We would like to invite you to the public lecture by Prof. Yusheng Xue from State Grid EPRI, China.
August 24th, 2017
10.00-12.00
Building 329, room U005
Prof. Yusheng Xue (M’88) received the M.Sc. degree in electrical engineering from EPRI, China, in 1981, and the Ph.D. degree in electrical engineering from the University of Liege, Liege, Belgium, in 1987. He was elected as an Academician of the Chinese Academy of Engineering in 1995.
He is now the Honorary President of State Grid Electric Power Research Institute (SGEPRI or NARI), China. He holds the positions of Adjunct Professor in many universities in China and is a Conjoint Professor with the University of Newcastle, Callaghan, NSW, Australia. He is also an Honorary Professor with the University of Queensland, Brisbane, Qld., Australia. He has been a member of the PSCC Council, and the Editorin- Chief of Automation of Electric Power System since 1999, and a Member of Editorial Board of IET Generation, Transmission, and Distribution, and Chairman of Technical Committee of Chinese National Committee of CIGRE since 2005.
Lecture 1 - From Smart Grids to Cyber-Energy-Society Systems
Abstract
Smart Grids (SGs) as cyber-physical systems (CPSs) in nature are electric networks that use innovative and intelligent monitoring, control, communication, and self-healing technologies to deliver better connections and operations for generators and distributors, flexible choices for prosumers, and reliability and security of electricity supply. However, in line with the global movement towards a sustainable renewable energy future to address Climate Change, SGs cannot fully reflect the requirements of dominating renewable energy generation, stringent economic and environmental constraints, market competition, social and regulatory requirements. On one hand, electric energy plays a central role in the whole energy supply chain since changing the energy from electric form to non-electric one may not be as effective as using electricity directly. On the other hand, any changes in primary energy and end-use energy significantly affect electric power reliability as well as economy. A more holistic (system-of-system) approach needs to be taken to deal with future energy, and a new concept of CESS (Cyber-Energy-Society Systems) is proposed. Consideration should be given to coordination of environmental, economic, social factors and human behaviors, a hybrid research framework across various disciplines concerned with different time and space scales. This enables collaborative mining big data with hidden causal relationships in the complex cross social, technological, economical, and environmental dimensions. The driving force induced by interaction between them may be much more powerful than the internal driving force of information systems, energy systems, and human societies themselves.
Lecture 2 - An Example of Integrating Various Research Forms to Solve Classical Topics
Abstract
The combination of causal analysis and statistical analysis is the embodiment of big data thinking. A good example is the trajectory based analysis for power system synchronism stability. The causal analysis for synchronism stability is based on a quantized index which is extracted from model simulation results; the statistical analysis is applied in turn to accelerate the speed of quantitative analysis.
The extended equal area criterion (EEAC) performs numerical integration for the full models in Rn, and then maps the resultant high dimensional trajectory during the whole process into a set of orthogonal time-varying one-machine infinite-bus planes in a step-by-step fashion by using a linear stability preserving dimension reduction mapping. Quantitative stability analysis can be performed for the image OMIB systems with time-varying parameters then.
EEAC can be viewed as a deep knowledge extraction technology from massive simulation data or from PMU data. The stability limit of multi-machine system model depends on the most critical image. Thus, the causal relationship analyses between the perturbed trajectory set (big data set) and system stability margin (single scalar data) are added to the experiential and statistical interpretation for the disturbed trajectory stability. The EEAC consists of three algorithms: static EEAC (time-variant factors are omitted), dynamic EEAC (time-variant factors are partially considered), and integrated EEAC (time-variant factors are fully considered). Their accuracies increase successively, at the cost of incremental calculation burden.
When the results given by dynamic EEAC are sufficiently close to the static EEAC, it is unnecessary to call the integrated EEAC. Based on comparison the result of dynamic EEAC and that of static one, a time-varying-degree index is defined. Moreover, another index of unstable mode variability is defined to quantize the sensitive degree of the unstable mode with respect to parameters. Both the two causal indexes merge into AI as the input features of machine learning, and largely improve the robustness of the case filtering, and only few cases need treatment of the integrated EEAC. Deeply merging numerical and analytical solutions with theory and AI makes the quantitative assessment of transient stability not only as accurate as the numerical integration, but also extremely fast.
This presentation shows how the big data thinking is applied to stability analyses, how the analytical solution merges with numerical integration, the theory analyses merge with data mining, and causal elements merge with AI method.