- By:
- Debnath, Suman ; Marthi, Phani Ratna Vanamali ; Xia, Qianxue
- Page Number:
- 430-434
- Issue Number:
- NA
- Book Title:
- 2023 IEEE PES Innovative Smart Grid Technologies Latin America (ISGT-LA)
- Publication Date:
- January 12, 2024
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- IEEE ISGT LA: PES Innovative Smart Grid Technologies Latin America
- Conference Location:
- San Juan, Puerto Rico, United States of America
- Conference Sponsor:
- IEEE PES
- View DOI Listing:
- https://doi.org/10.1109/ISGT-LA56058.2023.10328311
Abstract
Several electromagnetic transient (EMT) dynamic modeling methods are available to model systems like photovoltaic (PV) plants, wind power plants, variable-speed drives, among others. The methods include: (a) physics-based models and (b) data-driven models. The physics-based dynamic models may include high-fidelity switched system model and average-value model that both require the control algorithms included in the models. However, manufacturers typically prefer to provide black-box models to avoid disclosing proprietary. One of the solutions to prevent disclosing control algorithms is the use of data-driven dynamic EMT models of PV systems. In this paper, data-driven dynamic EMT model based on artificial intelligence (AI) algorithms are presented. The AI algorithms evaluated include convolutional neural networks, recurrent neural networks, and nonlinear auto-regressive exogenous model. Automation in generating data and training these models is also discussed in this paper. The results generated by the best AI algorithms have been observed to be greater than 95 % accurate.
