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
Detecting the Location of Short-Circuit Faults in Active Distribution Network Using PMU-Based State Estimation
TL;DR: The results proved that the proposed method for short-circuit fault detection and identification based on state estimation (SE) is more accurate and reliable than traditional SE based methods in fault conditions and can precisely determine the real location of fault at lower SE execution times.
Journal ArticleDOI
Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings.
Mahmoud Elsisi,Mahmoud Elsisi,Minh-Quang Tran,Karar Mahmoud,Karar Mahmoud,Matti Lehtonen,Mohamed M. F. Darwish,Mohamed M. F. Darwish +7 more
TL;DR: In this paper, the authors proposed a deep learning-based people detection system utilizing the YOLOv3 algorithm to count the number of persons in a specific area, and the status of the air conditioners are published via the internet to the dashboard of the IoT platform.
Proceedings ArticleDOI
Effect of harmonics on transformers loss of life
TL;DR: In this article, the effect of harmonics on transformers is discussed and a thermal model to predict a transformer hot spot temperature is presented, considering a time varying harmonic load cycle and ambient temperature.
Journal ArticleDOI
Circuit-Breaker Automated Failure Tracking Based on Coil Current Signature
TL;DR: In this paper, a new algorithm using trip and close coil current (CC) signature is proposed to detect the mode and cause of circuit breakers incipient failures, and the failures and their causes are categorized based on the outcome of these investigations.
Journal ArticleDOI
A novel hybrid self-adaptive heuristic algorithm to handle single- and multi-objective optimal power flow problems
TL;DR: A novel fuzzy adaptive hybrid configuration oriented to a joint self-adaptive particle swarm optimization (SPSO) and differential evolution algorithms, namely FAHSPSO-DE, is proposed to address the multi-objective OPF (MOOPF) problem.