Expert Library-Based Data Verification for National-Regional-Provincial Power Grid Integration Security Correction

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Abstract:

With the construction of national UHV power grid, the grid structure has become increasingly complex, and the national regional and provincial power grid has become increasingly interlinked. In order to more accurately grasp the future operation, grid security and stability, and clearly grasped the weak links of the grid the State Grid Corporation launched a NRPPGI (national-regional-provincial power grid Integration, NRPPGI) model in Security Correction. Data Verification is an important data prevention mechanism of NRPPGI Security Correction. In view of the characteristics and requirements of NRPPGI security checking data, this paper presents a data verification method based on expert library. The system power balance, the unit operating constraints, data completeness and rationality are considered as the quality Index of the received data. This paper also has data correction based on the verification results of the expert library. Verified by actual grid operation, the calibration data can significantly improve the calculation accuracy and convergence rate in the security correction and dispatching plan compared to the original data.

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Periodical:

Advanced Materials Research (Volumes 805-806)

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1087-1092

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September 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] ZHANG Zhigang, XIA Qing. Architecture and key technologies for generating scheduling of smart grid[J]. Power System Technology, 2009, 33(20): 1-8.

Google Scholar

[2] YE Feng , HE Hua, GU Quan, et al. Bad date identification and correction for load forecasting data in energy management system [J]. Automation of Electric Power Systems, 2006, 30(15): 85-88.

Google Scholar

[3] GE Zhaoqiang, WANG Dexing, GE Minhui, et al. Security Checking System for Daily Generation Scheduling of East China Power Grid and Its Expansion[J]. Automation of Electric Power Systems, 2008, 32(10): 45-48.

DOI: 10.1109/drpt.2008.4523405

Google Scholar

[4] XIAO Jun, SHI Changkai, LUO Fengzhang, et al. Rule- based Verification and Correction Methods for Exported Data from Distribution GIS[J]. Automation of Electric Power Systems, 2007, 31(10): 76-81.

Google Scholar

[5] ZHANG Liang. Research on data quality for electric power dispatch data centers [J]. East China Electric Power, 2009, 37(3): 403-406.

Google Scholar

[6] Men Deyue. Research on Grid Monthly Load Forecasting Based on Support Vector Machine[D]. North China Electric Power University , 2012, Beijing.

Google Scholar

[7] Men Deyue,Liu Wenying.Application of Least Squares Support Vector Machine (LS-SVM) Based on Time Series in Power System Monthly Load Forecasting [A]. In:APPEEC[C],2011: 1120-1124.

DOI: 10.1109/appeec.2011.5748632

Google Scholar