Modified Weighted Least Squares Method to Improve Active Distribution System State Estimation

Authors

  • A. Sheikholeslami Department of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran.
  • A. Zakariazadeh Department of Electrical Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran.
  • M. Ajoudani Department of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran.
Abstract:

The development of communications and telecommunications infrastructure, followed by the extension of a new generation of smart distribution grids, has brought real-time control of distribution systems to electrical industry professionals’ attention. Also, the increasing use of distributed generation (DG) resources and the need for participation in the system voltage control, which is possible only with central control of the distribution system, has increased the importance of the real-time operation of distribution systems. In real-time operation of a power system, what is important is that since the grid information is limited, the overall grid status such as the voltage phasor in the buses, current in branches, the values of loads, etc. are specified to the grid operators. This can occur with an active distribution system state estimation (ADSSE) method. The conventional method in the state estimation of an active distribution system is the weighted least squares (WLS) method. This paper presents a new method to modify the error modeling in the WLS method and improve the accuracy SVs estimations by including load variations (LVs) during measurement intervals, transmission time of data to the information collection center, and calculation time of the state variables (SVs), as well as by adjusting the variance in the smart meters (SM). The proposed method is tested on an IEEE 34-bus standard distribution system, and the results are compared with the conventional method. The simulation results reveal that the proposed approach is robust and reduces the estimation error, thereby improving ADSSE accuracy compared with the conventional methods.

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Journal title

volume 16  issue 4

pages  559- 572

publication date 2020-12

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