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
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Journal ArticleDOI
Utilization of in-pipe hydropower renewable energy technology and energy storage systems in mountainous distribution networks
TL;DR: A stochastic mixed-integer linear programming (MILP) formulation that simultaneously determines the optimal location and size of ESS and IHS in a microgrid (MG) considering the correlation between prevailing uncertainties is developed.
Modeling of rogowski coil for on-line PD monitoring in coveredconductor overhead distribution networks
TL;DR: In this article, an EMTP/ATP simulation environment is used to model the Rogowski coil for partial discharge (PD) monitoring due to leaning trees on the covered-conductor (CC) overhead distribution lines.
Proceedings ArticleDOI
High Frequency Current Transformer Modeling for Traveling Waves Detection
TL;DR: In this article, high frequency characterization of the current transformer was investigated using frequency response measurements where the current transform transfer function was calculated from measured impulse signals and its performance under transient conditions can be studied using different programs such as EMTP and MATLAB.
Journal ArticleDOI
Network-Constrained Transactive Coordination for Plug-In Electric Vehicles Participation in Real-Time Retail Electricity Markets
TL;DR: In this article, a real-time retail electricity market for plug-in electric vehicles (PEVs) under a transactive energy (TE) paradigm is proposed, where PEV owners estimate their willingness to pay/accept using a user-friendly strategy and submit the estimated values to the retail market operator.
Journal ArticleDOI
Adaptive Predictor Subset Selection Strategy for Enhanced Forecasting of Distributed PV Power Generation
TL;DR: An adaptive hybrid predictor subset selection (PSS) strategy to obtain the most relevant and nonredundant predictors for enhanced short-term forecasting of the power output of distributed PVs is proposed and outperforms the other prediction selection methods.