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Buckner, Mark

Publications and source records attributed to Buckner, Mark.

OPEN-CODE, REAL-TIME EMULATION TESTBED OF GRID-CONNECTED TYPE-3 WIND TURBINE SYSTEM WITH HARDWARE VALIDATION

This paper presents the design and development of an open-code Type-3 wind turbine virtual-testbed for real-time simulations. The proposed testbed includes the bare code for all the models required to emulate a Type-3 wind turbine system: the Doubly-Fed Induction Generator (DFIG) dynamic model, single-mass drive train, wind turbine system, and average model of the back to back power. This testbed can be executed in PC-layer for offline simulations or can be executed in real-time on the National Instruments (NI)-CompactRIO (cRIO) platform. The discrete-time models are implemented on the Field Programmable Gate Array (FPGA), using LabVIEW, and validated against a physical hardware setup. The emulated stator and rotor currents and DC-link voltage are compared against physical measurements creating a validated platform independent from third-party models and suitable for applications in real-time. The proposed testbed is intended to be available to researchers, wind turbine manufacturers, and utility companies to perform tests using a validated platform to verify proposed control designs of DFIG wind turbines.

Ferrari Maglia, Max↗

Integrated Multi Scale Data Analytics and Machine Learning for the Grid; Benchmarking Algorithms and Data Quality Analysis

Machine Learning and Data Analytics are terms which are utilized now ubiquitously throughout grid analytics forums, as a potential solution to the growing big data challenges for the distribution grid. As sensors become inexpensive and common, and communication networks grow, the analytics themselves must adapt and enable consumers and providers to visualize and utilize the data provided effectively. Machine learning is one of the techniques proposed in order to manage this data deluge. This report is a subset of the work in this activity, funded through the DOE Grid Modernization Initiative, to summarize the benchmarking and review of data quality impact on the algorithm sets being developed within each use case area.

42 ENGINEERING↗

Comparison of Doubly-Fed Induction Generator Machine Models for Real-Time Simulations

This work presents a detailed comparison among three implementations of the Doubly-Fed Induction Generator (DFIG) machine model. The alpha-beta-gamma, synchronous (dqo), and natural frame dynamic machine models were deployed on a Field-Programmable Gate Array (FPGA) and compared in terms of execution time and resource utilization. These machine models were also validated against a physical DFIG machine during balanced and unbalanced grid conditions. A benchmark is presented with the objective of selecting the machine model, orientation and state-space representation that is best suited for real-time simulations. The selected machine model is used to develop a real-time emulator of a Type-3 wind system. This emulator enables the study of wind turbine system in realtime using a validated platform while allowing controller testing without relying on expensive third-party solutions.

Ferrari Maglia, Max↗

Scoping Study on Research and Development Priorities for Distribution-System Phasor Measurement Units

This report addresses the potential use of phasor measurement units (PMUs) within electricity distribution systems, and was written to assess whether or not PMUs could provide significant benefit, at the national level. We analyze examples of present and emerging distribution-system issues related to reliability, integration of distributed energy resources, and the changing electrical characteristics of load. We find that PMUs offer important and irreplaceable advantages over present approaches. However, we also find that additional research and development for standards, testing and calibration, demonstration projects, and information sharing is needed to help industry capture these benefits.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Robotics

Mark Buckner wants to help machines see objects the way humans do, and he's also mentoring the next generation of scientists and engineers through FIRST Robotics.

42 ENGINEERING↗