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Gary W. Chang

Researcher at National Chung Cheng University

Publications -  165
Citations -  5151

Gary W. Chang is an academic researcher from National Chung Cheng University. The author has contributed to research in topics: Harmonic & Harmonics. The author has an hindex of 35, co-authored 155 publications receiving 4529 citations. Previous affiliations of Gary W. Chang include Siemens & Universidad Michoacana de San Nicolás de Hidalgo.

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An Improved Backward/Forward Sweep Load Flow Algorithm for Radial Distribution Systems

TL;DR: This letter presents an improved backward/ forward sweep algorithm for three-phase load-flow analysis of radial distribution systems and shows that the algorithm is accurate and computationally efficient in comparing with two other commonly used methods.
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Test systems for harmonics modeling and simulation

TL;DR: In this paper, the authors present three harmonic simulation test systems for the preparation and analysis of harmonic problems through case studies and simulation examples, which can be used as benchmark for the development of new harmonic simulation methods and for the evaluation of existing harmonic analysis software.
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Interharmonics: Theory and Modeling

TL;DR: In this paper, the most remarkable issues related to interharmonic theory and modeling are presented, starting from the basic definitions and concepts, attention is first devoted to inter-harmonic sources, and then the interharmonics assessment is considered with particular attention to the problem of the frequency resolution and of the computational burden associated with the analysis of periodic steady-state waveforms.
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Experiences with Mixed Integer Linear Programming-Based Approaches in Short-Term Hydro Scheduling

TL;DR: Numerical experiences show that the solution technique is computationally efficient, simple, and suitable for decision support of short-term hydro operations planning and can be easily extended for scheduling applications in deregulated environments.
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An improved neural network-based approach for short-term wind speed and power forecast

TL;DR: An improved radial basis function neural network-based model with an error feedback scheme (IRBFNN-EF) for forecasting short-term wind speed and power of a wind farm, where an additional shape factor is included in the classic Gaussian basis function associated with each neuron in the hidden layer.