Data mining and graph theory focused solutions to Smart Grid challenges

Data mining;Graph Theory;Spanning trees;Steiner tree;wind farm collector system;wind power integration;coordination of wind power with energy storage;energy storage sizing;wind power forecasting;pattern identification;data volume reduction;power system visualization;spark-lines;Smart Grid

Dutta, Sudipta

关键词: Data mining;    Graph Theory;    Spanning trees;    Steiner tree;    wind farm collector system;    wind power integration;    coordination of wind power with energy storage;    energy storage sizing;    wind power forecasting;    pattern identification;    data volume reduction;    power system visualization;    spark-lines;    Smart Grid;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/42195/Sudipta_Dutta.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

The Smart Grid represents a transition of the power and energy industry into a new era of improved efficiency, reliability, availability, and security, while contributing to economic and environmental health. However, several challenges must be addressed for real-life implementation of Smart Grids. Demonstrating the effectiveness of data mining and graph theory in solving some of these problems is the motivation of this dissertation.One of the key challenges in taking advantage of what the Smart Grid offers is to extract information from volumes of power system data accumulated by a suite of new sensors and measurement devices. Data presents unprecedented potential of developing better understanding of the underlying system. Handling “data explosion” in power systems and mining it for information is hence a critical challenge, necessitating the development of sophisticated algorithms. To address this need, a particular instance of power system data, namely transient stability data is studied. Generator frequencies in a large power system are analyzed with data mining techniques to extract information such as groups of coherent generators. An effective visualization method based on “spark-lines” is also presented answering a long-time question of how to best display time-varying power system data.Spark-lines are automatically placed on a geographical map of the system employing methods of graph drawing. Developed methods detected abnormal behavior in two generators of the system which was caused by errors in the generators’ simulation models that were previously undetected and subsequently corrected. This brings out the power of the developed methodology.Another important aspect of the Smart Grid is to enable integration of large quantities of renewables such as wind power. This requires installation of large wind farms and in turn availability of advanced methods for designing wind farms. The electrical collector system is the single most important element of a wind farm after the wind turbines, and its optimal design is necessary for optimal wind farm operation. However, there is a need for algorithms to automatically design optimal wind farm collector systems. This represents the second problem addressed in this dissertation. A graph-theoretic approach has been applied to design an optimal wind farm collector system with minimum total trenching length. Clustering techniques have also been found extremely useful in handling specific design constraints. Application of the developed methods generated designs with significantly lower costs compared to an actual real-world wind farm.The third and final challenge addressed is reliably integrating large quantities of wind power into the system. Inherent problems of variability of wind power can be overcome by developing better wind power forecasting methods and incorporating energy storage units such as batteries. A least squares estimation based short-term wind power forecasting method has been presented. Additionally, methods have been developed to determine optimal storage capacity required and optimal generation commitment for a wind farm with on-site energy storage. Both methods have been found to be extremely sensitive to the statistical properties of wind and load forecast data.In summary, this work applies tools, techniques, and concepts from the areas of graph theory and data mining to address three critical challenges of real-life implementation of Smart Grids. It is anticipated that the work presented in this dissertation will encourage future research in application of graph theory and data mining to other Smart Grid challenges.