Open AccessThe nonlinear autoregressive network with exogenous inputs (NARX) neural network to damp power system oscillations
Author(s) -
Carbonera Luis Felipe Bianchi,
Pinheiro Bernardon Daniel,
de Castro Karnikowski Douglas,
Alberto Farret Felix
Publication year - 2021
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12538
Subject(s) - nonlinear autoregressive exogenous model , damp , autoregressive model , artificial neural network , control theory (sociology) , nonlinear system , power network , computer science , power (physics) , electric power system , mathematics , artificial intelligence , control (management) , physics , econometrics , quantum mechanics , meteorology
Ensuring the stable operation of the interconnected dynamics of power systems with time‐varying and nonlinear elements is a complex matter, since it involves continuous adjustments among every part for a suitable and efficient performance. Small disturbances in the load variation procedure also routinely occur. Consequently, the controller parameters must be adjusted to the variable conditions. The nonlinear autoregressive model with exogenous input (NARX) neural network (NN) has been used in many nonlinear dynamic systems. This paper explores the NARX combined with a multiobjective optimization by using genetic algorithms (GAs) to damp local and interarea oscillation modes. The NN model is trained by using a historical database determined by the GA for several load levels. Subsequently, the model can change the stabilizer parameters in real time after the learning phase. This study is used to tune a power system stabilizer (PSS) in a two‐area four‐machine system. The results of extensive simulations indicate a substantial improvement of the GA‐NARX‐PSS design while maintaining a reasonable fault resilience of the synchronous machine for several operating loads.