Statistical Criteria for Evaluation of Distribution System State Estimators

The increasing penetration of distributed energy resources and electric vehicles, along with the strict requirements for power quality and system reliability have changed the way distribution systems are planned and operate. Therefore, it is of great importance for the Distribution Management System (DMS) to have functions adequately designed to analyze distribution systems. As the state estimator acts like a filter for the real-time measurements, it will constitute the core of the DMS. In this context, this paper presents a set of statistical criteria, namely bias, consistency and quality, that can be used to properly evaluate distribution system state estimators. The application of these criteria is illustrated considering four well-known distribution system state estimators on the IEEE 34bus distribution feeder. Additionally, the performance of these estimators in terms of accuracy and computational efficiency is analyzed and discussed. The purpose of the paper is to highlight the importance of considering more strict criteria for assessing distribution system state estimators, rather than just number of iterations, execution times and the difference between estimated and true states.

[1]  N.N. Schulz,et al.  A revised branch current-based distribution system state estimation algorithm and meter placement impact , 2004, IEEE Transactions on Power Systems.

[2]  Davide Della Giustina,et al.  Electrical distribution system state estimation: measurement issues and challenges , 2014, IEEE Instrumentation & Measurement Magazine.

[3]  Mario Paolone,et al.  State estimation of Active Distribution Networks: Comparison between WLS and iterated kalman-filter algorithm integrating PMUs , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).

[4]  Bikash C. Pal,et al.  Choice of estimator for distribution system state estimation , 2009 .

[5]  Paolo Attilio Pegoraro,et al.  Efficient Branch-Current-Based Distribution System State Estimation Including Synchronized Measurements , 2013, IEEE Transactions on Instrumentation and Measurement.

[6]  A. Leon-Garcia,et al.  Probability, statistics, and random processes for electrical engineering , 2008 .

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

[8]  Mamdouh Abdel-Akher,et al.  Fault Analysis of Multiphase Distribution Systems Using Symmetrical Components , 2010, IEEE Transactions on Power Delivery.

[9]  Ganesh Kumar Venayagamoorthy,et al.  Dishonest Gauss Newton method based power system state estimation on a GPU , 2016, 2016 Clemson University Power Systems Conference (PSC).

[10]  Roger C. Dugan,et al.  An open source platform for collaborating on smart grid research , 2011, 2011 IEEE Power and Energy Society General Meeting.

[11]  Paolo Attilio Pegoraro,et al.  Performance of three-phase WLS Distribution System State Estimation approaches , 2015, 2015 IEEE International Workshop on Applied Measurements for Power Systems (AMPS).

[12]  M. Pau,et al.  WLS distribution system state estimator based on voltages or branch-currents: Accuracy and performance comparison , 2013, 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

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

[14]  Chan-Nan Lu,et al.  A Review on Distribution System State Estimation , 2017, IEEE Transactions on Power Systems.

[15]  A. Monticelli State estimation in electric power systems : a generalized approach , 1999 .

[16]  Kaveh Dehghanpour,et al.  A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems , 2018, IEEE Transactions on Smart Grid.

[17]  Madson C. de Almeida,et al.  Specifying angular reference for three-phase distribution system state estimators , 2017 .

[18]  Chan Nan Lu,et al.  Performance comparison of distribution system state estimation methods , 2016, 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia).

[19]  Luis F. Ochoa,et al.  An Improved Three-Phase AMB Distribution System State Estimator , 2017, IEEE Transactions on Power Systems.

[20]  J. Teng,et al.  Distribution system state estimation , 1995 .

[21]  Tiago R. Ricciardi,et al.  Contributions to the sequence‐decoupling compensation power flow method for distribution system analysis , 2019, IET Generation, Transmission & Distribution.