Dissertation
Data-Driven Situational Awareness for Distribution System Resiliency
Washington State University
Doctor of Philosophy (PhD), Washington State University
2022
DOI:
https://doi.org/10.7273/000005106
Abstract
With the continuing deployment of distributed energy resources (DER), behind-the-meter resources, advanced sensors, and grid-edge intelligence, monitoring and operation of the distribution power grid are increasingly becoming complex. Effectively leveraging the massive amount of data available with digital automation to achieve situational awareness (SA) for the right operational decisions is critical to enhancing system resiliency, reliability, and sustainability.
Improving SA can be achieved through sensor data acquisition, real-time data analytics, physics-aware machine learning for the estimation of unmeasured data, efficient data management, and information extraction. Existing state-of-the-art grid monitoring approaches typically utilize data stovepiped into repositories of operational data, planning data, and third-party or enterprise data. Data from distribution phasor measurement units (D-PMUs) & supervisory control, and data acquisition (SCADA) are examples of operational data. Data from advanced metering infrastructure (AMI) meters is an example of enterprise data. In addition, historical data and asset data are examples of planning data. Furthermore, local news and forecasted weather are examples of third-party data. This segmentation limits realizing the unabridged value of available data. In addition, this set of data varies in terms of volume, velocity, variety, veracity, and value. The objective of this work is to coordinate, estimate and process the segmented data, and enhance the situational awareness of the distribution power grid with high penetration of DER. Specific goals of this work include: a) PV forecasting, nowcasting, and estimation, and b) utilizing estimated data and aggregation for efficient system monitoring and resilient operation. Results demonstrate the superiority of the developed PV estimation approach, and matching behavior of the reduced aggregated system with a fully modeled distribution network.
Metrics
10 File views/ downloads
27 Record Views
Details
- Title
- Data-Driven Situational Awareness for Distribution System Resiliency
- Creators
- Chuan Qin
- Contributors
- Anurag Srivastava (Advisor)Anjan Bose (Advisor)Anamika Dubey (Committee Member)Noel Schulz (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Theses and Dissertations
- Doctor of Philosophy (PhD), Washington State University
- Publisher
- Washington State University
- Number of pages
- 255
- Identifiers
- 99901019841001842
- Language
- English
- Resource Type
- Dissertation