Open AccessSiamese Sigmoid Networks for the open classification of grid disturbances in power transmission systems
Author(s) -
Kummerow André,
Dirbas Mohammad,
Monsalve Cristian,
Bretschneider Peter
Publication year - 2023
Publication title -
iet smart grid
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.612
H-Index - 11
ISSN - 2515-2947
DOI - 10.1049/stg2.12083
Subject(s) - computer science , benchmark (surveying) , phasor , artificial intelligence , observability , sigmoid function , set (abstract data type) , open set , grid , transmission (telecommunications) , probabilistic logic , electric power system , data mining , machine learning , real time computing , pattern recognition (psychology) , artificial neural network , power (physics) , telecommunications , mathematics , physics , geometry , geodesy , discrete mathematics , quantum mechanics , programming language , geography
The online classification of grid disturbances is an important prerequisite for an automated and reliable operation of power transmission systems. Most of the state‐of‐the‐art approaches assume that all classes are already known in the training phase and cannot handle new disturbance events, which appear in the application phase and lead to severe misclassifications. To mitigate this shortcoming, the disturbance detection is investigated as an open classification task and a novel recurrent Siamese neural network architecture is introduced to identify and locate known and unknown disturbance events from phasor measurements. Extending preliminary work, a probabilistic distance‐based classification approach with an integrated rejection mechanism is presented, which enables to learn class‐dependent decision boundaries and margins to reduce the open‐set risk. A detailed performance analysis is presented including multiple benchmark methods in different closed‐set and open‐set classification tasks for a simulated power transmission system. Additionally, a limited and full observability of the grid with phasor measurements are addressed in the experiments.