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3 results for: All records
Author ORCID ID is 0000000255590971
Full Text and Citations
  1. Short-term solar forecasting models based solely on global horizontal irradiance (GHI) measurements are often unable to discriminate the forecasting of the factors affecting GHI from those that can be precisely computed by atmospheric models. Here, we introduce a Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) that decomposes the forecasting of GHI into the computation of extraterrestrial solar radiation and solar zenith angle and the forecasting of cloud albedo and cloud fraction. The extraterrestrial solar radiation and solar zenith angle are accurately computed by the Solar Position Algorithm (SPA) developed at the National Renewable Energy Laboratory (NREL).more » A cloud retrieval technique is used to estimate cloud albedo and cloud fraction from surface-based observations of GHI. With the assumption of persistent cloud structures, the cloud albedo and cloud fraction are predicted for future time steps using a two-stream approximation and a 5-min exponential weighted moving average, respectively. Our model evaluation using the long-term observations of GHI at NREL's Solar Radiation Research Laboratory (SRRL) shows that the PSPI has a better performance than the persistence and smart persistence models in all forecast time horizons between 5 and 60 min, which is more significant in cloudy-sky conditions. Finally, compared to the persistence and smart persistence models, the PSPI does not require additional observations of various atmospheric parameters but is customizable in that additional observations, if available, can be ingested to further improve the GHI forecast. An advanced technology of cloud forecast is also expected to improve the future performance of the PSPI.« less
  2. Here, traditional synchrophasors rely on CTs and PTs physically connected to transmission lines or buses to acquire input signals for phasor measurement. However, it is challenging to install and maintain traditional phasor measurement units in some remote areas due to lack of facilities. Since transmission lines naturally generate alternating electrical and magnetic fields in the surrounding atmosphere, this paper presents two innovative designs for non-contact synchronized measurement devices (NCSMD), including an electric field sensor based non-contact SMD (E-NCSMD) and a magnetic field sensor based non-contact SMD (M-NCSMD). Compared with conventional synchrophasors, E-NCSMD and M-NCSMD are much more flexible to bemore » deployed and have much lower costs, making E-NCSMDs and M-NCSMD highly accessible and useful for a wide array of phasor measurement applications. Laboratory and field experiment results verified the effectiveness of the designs of both E-NCSMD and M-NCSMD.« less
  3. Here, this paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step ofmore » the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of the paper.« less

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