The WOLF system: forecasting wet-snow loads on power lines in Italy

Transcription

1 Chongqing, China May 8-13, 2011 The WOLF system: forecasting wet-snow loads on power lines in Italy M. Lacavalla, P. Bonelli, G. Mariani, P. Marcacci, G. Stella Research on the Energy Systems - RSE S.p.A. Milan, Italy matteo.lacavalla@rse-web.it

2 RSE Research on the Energy Systems total publicly-controlled Company MISSION: Research into the field of Energy Systems financed by the Fund for the Italian Electrical Systems under the Contract Agreement between RSE and the Ministry of Economic Development 2

3 Italy Land of Sun Why the interest in this research? Is the Italian territory affected by wet-snow events? Is wet-snow accretion a real problem for the management and operation of Overhead Lines? Is it possible to forecast wet-snow loads for the entire Italian Power Grid? 3

4 Wet-snow accretion The overload of snow on power lines is still the principal cause of major power outages during the winter season in Italy Fall of lattice due to overload of snow (North Italy; 2001) 4

5 Wet-snow accretion on conductors Natural Hazard for OverHead Lines (OHL) Chongqing, May 8-13, 2011 in the past and today North Italy; 1985 South Italy;

6 Alps Plain Apennines Severe power lines failures due to wet-snow overload (since 2001) 6

7 WOLF Wet-snow Overload alert and Forecasting Purposes of the system: to identify critical wet snow events to compute the wet-snow loads on OHL to provide a daily forecast to the Italian Transmission System Operator (TSO) 7

8 Numerical Weather Prediction Model WOLF procedure Chongqing, May 8-13, 2011 Transmission Lines information, DEM Air temperature, precipitation, wind Italy, +72h Wet-snow accretion model Anti-icing model 8 Snow-load on OHL AI-current

9 NWP model Chongqing, May 8-13, 2011 Driver model: Local Area Model Italy COSMO-LAMI 30 hpa 35 vertical layers h. resolution: ( 7 Km) 9

10 T, P, LWC,,W, Rh 3D model scheme Chongqing, May 8-13, 2011 Type of precipitation Altitude (m),, C/100m,,,,,,,,,, OHL altitude profile distance (km) 10

11 HV Trasmission Lines Network Chongqing, May 8-13, 2011 Path of power lines Type of conductors Diameter of conductors Electrical resistances Altitude profiles of OHL 380 kv 220 kv 150 kv 132 kv 11

12 The accretion phases on conductors Chongqing, May 8-13, 2011 Air temperature LWC of snowflakes conductor sleeve Wet-snow 0 C<T<2 C Rain/Warm,,,, T>2 C,, wet LWC>40% Dry-snow T<0 C LWC<15% wet growth Fusion/shedding wet growth dry growth forecast Time UTC Time UTC 12

13 Cylindrical Growth of Wet Snow Sleeves Eq.1: M = α 2P R t n Simplified version by Sundin & Makkonen Amount of wet-snow load (kg/m) r R 0 V Eq.2 P n = P 1 + V sin V s θ 2 snowfall intensity V s P n conductor Eq.3 M ( t) R ( t) = + R ρπ 2 0 Radius sleeve P V Eq.4 ρ = a + bv α Eq.5 1<V<10 m/s = 1 / V if wet-snow conditions 13 Sleeve density Collection coefficient

14 WOLF LOAD: total events N events predicted > 1 kg/m WOLF LOAD: Major events N events predicted > 5 kg/m Power failures From October 2010 to April 2011 From October 2010 to April 2011 Elaborated by RSE 14 Elaborated by RSE

15 Wet-snow occurrence at different orographic altitudes Cumulative frequency >2200 amsl (5%) amsl (25%) % amsl (45%) amsl (25%) > altitudes classes 15

16 Wet-snow case-study COSMO-LAMI model is used to predict the strong event of December 2008 (North of Italy): Location of wet-snowfall Timing of the event Amount of total precipitation Amount of snow water equivalent The COSMO-LAMI output feeds the wet snow accretion model to obtain snow loads from the: Flux of snowfall on conductor Temperature Wind speed Sleeve density Wetness of snowflakes (dry or wet) 16

17 Heavy wet-snowfall: Dec amsl Area of power lines failures Conductors touched the ground due to snow overload 17

18 160 mm/48 hrs!! TOTAL PRECIPITATION From: to Case study: Dec 2008 Map of accumulated precipitation predicted by the model The rain gauges recorded values very close to those expected Elaborated by 18RSE

19 % of snow vs. total precipitation From to Case study: Dec 2008 Precipitation types: red: wet-snow orange: dry snow yellow: snow mixed rain Elaborated by RSE 19

20 WEB-GIS 20

21 Timescale of developing heavy snowfall Case study: Dec2008 Temperature at different OHL altitudes ( C) Wind intensity (m/s) Precipitation histogram (mm/3h) Alert Loads on different conductors (kg/m) 21

22 Chongqing, May 8-13, 2011 Automatic wet-snow accretion station Automatic station installation ACSR 31.5mm 22

23 Automatic station Chongqing, May 8-13,

24 Wet-snow load (gr) and thickness of sleeves (mm) 2 8 /2 / /2 / /2 / /2 / /2 / /2 / /2 / /2 / /2 / /2 / /2 / /2 / /2 / /2 / /2 / /2 / Measures from the automatic station thck thck 2 thck 3 load 1 load g r m m shedding

25 Conclusion and Future works Chongqing, May 8-13, 2011 This work shows the system WOLF able to forecast wetsnow conditions in Italy and to compute wet-snow loads on the entire OHL power grid The system provides alarms and displays meteorological and power lines information on an WEB-GIS map In the future, it is planned to improve the WOLF algorithm, introducing more accurate accretion and shedding parameters, through data collected by the wetsnow accretion station 25

26 Snow is not only a work, also my passion! Thanks for your attention 26

27 Joule-effect heating AI current [A] Mathematical model proposed by Shurig and Frick: Eq.1: Eq.2: = P = P + j 5600 J 0 I c P D R P d t Diameter Resistance ACSR conductor Total Joule-effect dissipation Power losses by convection Radiative losses Temp, Wind, Press 27

28 water equivalent of snowfall From to Case study: Dec mm eq. /48 hrs!! Location of major power lines failures during the heavy wet-snow events (Piedmont region) Elaborated by RSE 28

29 OHL crossing a mountainous region Chongqing, May 8-13, 2011 High resolution orography (DEM: 250m spacing) 29