Wind speed severity scale model applied to overhead line reliability simulation

Abstract Wind-induced failures on electrical networks has damaging effects on utilities’ reliability performance, especially those operating long feeders, mainly composed of overhead lines and exposed to different wind profiles along the feeder. Traditional reliability models rely on failure and repair rates to reproduce the interruption cycles of components and, during the planning phase, these are assumed constant over time and causeless. Therefore, wind-related failures and wind conditions that characterize the operation of a particular system are not fully addressed in the evaluation. The methodology proposed adopts an original 5-wind speed severity scale and presents a probabilistic wind model for reliability studies that takes advantage of the utility reliability and wind speed data to extract the overhead lines wind-related failure rates and, then, a simulation technique to reproduce the random behavior of the components is combined with a 10 min time-resolution wind speed time series to recreate the wind interference with the grid. This way, overhead lines and the overall reliability of the system is tested using historical wind speed time series instead of traditional weather models. Consequences of adverse wind periods in terms of repair times are addressed as well as regional effects of wind by emulating simultaneous wind profiles in the grid. Simulation outcomes for two systems demonstrate that the method proposed can be valuable when assessing the reliability of overhead lines, considering wind interference, making it suitable for reliability studies.

[1]  F.A.B. Lemos,et al.  Forced Outage Cause Identification Based on Bayesian Networks , 2007, 2007 IEEE Lausanne Power Tech.

[2]  Anil Pahwa,et al.  Modeling Weather-Related Failures of Overhead Distribution Lines , 2006, IEEE Transactions on Power Systems.

[3]  K Alvehag,et al.  A Reliability Model for Distribution Systems Incorporating Seasonal Variations in Severe Weather , 2011, IEEE Transactions on Power Delivery.

[4]  Richard E. Brown,et al.  Electric Power Distribution Reliability , 2002 .

[5]  Wenyuan Li,et al.  Reliability Assessment of Electric Power Systems Using Monte Carlo Methods , 1994 .

[6]  Gerd Kjolle,et al.  Wind dependent failure rates for overhead transmission lines using reanalysis data and a Bayesian updating scheme , 2016, 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).

[7]  J. Oliver,et al.  The Encyclopedia of World Climatology , 2005 .

[8]  Xiaofu Xiong,et al.  Time-varying failure rate simulation model of transmission lines and its application in power system risk assessment considering seasonal alternating meteorological disasters , 2016 .

[9]  Pierluigi Mancarella,et al.  Influence of extreme weather and climate change on the resilience of power systems: Impacts and possible mitigation strategies , 2015 .

[10]  Friedrich Kiessling Overhead Power Lines: Planning, Design, Construction , 2003 .

[11]  Robert Whapham Aeolian Vibration of Conductors: Theory, Laboratory Simulation & Field Measurement , 2012 .

[12]  Roy Billinton,et al.  Reliability Evaluation of Engineering Systems , 1983 .

[13]  D. Issicaba,et al.  Impact evaluation of the network geometric model on power quality indices using probabilistic techniques , 2016, 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).

[14]  Rod A. Herman,et al.  Using probability distribution functions in reliability analyses , 2011 .

[15]  Jean-Louis Lilien,et al.  Wake-Induced Vibration in Power Transmission Line. Parametric study , 2004 .

[16]  Roy Billinton,et al.  A reliability test system for educational purposes-basic distribution system data and results , 1991 .

[17]  Roy Billinton,et al.  Reliability evaluation of power systems , 1984 .

[18]  A. D. Patton,et al.  Power System Reliability I-Measures of Reliability and Methods of Calculation , 1964 .

[19]  Pierluigi Mancarella,et al.  Power System Resilience to Extreme Weather: Fragility Modeling, Probabilistic Impact Assessment, and Adaptation Measures , 2017, IEEE Transactions on Power Systems.