Estimation of dominant power oscillation modes based on ConvLSTM approach using synchrophasor data and cross-validation technique

Abstract

This paper proposes a deep neural network approach considering performance-based cross-validation and confidence interval analysis to estimate a power system’s dominant power oscillation modes. Due to increased electricity demands, power utilities implement various generation sources in their power systems. Accordingly, a modern power system is increasingly complex as a multi-area and multi-machine power system. The electromechanical oscillation modes arise inevitably. Moreover, a major-unexpected event could excite weakly damped power oscillation modes and cause power system instability. The estimation of dominant power oscillation modes is significant for power system monitoring and control. A fast computing time of such modes estimation is essential for further actions. This paper applies the convolutional long short-term memory 2-dimension (ConvLSTM2D) approach to estimate dominant oscillation modes based on synchrophasor data. The proposed ConvLSTM2D approach provides precise estimation with a great opportunity to avoid a forced power system outage. The simulation results of the ConvLSTM2D approach show better accuracy of the dominant power oscillation modes estimated in comparison with the state-of-the-art algorithms (SOTA), i.e., long short-term memory (LSTM), gated recurrent unit (GRU), and hybrid convolutional neural networks-long short-term memory (CNN-LSTM) algorithms. The proposed approach is a systematic approach that can be adaptively improved over time. In addition, the proposed approach can be further applied to wide-area monitoring considering the stability margin of a transmission system. • A ConvLSTM approach is proposed to estimate multiple dominant power oscillation modes. • The proposed approach using time-series cross-validation provides a promising performance. • The performance of proposed approach is robust proved by confidence interval analysis. • The proposed approach can reduce model parameters and training time.