M
Matti Lehtonen
Researcher at Aalto University
Publications - 770
Citations - 12827
Matti Lehtonen is an academic researcher from Aalto University. The author has contributed to research in topics: Fault (power engineering) & Computer science. The author has an hindex of 40, co-authored 694 publications receiving 8559 citations. Previous affiliations of Matti Lehtonen include Razi University & New York University.
Papers
More filters
Proceedings ArticleDOI
Determining wave propagation characteristics of MV XLPE power cable using time domain reflectometry technique
TL;DR: In this paper, the wave propagation characteristics of single-phase medium voltage (MV) cross-linked polyethylene (XLPE) power cable are determined using Time Domain Reflectometry (TDR) measurement technique.
Journal ArticleDOI
Flashover Probability Distribution and Volt-Time Curves of Medium Voltage Overhead Line Insulation Under Combined AC and Lightning Impulse Voltages
TL;DR: In this article, the flashover probability distributions and the volt-time characteristics of different types of insulator gaps subjected to both positive and negative polarity standard impulse (SI) and short tail lightning impulse (STLI) voltages are presented.
Journal ArticleDOI
Effect of climate change on MV underground network operations in the future smart grid environment
TL;DR: In this article, the steady state current rating calculations for 3-phase XLPE distribution power cables are performed using an analytical set of thermal equations for different installation configurations (direct burial and tube installations) under various possible extreme Finnish environmental conditions.
Journal ArticleDOI
A Novel Multi-Area Distribution State Estimation Approach for Active Networks
Mohammad Gholami,Ali Abbaspour Tehrani-Fard,Matti Lehtonen,Moein Moeini-Aghtaie,Mahmud Fotuhi-Firuzabad +4 more
TL;DR: A hierarchically distributed algorithm for the execution of distribution state estimation function in active networks equipped with some phasor measurement units employs voltage-based state estimation in rectangular form and is well-designed for large-scale active distribution networks.