Impact Assessment of Various Wind Speeds on Dynamic Thermal Rating of the Terrain-Located EHV Power Grids: A Case of Valley in Taiwan

The dynamic thermal rating (DTR) of an extra-high voltage (EHV) power system is an important safety factor during dispatching power flow. The maximum line ampacity of an EHV power grid is closely related to the line temperature which can be calculated based on the real-time weather data from meteorological observation stations through the IEEE Std. 738. However, the impacts brought by varying terrains on wind speeds are not considered, which easily lead to an inaccurate estimation of line temperature and DTR. Thus, this paper used the long-term historical wind speed data to uncover which kinds of terrains might cause the risk of inaccurately estimating line temperature based on the DTR model. Then, we proposed an artificial neural network-based terrain-type wind speed correction model so that the line temperature can be estimated accurately even using weather data from climate grid. A case study illustrates that the proposed model can effectively evaluate the wind speed of a specific valley where EHV power line was deployed and further improve the accuracy of estimating its line temperature. This fact suggests that the line temperature estimated by our method can serve as a reliable reference for the power dispatching strategy.

[1]  Jonathan Ruel,et al.  Research note. Effect of topography on wind behaviour in a complex terrain , 1998 .

[2]  J Fu,et al.  Wind cooling effect on dynamic overhead line ratings , 2010, 45th International Universities Power Engineering Conference UPEC2010.

[3]  Albert Moser,et al.  Probabilistic ampacity forecasting for overhead lines using weather forecast ensembles , 2013 .

[4]  W. Z. Black,et al.  Real-Time Ampacity Model for Overhead Lines , 1983, IEEE Transactions on Power Apparatus and Systems.

[5]  Amber Nicole Brooks Modeling the Impact of Terrain on Wind Speed and Dry Particle Deposition Using WindNinja and ArcGIS Spatial Analyst , 2012 .

[6]  S.S. Venkata,et al.  Wind energy explained: Theory, Design, and application [Book Review] , 2003, IEEE Power and Energy Magazine.

[7]  Zhang Yan,et al.  A review on the forecasting of wind speed and generated power , 2009 .

[8]  J. Torres,et al.  Forecast of hourly average wind speed with ARMA models in Navarre (Spain) , 2005 .

[9]  Joe-Air Jiang,et al.  On Dispatching Line Ampacities of Power Grids Using Weather-Based Conductor Temperature Forecasts , 2018, IEEE Transactions on Smart Grid.

[10]  Eugene Fernandez,et al.  Analysis of wind power generation and prediction using ANN: A case study , 2008 .

[11]  C. Y. Chung,et al.  Time Series Modeling for Dynamic Thermal Rating of Overhead Lines , 2017, IEEE Transactions on Power Systems.

[12]  Carlos Cruzat,et al.  Power Network Reliability Evaluation Framework Considering OHL Electro-Thermal Design , 2016, IEEE Transactions on Power Systems.

[13]  Vijay Vittal,et al.  Increasing thermal rating by risk analysis , 1999 .

[14]  W. Rivera,et al.  Wind speed forecasting in the South Coast of Oaxaca, México , 2007 .

[15]  W. R. Hargraves,et al.  Nationwide assessment of potential output from wind-powered generators , 1976 .

[16]  W. F. Sandusky,et al.  Wind Energy Resource Atlas of the United States , 1987 .

[17]  Jin-O Kim,et al.  Prediction of transmission-line rating based on thermal overload probability using weather models , 2009 .

[18]  F. I. Oluwajobi Effect of sag on transmission line , 2012 .

[19]  D.A. Bechrakis,et al.  Correlation of wind speed between neighboring measuring stations , 2004, IEEE Transactions on Energy Conversion.

[20]  W. Black,et al.  Simplified Model for Steady State and Real-Time Ampacity of Overhead Conductors , 1985, IEEE Transactions on Power Apparatus and Systems.

[21]  D. A. Douglass,et al.  An experimental evaluation of a thermal line uprating by conductor temperature and weather monitoring , 1988 .

[22]  M. J. Stevens,et al.  The estimation of the parameters of the Weibull wind speed distribution for wind energy utilization purposes , 1979 .

[23]  L. Kamal,et al.  Time series models to simulate and forecast hourly averaged wind speed in Quetta, Pakistan , 1997 .

[24]  J. C. Lam,et al.  A study of Weibull parameters using long-term wind observations , 2000 .

[25]  Adrian Clark,et al.  Towards Smart Grid Dynamic Ratings , 2011, WCE 2011.

[26]  Paul E. Bieringer,et al.  A Space-Time Multiscale Analysis System: A Sequential Variational Analysis Approach , 2011 .

[27]  P. Dokopoulos,et al.  Short-term forecasting of wind speed and related electrical power , 1998 .

[28]  R. G. Olsen,et al.  A New Method for Real-Time Monitoring of High-Voltage Transmission Line Conductor Sag , 2002, IEEE Power Engineering Review.

[29]  H. Panofsky,et al.  Atmospheric Turbulence: Models and Methods for Engineering Applications , 1984 .

[30]  N. P. Schmidt Comparison between IEEE and CIGRE ampacity standards , 1999 .

[31]  Athanasios Sfetsos,et al.  A comparison of various forecasting techniques applied to mean hourly wind speed time series , 2000 .

[32]  A. Mueller Atmospheric Boundary Layer Flows Their Structure And Measurement , 2016 .

[33]  T. O. Halawani,et al.  Weibull parameters for wind speed distribution in Saudi Arabia , 1994 .

[34]  Jaromir Hosek,et al.  Effect of time resolution of meteorological inputs on dynamic thermal rating calculations , 2011 .

[35]  Gehao Sheng,et al.  Design and calculation method for dynamic increasing transmission line capacity , 2008 .

[36]  Erasmo Cadenas,et al.  Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model , 2010 .