This speech explores the sensitivity analysis of sensor networks for distribution grid

Burt, Graeme

Clarkson, Paul

Forbes, Alistair

Roscoe, Andrew

Venturi, Alberto

Wright, Paul

Yang, Xin-She

English

Name not available

Sensitivity analysis of sensor networks for distribution grids

University of Strathclyde Institutional Repository

Strathprints Institutional RepositoryClarkson, Paul and Venturi, Alberto and Forbes, Alistair and Yang, Xin-She and Wright, Paul andRoscoe, Andrew and Burt, Graeme (2013) Sensitivity analysis of sensor networks for distributiongrids. In: EU EURAMET EMRP Metrology for Smart Grids Workshop, 25-26 June 2013, 2013-07-25- 2013-07-26, Noordwijk.Strathprints is designed to allow users to access the research output of the University of Strathclyde.Copyright c© and Moral Rights for the papers on this site are retained by the individual authorsand/or other copyright owners. You may not engage in further distribution of the material for anyprofitmaking activities or any commercial gain. You may freely distribute both the url (http://strathprints.strath.ac.uk/) and the content of this paper for research or study, educational, ornot-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to Strathprints administrator:mailto:strathprints@strath.ac.ukhttp://strathprints.strath.ac.uk/Sensitivity Analysis of Sensor Networks for Distribution Grids   Paul Clarkson, Alberto Venturi, Alistair Forbes, Xin-She Yang, Paul Wright, Andrew Roscoe, Graeme Burt 26 June, 2013 Sensor Coverage in Electricity Networks   How many Sensors are Needed ? Which sensors can be removed ?  Estimate unknown Cable impedances What happens when a given sensor fails ?  How to handle Too much data ? How accurate Should a given Sensor be ? State estimation  From limited power flow and voltage measurements derive voltage magnitudes and angles at each bus  Derive power flow at every node in the network from estimated impedances and calculated voltages. Active power flow Reactive power flow Voltage magnitude Voltage angle Z1 Z2 Z3 Z4 Z5 Z6 Sensitivity Analysis Suppose the state estimation problem is given as    Where:  x are the parameters to be estimated  H is a (linearized)  observation matrix  E is a  constraint matrix  z and y are vectors storing measured data values  The solution depends linearly on y and z:  And the variance matrix is:  (z and y supposed uncorrelated)  The matrices Sz and Sy show precisely how the uncertainties associated with the data vectors z and y contribute to the uncertainties associated with the parameter estimates x.   )()(min T xzxzx HH   yx ESubject  to:  yzx yz SS  TTyyyzzx SVSSVSV z Sensitivity analysis Z1 Z2 Z3 Z4 Z5 Z6 Active power flow Reactive power flow Voltage magnitude Voltage angle SVm2,Qf1 Z1 Z2 Z3 Z4 Z5 Z6 Bus2 Bus3 Bus4 Bus5 Bus6 Bus7 Bus1 SVm4,Qf1 SVm3,Qf1 SVm6,Qf1 SVm5,Qf1 SVm7,Qf1 SVm2,Vm2 SVm3,Vm2 SVm4,Vm2 SVm5,Vm2 SVm6,Vm2 SVm7,Vm2 Estimates Measurements Example simulated networks Network 1 – United Kingdom Generic Distribution System (UKGDS) 77 bus network Example simulated networks Network 2 –UKGDS 290 bus network Example simulated networks Network 3 – IEEE 30 bus system Example simulated networks Network 4 – IEEE 300 bus system Choosing optimum measurement locations using sensitivity analysis Network 1 Choose positions of high accuracy “real” measurements and low accuracy “pseudo” measurements Choosing optimum measurement locations using sensitivity analysis Network 2 Choose positions of high accuracy “real” measurements and low accuracy “pseudo” measurements Choosing optimum measurement locations using sensitivity analysis Network 3 Choose positions of high accuracy “real” measurements and low accuracy “pseudo” measurements Choosing optimum measurement locations using sensitivity analysis Network 4 Choose positions of high accuracy “real” measurements and low accuracy “pseudo” measurements University of Strathclyde experimental smart-grid Control system Network model Testing on microgrid Active power flow Reactive power flow Voltage magnitude Voltage angle Apply state estimation Apply sensitivity analysis  Measurement uncertainty  Measurement error  Start with fully instrumented grid Start with fully instrumented grid Average measurement error of 1.7 % Average state variable uncertainty of 0.4 % Active power flow Reactive power flow Voltage magnitude Voltage angle Testing on microgrid Apply algorithm to find optimum minimum measurement set Average measurement error of 1.0 % Average state variable uncertainty of 0.7 % Active power flow Reactive power flow Voltage magnitude Voltage angle Testing on microgrid Compare with random selection of measurements Average measurement error of 3.1 % Average state variable uncertainty of 2.2 % Active power flow Reactive power flow Voltage magnitude Voltage angle Testing on microgrid Microgrid measurement errors Comparison of measurement error and state variable uncertainty Minimum set from sensitivity analysis Fully instrumented network Summary  Successfully modelled Strathclyde microgrid in MATLAB  Applied state estimator to Strathclyde microgrid and larger simulated networks  Verified uncertainty calculations against monte-carlo calculations  Expanded state estimator to include uncertain impedances in the network interconnections  Verified state variable uncertainty calculations against real measurements Next steps  Improve speed of optimal measurement placement algorithm  Sensitivity analysis with cable impedances included  Apply techniques to estimate line impedances  Expand to include unbalanced networks  Test with distributed generation included  Investigate use of PMU and smart meter data.  Try on full size real networks – need to engage with DNO 

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Sensitivity analysis of sensor networks for distribution grids

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