A Novel PSOS-CGSA Method for State Estimation in Unbalanced DG-Integrated Distribution Systems

This paper proposes a novel optimization method, namely, hybrid PSOS-CGSA for state estimation in three-phase unbalanced DG-integrated distribution systems. The distribution system state estimation (DSSE) is formulated as a nonlinear optimization problem with constraints where loads and DG outputs are considered as the control variables, while real-time measurements are treated as dependent variables. The proposed DSSE model estimates the loads and DG outputs at each bus by minimizing the difference between the measure and calculated values of the variables monitored in real-time. A novel hybrid algorithm of particle swarm optimization with sigmoid-based acceleration coefficients and chaotic gravitational search algorithm (PSOS-CGSA) is proposed and applied for the DG-integrated DSSE. The feasibility of the proposed approach is verified on the IEEE 13-bus test system, the IEEE 37-bus test system, and the IEEE 123-bus test system. These simulations show that the proposed DSSE model provides reliable and accurate state estimation of DG-integrated distribution systems with a very limited number of real-time measurements at the source substation. The results obtained by proposed hybrid PSOS-CGSA are evaluated by comparing with other methods under the same test conditions, and the obtained results demonstrate the merits of the proposed scheme.

[1]  Wanxing Sheng,et al.  Unbalanced three-phase distribution state estimation using cooperative particle swarm optimization , 2014, 2014 IEEE PES T&D Conference and Exposition.

[2]  Ubiratan Holanda Bezerra,et al.  Full-Observable Three-Phase State Estimation Algorithm Applied to Electric Distribution Grids , 2019, Energies.

[3]  Zhongzhi Shi,et al.  Chaotic particle swarm optimization with sigmoid-based acceleration coefficients for numerical function optimization , 2019, Swarm Evol. Comput..

[4]  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.

[5]  M. Majidi,et al.  Distribution system state estimation using compressive sensing , 2017 .

[6]  Muhammad Tariq,et al.  A novel ANN-based distribution network state estimator , 2019 .

[7]  A. J. Urdaneta,et al.  A Hybrid Particle Swarm Optimization for Distribution State Estimation , 2002, IEEE Power Engineering Review.

[8]  Jordan Radosavljević,et al.  Metaheuristic Optimization in Power Engineering , 2018 .

[9]  Taher Niknam,et al.  A new smart approach for state estimation of distribution grids considering renewable energy sources , 2016 .

[10]  Li Li,et al.  Phasor particle swarm optimization: a simple and efficient variant of PSO , 2018, Soft Computing.

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

[12]  M. Fadali,et al.  Distribution systems state estimation using sparsified voltage profile , 2016 .

[13]  Gang Wang,et al.  Distribution system state estimation: an overview of recent developments , 2019, Frontiers of Information Technology & Electronic Engineering.

[14]  Jan-Hendrik Menke,et al.  Distribution System Monitoring for Smart Power Grids with Distributed Generation Using Artificial Neural Networks , 2018, International Journal of Electrical Power & Energy Systems.

[15]  N.N. Schulz,et al.  Development of Three-Phase Unbalanced Power Flow Using PV and PQ Models for Distributed Generation and Study of the Impact of DG Models , 2007, IEEE Transactions on Power Systems.

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

[17]  Magdy M. A. Salama,et al.  A novel smart meter technique for voltage and current estimation in active distribution networks , 2019 .

[18]  Amir Hossein Gandomi,et al.  Chaotic gravitational constants for the gravitational search algorithm , 2017, Appl. Soft Comput..

[19]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

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

[21]  Manoel Firmino de Medeiros Junior,et al.  A three-phase algorithm for state estimation in power distribution feeders based on the powers summation load flow method , 2015 .

[22]  P. M. De Oliveira-De Jesus,et al.  A detailed network model for distribution systems with high penetration of renewable generation sources , 2018 .

[23]  J.F.G. Cobben,et al.  Three-phase state estimation in the medium-voltage network with aggregated smart meter data , 2018 .

[24]  Fei Wang,et al.  Enhanced state estimation and bad data identification in active power distribution networks using photovoltaic power forecasting , 2019 .

[25]  Adisa A. Jimoh,et al.  Particle swarm optimization for power system state estimation , 2015, Neurocomputing.

[26]  Andrija T. Saric,et al.  A three-phase state estimation in active distribution networks , 2014 .

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

[28]  Li Fei,et al.  Field testing of distribution state estimator , 2013 .

[29]  S. Mirjalili,et al.  A new hybrid PSOGSA algorithm for function optimization , 2010, 2010 International Conference on Computer and Information Application.

[30]  Shaorong Wang,et al.  A Solution to the Optimal Power Flow Problem Considering WT and PV Generation , 2019, IEEE Access.