What is he best known for?
The fields of study he is best known for:
- Electrical engineering
- Voltage
- Artificial intelligence
Gary W. Chang mainly investigates Electronic engineering, Harmonic analysis, Harmonic, Harmonics and Control theory.
His work deals with themes such as Control engineering, Transformer and Power system simulation, Electric power system, which intersect with Electronic engineering.
His biological study spans a wide range of topics, including Power system harmonics, Fundamental frequency and Voltage.
His Harmonic research incorporates themes from Time domain and MATLAB.
In his study, which falls under the umbrella issue of Harmonics, Backpropagation and Radial basis function is strongly linked to Signal.
His Control theory study incorporates themes from Total harmonic distortion and Wind speed.
His most cited work include:
- Experiences with Mixed Integer Linear Programming-Based Approaches in Short-Term Hydro Scheduling (229 citations)
- Test systems for harmonics modeling and simulation (221 citations)
- An Improved Backward/Forward Sweep Load Flow Algorithm for Radial Distribution Systems (211 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Electronic engineering, Control theory, Electric power system, Harmonics and Voltage.
Gary W. Chang studies Harmonic analysis which is a part of Electronic engineering.
The various areas that Gary W. Chang examines in his Control theory study include Total harmonic distortion, Electronic filter, Artificial neural network, AC power and Filter.
Gary W. Chang has researched Electric power system in several fields, including Control engineering, Power quality, Fault and Nonlinear system.
His work is dedicated to discovering how Harmonics, Fast Fourier transform are connected with Signal processing and other disciplines.
His research in the fields of Transformer and Power electronics overlaps with other disciplines such as Electric arc furnace.
He most often published in these fields:
- Electronic engineering (50.31%)
- Control theory (38.36%)
- Electric power system (32.70%)
What were the highlights of his more recent work (between 2015-2020)?
- Control theory (38.36%)
- Voltage (32.08%)
- Photovoltaic system (6.29%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Control theory, Voltage, Photovoltaic system, Artificial neural network and Power quality.
His research in Control theory intersects with topics in Wind power and Harmonic.
His Voltage research includes elements of Reliability engineering, Electric power system and Electric power.
His Photovoltaic system research incorporates elements of Electricity generation, Inverter and Benchmark.
Gary W. Chang has included themes like Data modeling and Autoregressive integrated moving average in his Artificial neural network study.
His Harmonic analysis research is within the category of Electronic engineering.
Between 2015 and 2020, his most popular works were:
- An improved neural network-based approach for short-term wind speed and power forecast (109 citations)
- On waveform distortion in the frequency range of 2 kHz–150 kHz—Review and research challenges (47 citations)
- Integrating Gray Data Preprocessor and Deep Belief Network for Day-Ahead PV Power Output Forecast (14 citations)
In his most recent research, the most cited papers focused on:
- Electrical engineering
- Voltage
- Artificial intelligence
Gary W. Chang mainly focuses on Artificial neural network, Photovoltaic system, Autoregressive integrated moving average, Control theory and Wind power.
His Photovoltaic system research includes themes of Voltage drop, AC power, Voltage and Benchmark.
His Voltage study integrates concerns from other disciplines, such as Electricity generation, MATLAB, Shadow and Renewable energy.
As part of one scientific family, Gary W. Chang deals mainly with the area of Autoregressive integrated moving average, narrowing it down to issues related to the Support vector machine, and often Power station, Preprocessor, Deep belief network, Data mining and Data modeling.
His Control theory research is multidisciplinary, incorporating elements of Wind speed and Simulation.
The study incorporates disciplines such as Probabilistic forecasting and Nonlinear system in addition to Wind speed.
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