Improving observability using optimal placement of phasor measurement units

Abstract State estimator is crucial for on-line power system monitoring, analysis and control. With the increasing use of synchronized phasor measurement units (PMUs) in power grids, utilization of phasor measurements to improve the precision and observability of state estimator becomes imperative. However, for state estimation, the PMUs should be placed appropriately in the network. In this paper, a novel state estimator for minimizing the size of the PMU configuration while allowing full observability of the network is proposed. The proposed approach initially finds the best configuration of PMUs for observability. Then a novel meta-heuristic algorithm called improved fruit fly optimization method is used to determine the minimum number of phasor measurement units that can sustain observability. This methodology is tested on IEEE 14, 24, 30, 57 and 108 bus systems and the results are compared with those found in literature. Results obtained validate the versatility of the approach to deliver reliable and accurate measurements for the standard test systems under study.

[1]  A. Conejo,et al.  An efficient algebraic approach to observability analysis in state estimation , 2010 .

[2]  Suttichai Premrudeepreechacharn,et al.  An Optimal PMU Placement Method Against Measurement Loss and Branch Outage , 2007 .

[3]  Umi Kalthum Ngah,et al.  A simulation based fly optimisation algorithm for swarms of mini autonomous surface vehicles application , 2011 .

[4]  Yuanzhan Sun,et al.  Optimal PMU placement for full network observability using Tabu search algorithm , 2006 .

[5]  Ross Baldick,et al.  State Estimator Condition Number Analysis , 2001 .

[6]  P. Komarnicki,et al.  PMU placement method based on decoupled newton power flow and sensitivity analysis , 2007, 2007 9th International Conference on Electrical Power Quality and Utilisation.

[7]  A.G. Phadke,et al.  Phasor measurement unit placement techniques for complete and incomplete observability , 2005, IEEE Transactions on Power Delivery.

[8]  Federico Milano,et al.  A security oriented approach to PMU positioning for advanced monitoring of a transmission grid , 2002, Proceedings. International Conference on Power System Technology.

[9]  Demetrios G. Eliades,et al.  Placement of Synchronized Measurements for Power System Observability , 2009, IEEE Transactions on Power Delivery.

[10]  Huiru Zhao,et al.  Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm , 2012 .

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

[12]  M. Gilles,et al.  Observability analysis: a new topological algorithm , 1991 .

[13]  Vijay Vittal,et al.  Solution for the crisis in electric power supply , 2001 .

[14]  Li Ying,et al.  Sensitivity Constrained PMU Placement for Complete Observability of Power Systems , 2005, 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific.

[15]  Pierre Borne,et al.  Firefly Algorithm for Economic Power Dispatching With Pollutants Emission , 2012 .

[16]  A. Conejo,et al.  Observability analysis in state estimation: a unified numerical approach , 2006, IEEE Transactions on Power Systems.

[17]  A.V. Garcia,et al.  On the Use of Gram Matrix in Observability Analysis , 2008, IEEE Transactions on Power Systems.

[18]  G. Korres,et al.  A Hybrid Method for Observability Analysis Using a Reduced Network Graph Theory , 2002, IEEE Power Engineering Review.

[19]  Babak Mozafari,et al.  Optimal placement of PMUs to maintain network observability using a modified BPSO algorithm , 2011 .

[20]  P. S. Georgilakis,et al.  Optimal placement of phasor measurement units: A literature review , 2011, 2011 16th International Conference on Intelligent System Applications to Power Systems.

[21]  A. Abur,et al.  Observability analysis and measurement placement for systems with PMUs , 2004, IEEE PES Power Systems Conference and Exposition, 2004..

[22]  M. Begovic,et al.  Nondominated Sorting Genetic Algorithm for Optimal Phasor Maesurement Placement , 2002 .

[23]  B. Gou,et al.  Generalized Integer Linear Programming Formulation for Optimal PMU Placement , 2008, IEEE Transactions on Power Systems.

[24]  Atte Moilanen,et al.  Simulated Evolutionary Optimization and Local Search: Introduction and Application to Tree Search , 2001 .

[25]  T. Baldwin,et al.  Power system observability with minimal phasor measurement placement , 1993 .

[26]  Theofanis Apostolopoulos,et al.  Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem , 2011 .

[27]  M. Shahidehpour,et al.  Contingency-Constrained PMU Placement in Power Networks , 2010, IEEE Transactions on Power Systems.

[28]  Mehrdad Tarafdar Hagh,et al.  Minimization of load shedding by sequential use of linear programming and particle swarm optimization , 2011 .

[29]  M. Begovic,et al.  Nondominated sorting genetic algorithm for optimal phasor measurement placement , 2002, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[30]  Felix F. Wu,et al.  Network Observability: Theory , 1985, IEEE Power Engineering Review.

[31]  Wen-Tsao Pan,et al.  A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example , 2012, Knowl. Based Syst..

[32]  P. S. Georgilakis,et al.  Taxonomy of PMU Placement Methodologies , 2012, IEEE Transactions on Power Systems.

[33]  G. R. Krumpholz,et al.  The Solution of Ill-Conditioned Power System State Estimation Problems Via the Method of Peters and Wilkinson , 1983, IEEE Transactions on Power Apparatus and Systems.

[34]  B. Gou.  Optimal Placement of PMUs by Integer Linear Programming , 2008, IEEE Transactions on Power Systems.

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