Sensor Placement for Structural Monitoring of Transmission Line Towers

Transmission line towers are usually analyzed using linear elastic idealized truss models. Due to the assumptions used in the analysis, there are discrepancies between the actual results obtained from full scale prototype testing and the analytical results. Therefore, design engineers are interested in assessing the actual stress levels in transmission line towers. Since it is costly to place sensors on every member of a tower structure, the best locations for sensors need to be carefully selected. This study evaluates a methodology for sensor placement in transmission line towers. The objective is to find optimal locations for sensors such that the real behavior of the structure can be explained from measurements. The methodology is based on the concepts of entropy and model falsification. Sensor locations are selected based on maximum entropy such that there is maximum separation between model instances that represent different possible combinations of parameter values which have uncertainties. The performance of the proposed algorithm is compared to that of an intuitive method in which sensor locations are selected where the forces are maximum. A typical 220 kV transmission tower is taken as case study in this paper. It is shown that the intuitive method results in much higher number of non-separable models compared to the optimal sensor placement algorithm. Thus the intuitive method results in poor identification of the system.

[1]  Ian F. C. Smith,et al.  A model-based data-interpretation framework for improving wind predictions around buildings , 2015 .

[2]  Michele Meo,et al.  On the optimal sensor placement techniques for a bridge structure , 2005 .

[3]  N. Prasad Rao,et al.  Non-linear behaviour of lattice panel of angle towers , 2001 .

[4]  Ian F. C. Smith,et al.  Hierarchical Sensor Placement Using Joint Entropy and the Effect of Modeling Error , 2014, Entropy.

[5]  X. Rosalind Wang,et al.  Optimising Sensor Layouts for Direct Measurement of Discrete Variables , 2009, 2009 Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems.

[6]  Ian F. C. Smith,et al.  Quantifying the Effects of Modeling Simplifications for Structural Identification of Bridges , 2014 .

[7]  Haibo Chen,et al.  Effect of Bolt Slippage and Joint Eccentricity on the Response of Lattice Structure with Non-Uniform Settlement , 2013 .

[8]  I. Smith,et al.  Structural identification with systematic errors and unknown uncertainty dependencies , 2013 .

[9]  Munusamy Selvaraj,et al.  Analysis and experimental testing of a built-up composite cross arm in a transmission line tower for mechanical performance , 2013 .

[10]  Heung-Fai Lam,et al.  How to Install Sensors for Structural Model Updating , 2011 .

[11]  M. E. Kartal Effects of Semi-Rigid Connection on Structural Responses , 2010 .

[12]  Mohammad Azarbayejani,et al.  A probabilistic approach for optimal sensor allocation in structural health monitoring , 2008 .

[13]  Ian F. C. Smith,et al.  Predicting the usefulness of monitoring for identifying the behavior of structures , 2013 .

[14]  D. Kammer,et al.  Enhancement of On-Orbit Modal Identification of Large Space Structures Through Sensor Placement , 1994 .

[15]  Ian F. C. Smith,et al.  Configuration of measurement systems using Shannon's entropy function , 2005 .

[16]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[17]  Vinay R.B Optimization of Transmission Line Towers:P-Delta Analysis , 2014 .

[18]  Eric B. Flynn,et al.  A Bayesian approach to optimal sensor placement for structural health monitoring with application to active sensing , 2010 .

[19]  An. Afandi OPTIMIZATION OF TRANSMISSION LINES USING LINEAR PROGRAMMING , 2009 .

[20]  Costas Papadimitriou,et al.  Optimal sensor placement methodology for parametric identification of structural systems , 2004 .

[21]  Reidar Bjorhovde,et al.  Classification System for Beam‐to‐Column Connections , 1990 .

[22]  Firdaus E. Udwadia,et al.  A Methodology for Optimal Sensor Locations for Identification of Dynamic Systems , 1978 .

[23]  J. Beck Bayesian system identification based on probability logic , 2010 .