Neural network based pattern recognition for classification of the forced and natural oscillation.

Forced Oscillation (FO) has emerged as a major concern in power system due to its unpredictable frequency range. When the frequency of FO lies in the vicinity of that of the inherent natural oscillation, it can lead to resonance, posing a serious risk to the system. Improper identification and dissociation of FO in a power system can imprint huge detrimental effects, including blackouts. To address this issue, a novel method based on pattern recognition using artificial neural networks (ANNs) is proposed in this paper to classify FO and electromechanical oscillation. Multivariate time series data are collected from phasor measurement units (PMUs) placed at different buses are used as input for a two-layer NN, which is optimized using the stochastic scaled conjugate gradient algorithm. The proposed algorithm is simulated on the standard IEEE 10 machine, 39 bus system for various cases. The accuracy of the proposed algorithm is evaluated using the F1 score, obtained from the confusion plot. The results demonstrate that the network achieves a training accuracy of 97.5% and a testing accuracy of 96.67% with an F1 score of 96.7%. Additionally, ROC curves shows the efficiency of the designed model to predict the FO. • Classification of forced and natural oscillations in the grid system. • Utilization of backpropagation neural network based approach. • Classification problem framed as a sequential, multivariate time series problem. • Training network using stochastic conjugate scaled gradient optimization algorithm. • Robustness of technique using different statistical evaluation performance measures. [ABSTRACT FROM AUTHOR]