Intelligent DE algorithm for measurement location and PSO for bus voltage estimation in power distribution system

Abstract Accurate monitoring and estimating the state of the distribution system poses an immense challenge to power engineering researchers because of bidirectional distribution system. This paper is executed in two stage methodology. The initial stage is to identify the optimal location for the installation of monitoring instrument with minimal investment cost through DE and PSO. The second stage is to estimate the bus voltage magnitude where real time measurement is measured through identified meter location which is more essential for decision making in DSCADA. The hybrid Intelligent technique is applied to execute the above two algorithms. The algorithms are tested with IEEE and TNEB benchmark systems.

[1]  V. Cecchi,et al.  Instrumentation and Measurement of a Power Distribution System Laboratory for Meter Placement and Network Reconfiguration Studies , 2007, IEEE Instrumentation & Measurement Magazine.

[2]  Andrea Bernieri,et al.  Neural networks and pseudo-measurements for real-time monitoring of distribution systems , 1995 .

[3]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[4]  Biswarup Das Rule Based Algorithm for Meter Placement and ANN Based Bus Voltage Estimation in Radial Power Distribution System , 2005 .

[5]  M. Fotuhi-Firuzabad,et al.  Optimal Switch Placement in Distribution Systems Using Trinary Particle Swarm Optimization Algorithm , 2008, IEEE Transactions on Power Delivery.

[6]  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.

[7]  Karen Miu,et al.  Weighted least squares methods for load estimation in distribution networks , 2003 .

[8]  Andrew Knight Basics of MATLAB and Beyond , 1999 .

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

[10]  P. A. Crossley,et al.  Electrical Power System State Estimation Meter Placement—A Comparative Survey Report , 2008 .

[11]  F. Pilo,et al.  Optimal Placement of Measurement Devices in Electric Distribution Systems , 2006, 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings.

[12]  Jinxiang Zhu,et al.  Meter placement for real-time monitoring of distribution feeders , 1995 .

[13]  R. Jabr,et al.  Distribution system state estimation through Gaussian mixture model of the load as pseudo-measurement , 2010 .

[14]  B. Pal,et al.  Modelling of pseudo-measurements for distribution system state estimation , 2008 .

[15]  S.P. Chowdhury,et al.  Planning optimal intelligent metering for distribution system monitoring and control , 2008, 2008 Annual IEEE India Conference.

[16]  R. Vinter,et al.  Measurement Placement in Distribution System State Estimation , 2009, IEEE Transactions on Power Systems.

[17]  Ali Abur,et al.  Optimal Placement of Phasor Measurement Units for State Estimation , 2005 .

[18]  Yoshikazu Fukuyama,et al.  A hybrid particle swarm optimization for distribution state estimation , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[19]  Goran Strbac,et al.  Measurement location for state estimation of distribution networks with generation , 2005 .

[20]  S. Chowdhury,et al.  Distributed state estimation technique for active distribution networks , 2007, 2007 42nd International Universities Power Engineering Conference.

[21]  A.M. Ranjbar,et al.  Optimal Placement of Phasor Measurement Units: Particle Swarm Optimization Approach , 2007, 2007 International Conference on Intelligent Systems Applications to Power Systems.

[22]  M.B.D.C. Filho,et al.  Optimal metering systems for monitoring power networks under multiple topological scenarios , 2006, 2006 IEEE Power Engineering Society General Meeting.

[23]  J. Teng,et al.  A Novel ACS-Based Optimum Switch Relocation Method , 2002, IEEE Power Engineering Review.

[24]  Fred C. Schweppe,et al.  Power System Static-State Estimation, Part I: Exact Model , 1970 .

[25]  N. RamaRao,et al.  A new algorithm for power system state estimation , 1982, Proceedings of the IEEE.

[26]  Sergey Edward Lyshevski,et al.  Engineering and Scientific Computations Using MATLAB , 2003 .

[27]  Ali Zilouchian,et al.  Intelligent Control Systems Using Soft Computing Methodologies , 2000 .

[28]  Fabrizio Pilo,et al.  Optimal Allocation of Multichannel Measurement Devices for Distribution State Estimation , 2009, IEEE Transactions on Instrumentation and Measurement.

[29]  Mesut Baran,et al.  Distribution system state estimation using AMI data , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[30]  A. Abur,et al.  Power system state estimation , 2004 .