In this paper, an artificial neural network (ANN) based estimation method for missing entries in synchrophasor data is proposed. The proposed estimation method is comprised of two stages; initial training of the ANN and subsequent updating of the initially trained network. In the first stage, ANN is trained by using synchrophasor data of neighbor phasor measurement units (PMUs) for estimation of missed or missing voltage/current phasor data. The weights of initially trained ANN are tuned in the updating stage for every specified timestep. The updating stage yields accurate point-wise estimation of missing entries by reflecting dynamic variation of wide-area electric power systems. Estimation performance of real-world synchrophasor data is investigated by setting different ranges of neighbor PMUs, and directly calculated synchrophasor signal using line parameters is also compared. The proposed method is capable of accurate estimation of missing entries in both ambient and event states, and implementability is also discussed by comparing the initial training and updating stages.
|Title of host publication||2019 9th International Conference on Power and Energy Systems, ICPES 2019|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2019 Dec|
|Event||9th International Conference on Power and Energy Systems, ICPES 2019 - Perth, Australia|
Duration: 2019 Dec 10 → 2019 Dec 12
|Name||2019 9th International Conference on Power and Energy Systems, ICPES 2019|
|Conference||9th International Conference on Power and Energy Systems, ICPES 2019|
|Period||19/12/10 → 19/12/12|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, ICT & Future Planning #NRF-2017R1A2A1A05001022. This research was also supported by Korea Electric Power Corporation (KEPCO) #R18XA05.
© 2019 IEEE.
All Science Journal Classification (ASJC) codes
- Energy Engineering and Power Technology
- Fuel Technology
- Renewable Energy, Sustainability and the Environment
- Electrical and Electronic Engineering
- Control and Optimization