The nonlinear autoregressive network with exogenous inputs (NARX) neural network to damp power system oscillations. (26th July 2020)

• Record Type: Journal Article
• Authors: Carbonera, Luis Felipe Bianchi; Pinheiro Bernardon, Daniel; de Castro Karnikowski, Douglas; Alberto Farret, Felix
• Is Part Of: International transactions on electrical energy systems. Volume 31:Number 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-26
• Subjects: genetic algorithm -- neural network -- power system stabilizers -- synchronous generation; Electric power -- Periodicals; Electric power systems -- Periodicals; Electrical engineering -- Periodicals; 621.3
• DOI: 10.1002/2050-7038.12538
• Languages: English
• ISSNs: 2050-7038

Abstract

Abstract: 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.