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Gary W. Chang

Bio: 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.


Papers
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Journal ArticleDOI
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.
Abstract: This letter presents an improved backward/ forward sweep algorithm for three-phase load-flow analysis of radial distribution systems. In the backward sweep, Kirchhoff's Current Law and Kirchhoff's Voltage Law are used to calculate the upstream bus voltage of each line or a transformer branch. Then, the linear proportional principle is adopted to find the ratios of the real and imaginary components of the specified voltage to those of the calculated voltage at the substation bus. In the forward sweep, the voltage at each downstream bus is then updated by the real and imaginary components of the calculated bus voltage multiplying with the corresponding ratio. The procedure stops after the mismatch of the calculated and the specified voltages at the substation is less than a convergence tolerance. The proposed algorithm is tested with three IEEE benchmark distribution systems. Results show that the algorithm is accurate and computationally efficient in comparing with two other commonly used methods

291 citations

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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.
Abstract: This paper presents three harmonic simulation test systems. The purpose is to demonstrate guidelines for the preparation and analysis of harmonic problems through case studies and simulation examples. The systems can also be used as benchmark systems for the development of new harmonic simulation methods and for the evaluation of existing harmonic analysis software.

280 citations

Journal ArticleDOI
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.
Abstract: Some of the most remarkable issues related to interharmonic theory and modeling are presented. Starting from the basic definitions and concepts, attention is first devoted to interharmonic sources. Then, the interharmonic 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. Finally, modeling of different kinds of interharmonic sources and the extension of the classical models developed for power system harmonic analysis to include interharmonics are discussed. Numerical results for the issues presented are given with references to case studies constituted by popular schemes of adjustable speed drives.

264 citations

Journal ArticleDOI
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.
Abstract: This paper describes experiences with mixed integer linear programming (MILP) based approaches on the short-term hydro scheduling (STHS) function. The STHS is used to determine the optimal or near-optimal schedules for the dispatchable hydro units in a hydro-dominant system for a user-definable study period at each time step while respecting all system and hydraulic constraints. The problem can be modeled in detail for a hydro system that contains both conventional and pumped-storage units. Discrete and dynamic constraints such as unit startup/shutdown and minimum-up/minimum-down time limits are also included in the model for hydro unit commitment (HUC). The STHS problem is solved with a state-of-the-art package which includes an algebraic modeling language and a MILP solver. The usefulness of the proposed solution algorithm is illustrated by testing the problem with actual hydraulic system data. Numerical experiences show that the solution technique is computationally efficient, simple, and suitable for decision support of short-term hydro operations planning. In addition, the proposed approaches can be easily extended for scheduling applications in a deregulated environment.

251 citations

Journal ArticleDOI
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.

204 citations


Cited by
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TL;DR: This paper presents a detailed overview of the basic concepts of PSO and its variants, and provides a comprehensive survey on the power system applications that have benefited from the powerful nature ofPSO as an optimization technique.
Abstract: Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.

2,147 citations

01 Jan 2011
TL;DR: The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h, where the results indicate that for forecasts up to 2 h ahead the most important input is the available observations ofSolar power, while for longer horizons NWPs are theMost important input.
Abstract: This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

585 citations

Journal ArticleDOI
TL;DR: A comprehensive and extensive review of renewable energy forecasting methods based on deep learning to explore its effectiveness, efficiency and application potential and the current research activities, challenges, and potential future research directions are explored.

537 citations

Journal ArticleDOI
TL;DR: In this paper, the authors reviewed and evaluated contemporary forecasting techniques for photovoltaics into power grids, and concluded that ensembles of artificial neural networks are best for forecasting short-term PV power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty.
Abstract: Integration of photovoltaics into power grids is difficult as solar energy is highly dependent on climate and geography; often fluctuating erratically. This causes penetrations and voltage surges, system instability, inefficient utilities planning and financial loss. Forecast models can help; however, time stamp, forecast horizon, input correlation analysis, data pre and post-processing, weather classification, network optimization, uncertainty quantification and performance evaluations need consideration. Thus, contemporary forecasting techniques are reviewed and evaluated. Input correlational analyses reveal that solar irradiance is most correlated with Photovoltaic output, and so, weather classification and cloud motion study are crucial. Moreover, the best data cleansing processes: normalization and wavelet transforms, and augmentation using generative adversarial network are recommended for network training and forecasting. Furthermore, optimization of inputs and network parameters, using genetic algorithm and particle swarm optimization, is emphasized. Next, established performance evaluation metrics MAE, RMSE and MAPE are discussed, with suggestions for including economic utility metrics. Subsequently, modelling approaches are critiqued, objectively compared and categorized into physical, statistical, artificial intelligence, ensemble and hybrid approaches. It is determined that ensembles of artificial neural networks are best for forecasting short term photovoltaic power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty. Additionally, convolutional neural network is found to excel in eliciting a model's deep underlying non-linear input-output relationships. The conclusions drawn impart fresh insights in photovoltaic power forecast initiatives, especially in the use of hybrid artificial neural networks and evolutionary algorithms.

446 citations