Multi-Phase State Estimation Featuring Industrial-Grade Distribution Network Models

This paper proposes a novel implementation of a multiphase distribution network state estimator which employs industrial-grade modeling of power components and measurements. Unlike the classical voltage-based and current-based state estimators, this paper presents the implementation details of a constrained weighted least squares state calculation method that includes standard three-phase state estimation capabilities in addition to practical modeling requirements from the industry; these requirements comprise multiphase line configurations, unsymmetrical and incomplete transformer connections, power measurements on ${ \triangle }$ -connected loads, cumulative-type power measurements, line-to-line voltage magnitude measurements, and reversible line drop compensators. The enhanced modeling equips the estimator with capabilities that make it superior to a recently presented state-of-the-art distribution network load estimator that is currently used in real-life distribution management systems; comparative performance results demonstrate the advantage of the proposed estimator under practical measurement schemes.

[1]  A. W. Kelley,et al.  State estimation for real-time monitoring of distribution systems , 1994 .

[2]  D. L. Lubkeman,et al.  Field results for a distribution circuit state estimator implementation , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

[3]  A. K. Ghosh,et al.  Load modeling for distribution circuit state estimation , 1996 .

[4]  Malcolm Irving,et al.  Robust algorithm for load estimation in distribution networks , 1998 .

[5]  Bikash C. Pal,et al.  Real Time Estimation of Loads in Radial and Unsymmetrical Three-Phase Distribution Networks , 2013, IEEE Transactions on Power Systems.

[6]  A. G. Expósito,et al.  Power system state estimation : theory and implementation , 2004 .

[7]  Jose Roberto Sanches Mantovani,et al.  Distribution System State Estimation Using the Hamiltonian Cycle Theory , 2016, IEEE Transactions on Smart Grid.

[8]  Ross Baldick,et al.  Applied Optimization: Formulation and Algorithms for Engineering Systems (Baldick, R.; 2006) , 2008, IEEE Control Systems.

[9]  G. Strbac,et al.  Distribution System State Estimation Using an Artificial Neural Network Approach for Pseudo Measurement Modeling , 2012, IEEE Transactions on Power Systems.

[10]  S. M. Shahidehpour,et al.  Practical aspects of distribution automation in normal and emergency conditions , 1993 .

[11]  M.E. Baran,et al.  A branch-current-based state estimation method for distribution systems , 1995 .

[12]  M. K. Celik,et al.  A practical distribution state calculation algorithm , 1999, IEEE Power Engineering Society. 1999 Winter Meeting (Cat. No.99CH36233).

[13]  Milan Prodanovic,et al.  A Closed-Loop State Estimation Tool for MV Network Monitoring and Operation , 2015, IEEE Transactions on Smart Grid.

[14]  Bikash C. Pal,et al.  Three-phase state estimation using hybrid particle swarm optimization , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[15]  S. M. Shahidehpour,et al.  State estimation for electric power distribution systems in quasi real-time conditions , 1993 .

[16]  Fred Denny,et al.  Distribution System Modeling and Analysis , 2001 .

[17]  Rabih A. Jabr,et al.  Transformer Modeling for Three-Phase Distribution Network Analysis , 2015, IEEE Transactions on Power Systems.

[18]  Jos Arrillaga,et al.  Computer Analysis of Power Systems , 1990 .

[19]  Jianzhong Wu,et al.  A robust state estimator for medium voltage distribution networks , 2013, IEEE Transactions on Power Systems.

[20]  J. Arrillaga,et al.  Computer Analysis of Power Systems: Arrillaga/Computer Analysis of Power Systems , 1990 .

[21]  Bikash C. Pal,et al.  Three-Phase State Estimation Using Hybrid Particle Swarm Optimization , 2017, IEEE Transactions on Smart Grid.