most popular schemes to compute the GIC from the resulting equivalent network by solving the circuit laws for the earthing current or the voltage at each node, respectively. A new scheme is presented here that solves the circuit laws for the current flowing between the bus nodes and the neutral point which, unlike LP, requires no (infinite‐resistance) earth connections for the buses. The number of equations/unknowns of the resulting GIC matrix equation is reduced compared to the traditional methods, thus optimizing the computational cost ‐an advantage that becomes important for large networks‐, while keeping a good condition of the design matrix associated with the inversion. Examples are shown for different test cases and a standalone computationally efficient code is provided.

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2021

Year:

2021

Year:

2021

Year:

2020

Collaborators (5)

Joan Miquel Torta

-

SPAIN
View details

Ramon Oliver

University of the Balearic Islands

SPAIN
View details

Perla Piña-Varas

University of Western Australia

AUSTRALIA
View details

Aude Chambodut

-

FRANCE
View details

Pilar QUERALT

Full Professor

University of Barcelona

SPAIN
View details
ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.