Operational Data

14,000,000 Leading Edge Experts on the ideXlab platform

Scan Science and Technology

Contact Leading Edge Experts & Companies

Scan Science and Technology

Contact Leading Edge Experts & Companies

The Experts below are selected from a list of 370251 Experts worldwide ranked by ideXlab platform

P.s. Nagendra Rao - One of the best experts on this subject based on the ideXlab platform.

  • Arithmetic coding based lossless compression schemes for power system steady state Operational Data
    International Journal of Electrical Power & Energy Systems, 2012
    Co-Authors: Sarasij Das, P.s. Nagendra Rao
    Abstract:

    Introduction of processor based instruments in power systems is resulting in the rapid growth of the measured Data volume. The present practice in most of the utilities is to store only some of the important Data in a retrievable fashion for a limited period. Subsequently even this Data is either deleted or stored in some back up devices. The investigations presented here explore the application of lossless Data compression techniques for the purpose of archiving all the Operational Data - so that they can be put to more effective use. Four arithmetic coding methods suitably modified for handling power system steady state Operational Data are proposed here. The performance of the proposed methods are evaluated using actual Data pertaining to the Southern Regional Grid of India. (C) 2012 Elsevier Ltd. All rights reserved.

  • Principal component analysis based compression scheme for power system steady state Operational Data
    ISGT2011-India, 2011
    Co-Authors: Sarasij Das, P.s. Nagendra Rao
    Abstract:

    Growing use of digital instruments in smart grids (SG) is resulting in the rapid increase of the measured Data volume. In future SG, vast amount of Data will be generated by smart meters, PMU based WAMS, SCADA and other monitoring devices. While research has been done to find suitable compression technologies to store power system disturbance and PMU Data; there is lack of research on the storage of SCADA Data which is basically steady state Operational Data of the grid. Presently, utilities store some important SCADA Data for a limited period and then they either delete them or store them in unreliable manner (CD/DVD etc.). The investigations presented here explore the application of Principal Component Analysis based lossy compression technique for archiving the steady state Operational Data. Four important Operational Data - voltage, line flow, MW and MVAr generation are considered for the study. The effectiveness of the proposed method is evaluated considering practical Data pertaining to the Southern Regional Grid of India. The results illustrate the usefulness of the technique.

  • Understanding Power System Behavior through Mining Archived Operational Data
    International Journal of Emerging Electric Power Systems, 2009
    Co-Authors: Sarasij Das, P.s. Nagendra Rao
    Abstract:

    This paper is the outcome of an attempt in mining recorded power system Operational Data in order to get new insight to practical power system behavior. Data mining, in general, is essentially finding new relations between Data sets by analyzing well known or recorded Data. In this effort we make use of the recorded Data of the Southern regional grid of India. Some interesting relations at the total system level between frequency, total MW/MVAr generation, and average system voltage have been obtained. The aim of this work is to highlight the potential of Data mining for power system applications and also some of the concerns that need to be addressed to make such efforts more useful.

M. Patrik - One of the best experts on this subject based on the ideXlab platform.

Sarasij Das - One of the best experts on this subject based on the ideXlab platform.

  • Arithmetic coding based lossless compression schemes for power system steady state Operational Data
    International Journal of Electrical Power & Energy Systems, 2012
    Co-Authors: Sarasij Das, P.s. Nagendra Rao
    Abstract:

    Introduction of processor based instruments in power systems is resulting in the rapid growth of the measured Data volume. The present practice in most of the utilities is to store only some of the important Data in a retrievable fashion for a limited period. Subsequently even this Data is either deleted or stored in some back up devices. The investigations presented here explore the application of lossless Data compression techniques for the purpose of archiving all the Operational Data - so that they can be put to more effective use. Four arithmetic coding methods suitably modified for handling power system steady state Operational Data are proposed here. The performance of the proposed methods are evaluated using actual Data pertaining to the Southern Regional Grid of India. (C) 2012 Elsevier Ltd. All rights reserved.

  • Principal component analysis based compression scheme for power system steady state Operational Data
    ISGT2011-India, 2011
    Co-Authors: Sarasij Das, P.s. Nagendra Rao
    Abstract:

    Growing use of digital instruments in smart grids (SG) is resulting in the rapid increase of the measured Data volume. In future SG, vast amount of Data will be generated by smart meters, PMU based WAMS, SCADA and other monitoring devices. While research has been done to find suitable compression technologies to store power system disturbance and PMU Data; there is lack of research on the storage of SCADA Data which is basically steady state Operational Data of the grid. Presently, utilities store some important SCADA Data for a limited period and then they either delete them or store them in unreliable manner (CD/DVD etc.). The investigations presented here explore the application of Principal Component Analysis based lossy compression technique for archiving the steady state Operational Data. Four important Operational Data - voltage, line flow, MW and MVAr generation are considered for the study. The effectiveness of the proposed method is evaluated considering practical Data pertaining to the Southern Regional Grid of India. The results illustrate the usefulness of the technique.

  • Understanding Power System Behavior through Mining Archived Operational Data
    International Journal of Emerging Electric Power Systems, 2009
    Co-Authors: Sarasij Das, P.s. Nagendra Rao
    Abstract:

    This paper is the outcome of an attempt in mining recorded power system Operational Data in order to get new insight to practical power system behavior. Data mining, in general, is essentially finding new relations between Data sets by analyzing well known or recorded Data. In this effort we make use of the recorded Data of the Southern regional grid of India. Some interesting relations at the total system level between frequency, total MW/MVAr generation, and average system voltage have been obtained. The aim of this work is to highlight the potential of Data mining for power system applications and also some of the concerns that need to be addressed to make such efforts more useful.

Corey D. Markfort - One of the best experts on this subject based on the ideXlab platform.

  • A Calibration Procedure for an Analytical Wake Model Using Wind Farm Operational Data
    Energies, 2020
    Co-Authors: Jian Teng, Corey D. Markfort
    Abstract:

    Wind energy is one of the fastest growing renewable energy sources in the U.S. Wind turbine wakes change the flow field within wind farms and reduce power generation. Prior research has used experimental and computational methods to investigate and model wind farm wake effects. However, these methods are costly and time-consuming to use commercially. In contrast, a simple analytical approach can provide reasonably accurate estimates of wake effects on flow and power. To reducing errors in wake modeling, one must calibrate the model based on a specific wind farm setting. The purpose of this research is to develop a calibration procedure for wind farm wake modeling using a simple analytical approach and wind turbine Operational Data obtained from the Supervisory Control And Data Acquisition (SCADA) system. The proposed procedure uses a Gaussian-based analytical wake model and wake superposition model. The wake growth rate varies across the wind farm based on the local streamwise turbulence intensity. The wake model was calibrated by implementing the proposed procedure with turbine pairs within the wind farm. The performance of the model was validated at an onshore wind farm in Iowa, USA. The results were compared with the industry standard wind farm wake model and shown to result in an approximate 1% improvement in sitewide total power prediction. This new SCADA-based calibration procedure is useful for real-time wind farm Operational optimization.

A. Rodionov - One of the best experts on this subject based on the ideXlab platform.

Related terms

Operational Data - 15 days free trial to Access Experts & Abstracts