Ngamroo, Issarachai
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Preferred name
Ngamroo, Issarachai
Alternative Name
Ngamroo, I.
Main Affiliation
Email
issarachai.ng@kmitl.ac.th
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Item type:Publication, Estimation of dominant power oscillation modes based on ConvLSTM approach using synchrophasor data and cross-validation technique(2022-09-01); Dechanupaprittha, SanchaiThis 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, ConvLSTM-based real-time power flow estimation of smart grid with high penetration of uncertain PV considering measurement noise(2024-04-01); Dechanupaprittha, SanchaiA modern smart grid tends to have increasingly various uncertain renewable generations. Due to different geographical areas and network topology constraints, operations of a smart grid become complicated and challenging. Moreover, using existing methods, power flow estimation in real-time could be time-consuming and computationally expensive. This paper proposes an efficient deep learning approach to estimate real-time power flow solutions of the smart grid with high penetration of uncertain PV generations using synchrophasor data considering measurement noise. The performance and effectiveness of the proposed convolutional long short-term memory (ConvLSTM) with time-series cross-validation technique are examined using synchrophasor data with Gaussian noise in the IEEE 39 bus test system. The proposed ConvLSTM approach shows better robust performance than weighted least square (WLS) state estimation and long short-term memory (LSTM) approaches. In addition, state measurements and confidence intervals are employed to confirm the accuracy of estimated real-time power flow results. The accurate real-time power flow estimation is crucial to determining dynamic available transfer capability (ATC) results and efficient operations of smart grids.