Optimal Meter Placement for Robust Measurement Systems in Active Distribution Grids

Future active distribution grids are characterized by rapid and significant changes of operation and behavior due to, for example, intermittent power injections from renewable sources and the load-generation characteristic of the so-called prosumers. The design of a robust measurement infrastructure is critical for safe and effective grid control and operation. We had earlier proposed a placement procedure that allows finding an optimal robust measurement location incorporating phasor measurement units and smart metering devices for distribution system state estimation. In this paper, the lack of detailed information on distributed generation is also considered in the optimal meter placement procedure, so that the distributed measurement system can provide accurate estimates even with limited knowledge of the profile of the injected power. Possible non-Gaussian distribution of the distributed power generation has been taken into account. With this aim, the Gaussian mixture model has been incorporated into the placement optimization by means of the so-called Gaussian component combination method. The occurrence of either loss of data or degradation of metrological performance of the measurement devices is also considered. Tests performed on a UKGDS 16-bus distribution network are presented and discussed.

[1]  E.P.M. Brown,et al.  Representation of non-Gaussian probability distributions in stochastic load-flow studies by the method of Gaussian sum approximations , 1983 .

[2]  Giovanni Artale,et al.  Medium Voltage Smart Grid: Experimental Analysis of Secondary Substation Narrow Band Power Line Communication , 2013, IEEE Transactions on Instrumentation and Measurement.

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

[4]  B. Gou,et al.  An Improved Measurement Placement Algorithm for Network Observability , 2001, IEEE Power Engineering Review.

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

[6]  A. Feijoo,et al.  Probabilistic Load Flow Including Wind Power Generation , 2011, IEEE Transactions on Power Systems.

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

[8]  M. Paolone,et al.  Synchronized phasors monitoring during the islanding maneuver of an active distribution network , 2010, 2010 Innovative Smart Grid Technologies (ISGT).

[9]  Lihua Xie,et al.  An Efficient EM Algorithm for Energy-Based Multisource Localization in Wireless Sensor Networks , 2011, IEEE Transactions on Instrumentation and Measurement.

[10]  Felix F. Wu,et al.  Network Observability: Identification of Observable Islands and Measurement Placement , 1985, IEEE Power Engineering Review.

[11]  A.G. Phadke,et al.  An Alternative for Including Phasor Measurements in State Estimators , 2006, IEEE Transactions on Power Systems.

[12]  Xin-She Yang,et al.  Engineering Optimization: An Introduction with Metaheuristic Applications , 2010 .

[13]  Hong-Yi Fan,et al.  Quasi-probability distribution in the coherent thermal state representation and its time evolution , 2002 .

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

[15]  Paolo Attilio Pegoraro,et al.  On the robustness in distribution system state estimation , 2012, 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

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

[17]  S. Borlase,et al.  The evolution of distribution , 2009, IEEE Power and Energy Magazine.

[18]  Renke Huang,et al.  Smart Grid Technologies for Autonomous Operation and Control , 2011, IEEE Transactions on Smart Grid.

[19]  Junqi Liu,et al.  Trade-Offs in PMU Deployment for State Estimation in Active Distribution Grids , 2012, IEEE Transactions on Smart Grid.

[20]  R. Vinter,et al.  Meter Placement for Distribution System State Estimation: An Ordinal Optimization Approach , 2011 .

[21]  G. Strbac,et al.  Distribution System State Estimation Using an Artificial Neural Network Approach for Pseudo Measurement Modeling , 2012, IEEE Transactions on Power Systems.

[22]  Kari Mäki,et al.  ADINE - EU Demonstration Project of Active Distribution Network , 2008 .

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

[24]  Gerald T. Heydt,et al.  Versatile platforms for wide area synchronous measurements in power distribution systems , 2010, North American Power Symposium 2010.

[25]  Paolo Attilio Pegoraro,et al.  Robustness-Oriented Meter Placement for Distribution System State Estimation in Presence of Network Parameter Uncertainty , 2013, IEEE Transactions on Instrumentation and Measurement.

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

[27]  Junqi Liu,et al.  PMU and smart metering deployment for state estimation in active distribution grids , 2012, 2012 IEEE International Energy Conference and Exhibition (ENERGYCON).

[28]  Junqi Liu,et al.  Optimal placement for robust distributed measurement systems in active distribution grids , 2013, 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[29]  G. Valverde,et al.  Stochastic Monitoring of Distribution Networks Including Correlated Input Variables , 2013, IEEE Transactions on Power Systems.

[30]  Albert Moser,et al.  Optimized positioning of measurements in distribution grids , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).

[31]  R. Jabr,et al.  Statistical Representation of Distribution System Loads Using Gaussian Mixture Model , 2010 .