Power system data classification and prediction by functional data analysis
Date
2021
Authors
Sun, Hongfei, author
Yang, Liuqing, advisor
Luo, Jie, advisor
Zhang, Hongming, committee member
Duan, Dongliang, committee member
Wang, Haonan, committee member
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Abstract
The last couple of decades have witnessed the development of our electric power grid. The growing population size and increasing consumerism have increased the load demand and brought more pressure on the grid. Meanwhile, new elements are being introduced to the power grid, such as various forms of renewable energy resources, electric vehicles, and so on, which need to be monitored constantly and managed adequately. In addition, the allocation of the various resources in the power systems is now conducted in a much more dynamic manner than ever. All these new dimensions have driven the development of the traditional grid into the smart grid and call for new methodologies in system design, operation, and control. This dissertation focuses on modeling power systems with data-driven approaches, with applications in power system cyber-attack detection and recovery, and large-scale, long-term load characterization. Firstly, the modeling of the spatial-temporal relationship among the quantities across the entire power systems is provided with applications to cyber-attack detection and data recovery. Then, the non-conforming load classification approaches based on Functional Principle Component Analysis (FPCA) will be introduced. This work is the first effort towards such loads due to the recently growing penetration of Distributed Energy Resources (DER) users. Lastly, we will introduce the regional high-resolution medium-term load forecasting approach. In order to satisfy the new purpose of load forecasting, serving for real-time applications, our approach can provide higher resolution than existing long-term load forecasting and longer leading time than the existing short-term load forecasting time-series load curve. Based on the presented case studies and simulation results, we provided the corresponding suggestions to the present industrial power system.
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Subject
DER penetration
load prediction
cybersecurity
power system modeling
load characterization