Data-driven Feature Analysis in Control Design for Series-Compensated Transmission Systems
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- PacifiCorp, Portland, OR (United States)
- Idaho Power Company, Boise, ID (United States)
One challenge in power-system control designs is the gap between numerical model-based analysis and complex real-world power systems. With increased data and measurements being collected from power systems, data-driven analysis (e.g., machine learning) may provide an alternative approach to reveal hidden information through learning from the real system data, and provide insights for better control scheme design during the utility planning process. Data-driven feature analysis was performed to evaluate the relationships between series compensation, power generation, and path flows in a real transmission system, as well as temporal patterns. The main data-driven analysis methods, including statistical cross-correlation, multinomial logistical regression, and classification and regression trees, were integrated for feature selection and developing predictive models of series compensation. Analysis results demonstrated the effectiveness of the proposed methodology in feature analysis and the potential to help improve power-system control scheme design.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE Office of Electricity (OE)
- Grant/Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1509840
- Report Number(s):
- PNNL-SA-139624
- Journal Information:
- IEEE Transactions on Power Systems, Vol. 34, Issue 4; ISSN 0885-8950
- Publisher:
- IEEECopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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