CitationsSee all >3 References
14.04 · Universidad de Cantabria13.37 · Universidad de Cantabria+ 3Show
more authorsAbstractThis paper presents the steady and dynamic thermal balances of an overhead power line proposed by CIGRE (Technical Brochure 601, 2014) and IEEE (Std.738, 2012) standards. The estimated temperatures calculated by the standards are compared with the averaged conductor temperature obtained every 8 min during a year. The conductor is a LA 280 Hawk type, used in a 132-kV overhead line. The steady and dynamic state comparison shows that the number of cases with deviations to conductor temperatures higher than 5°C decreases from around 20% to 15% when the dynamic analysis is used. As some of the most critical variables are magnitude and direction of the wind speed, ambient temperature and solar radiation, their influence on the conductor temperature is studied. Both standards give similar results with slight differences due to the different way to calculate the solar radiation and convection. Considering the wind, both standards provide better results for the estimated conductor temperature as the wind speed increases and the angle with the line is closer to 90°. In addition, if the theoretical radiation is replaced by that measured with the pyranometer, the number of samples with deviations higher than 5 °C is reduced from around 15% to 5%.Discover the world's research13+ million members100+ million publications700k+ research projects
ArticleComparison between IEEE and CIGRE ThermalBehaviour Standards and Measured Temperature on a132-kV Overhead Power LineAlberto Arroyo 1,*, Pablo Castro 1, Raquel Martinez 1, Mario Manana 1, Alfredo Madrazo 1,Ramón Lecuna 1and Antonio Gonzalez 2Received: 6 July 2015; Accepted: 19 November 2015; Published: 2 December 2015Academic Editor: Ying-Yi Hong1Electrical and Energy department, University of Cantabria, Av. Los Castros S/N, Santander 39005, Spablo.castro@unican.es (P.C.);
(R.M.); mario.manana@unican.es (M.M.);alfredo.madrazo@unican.es (A.M.); ramon.lecuna@unican.es (R.L.)2Viesgo, Santander 39011, S antonio.*Correspondence: arroyoa@unican. Tel.: +34-942-201-371; Fax: +34-942-201-385Abstract: This paper presents the steady and dynamic thermal balances of an overhead powerline proposed by CIGRE (Technical Brochure 601, 2014) and IEEE (Std.738, 2012) standards.The estimated temperatures calculated by the standards are compared with the averaged conductortemperature obtained every 8 min during a year. The conductor is a LA 280 Hawk type, usedin a 132-kV overhead line. The steady and dynamic state comparison shows that the number ofcases with deviations to conductor temperatures higher than 5 oC decreases from around 20% to15% when the dynamic analysis is used. As some of the most critical variables are magnitudeand direction of the wind speed, ambient temperature and solar radiation, their influence onthe conductor temperature is studied. Both standards give similar results with slight differencesdue to the different way to calculate the solar radiation and convection. Considering the wind,both standards provide better results for the estimated conductor temperature as the wind speedincreases and the angle with the line is closer to 90o. In addition, if the theoretical radiation isreplaced by that measured with the pyranometer, the number of samples with deviations higherthan 5 oC is reduced from around 15% to 5%.Keywords:
overh real-time monitoring1. IntroductionElectricity distribution networks are increasingly affected by new operation scenarios thatmake integration more complex. Some of these factors are electricity market liberalization andthe integration of a large number of renewable installations [1]. As a result, line congestions areincreasing, resulting in problems for both the distribution company, which is not capable of absorbingall of the energy generated, resulting in a decrease in efficiency, and the generation company becauseit will be requested to limit production and, in some cases, to stop it. These scenarios produce greatinefficiencies in the system from both energy and environmental aspects because the generation ofclean energy is limited to avoid problems in the distribution lines.The basic solution is to increase the distribution and transmission line capacity, which can beperformed in several ways. The most obvious way is to build new lines to reinforce the network.However, this solution is constrained by the high costs and legal difficulties of building new lines [2].Because of the unviability of the first proposal, electrical line operators are focusing on solutions basedon the modification of existing lines and an increase in their capacity.Energies 2015,8, 1; doi:10.3390/en /journal/energies
Energies 2015,8, 1Increasing the capacity of overhead power lines is currently one of the important areas ofresearch due to a good balance between the results obtained and the costs involved. There aredifferent techniques to increase this capacity: determine meteorological conditions by means ofdeterministic [3] or probabilistic [4] methods, up to the newest innovations in smart grids and lineparameters real-time monitoring: temperature, sag, tilt, power, current and weather conditions [5–7].In the case of wind farm integration into the grid, monitoring weather conditions in real timecan be very useful to obtain a win-win situation [8,9]. Strong winds increase wind farm production.At the same time, they cool down the conductors of the distribution lines near the farm. This coolingeffect allows the grid to be overloaded when it is most needed.2. Ampacity, Conductor Temperature and Dynamic Calibration of Overhead LinesThe notion of ampacity appeared as a result of research on increasing power line capacity, andit is defined as the maximum amount of electrical current a conductor can continuously carry beforesustaining deterioration. Ampacity is limited by several factors: the conductor structure and design,the surrounding environmental conditions and the operating conditions of the line.Ampacity can be used as a static or dynamic value [10–12]. Static ampacity always assumesthe most constrained conditions for the conductor and its environment. This condition gives veryconservative values and low efficiency grids.On the other hand, dynamic ampacity considers the variability of the grid and its surroundings(ambient temperature, solar radiation, wind, etc.). Thus, if the different conductor cooling and heatingprocesses are measured in real time, the maximum instantaneous practical current can be measured(dynamic ampacity) without reaching the maximum thermal rating [13,14]. This is why dynamicampacity is considered to be a more efficient control parameter of the power grid than static ampacity.Working parameters should be measured or estimated by different methods (deterministic orprobabilistic methods) to calculate the ampacity. International Council on Large Electric Systems(in French: Conseil International des Grands Reseaux Electriques, CIGRE) [15] and Institute ofElectrical and Electronics Engineers (IEEE) [16] have standards in which the algorithms to estimatethe ampacity and the temperature of the conductor are described.3. Thermal Balance of Overhead Lines Calculation MethodsBoth algorithms (CIGRE and IEEE) are based on the thermal balance between the gained andlost heat in the conductor due to the load and environmental conditions [17]. They suggest two waysto estimate the conductor temperature of an overhead power line. The first way uses steady stateconditions to calculate the conductor temperature while the second way estimates the temperature ina dynamic balance taking into account the conductor thermal inertia.The basic thermal balance used in steady state conditions is:qc+qr=qs+qj+qm(1)where qcis the cooling due to convection, qris the cooling due to the radiation to the surroundings,qsis the heating due to the solar radiation, qjis the heating due to the Joule effect and qmis the heatingdue to the magnetic effect.If the thermal inertia of the conductor is considered, the following dynamic thermal balance isused instead:mc dTcdt =qs+qj+qm-qc-qr(2)where mis the mass per unit length, cthe specific heat capacity and Tcthe theoreticalconductor temperature.The main similarities and differences between both algorithms are [18]:13661
Energies 2015,8, 1o Both methods consider the weather conditions, including wind speed and direction,ambient temperature and solar radiation, but they use different approaches to calculate thethermal balance.o Solar heating is calculated by considering the sun’s position depending on the hour andday of the year. CIGRE uses a more complex algorithm including the direct, diffuse andreflected radiation.o Convective cooling is approached by CIGRE using Morgan correlations based on Nusseltnumber and by IEEE using McAdams correlations based on Reynolds number.Focusing on CIGRE and IEEE standards and in the guide for selection of weather parameters forbare overhead conductor ratings of CIGRE [19], the variables that should be measured or estimatedare the ambient temperature (Ta), solar radiation (Qs), wind speed (uw), wind direction (φw) andthe current of the conductor (IT MSc). This paper shows the measured conductor surface temperature(TTMSc) and compares it with the temperature estimated by the standards (TCIGREc&TIEEEc).On the one hand, the steady state balance is used and the conductor temperature Tc,ss is obtainedfrom the solution of Equation (1). On the other hand, the dynamic state balance is calculated byEquation (2), tracking the conductor temperature Tc,ds using a time step dt = 1 s (Figure 1).DATAMONITORINGDYNAMIC-STATEBALANCE (TRACKING):mici·dTi/dti=(qSi+qji+qmi-qci-qri)STEADY-STATEBALANCE:qsi+qji+qmi=qci+qriCALCULATION OFCONDUCTOR TEMPERATURECONDUCTOR TEMPERATUREDEVIATION CONTROLTCTMSTc,ss Tc,dsFigure 1. Conductor heat balance flow chart.The values of the parameters to calculate the temperature are measured by a meteorologicalstation placed in the tower (ambient temperature, humidity, wind speed and direction and solarradiation). Measured solar radiation is used to compare it with that estimated by the standards andto show the error made by the standards due to the estimated solar radiation use. The conductortemperature calculated for each set of data is then compared with the value measured by atemperature measurement sensor (TMS) placed in the overhead line and close to the meteorologicalstation. This TMS is also used to measure the conductor current needed to calculate qj. All data frommeteorological stations and TMS are obtained every second and used to calculate their average valuesin periods of 8 min.The evaluation of the optimal place for the location of the weather station has been carriedout using both historical data and a meso-scale (convection-permitting) model called HIRLAM(High-Resolution Limited Area Model) that is widely used in Europe for numerical weatherprediction. The HIRLAM model had a resolution of 0.05, which means a data grid of 4 km.The resolution of the micro-climatic study was reduced to 500 m by using bi-cubic interpolation.The model also included the surface roughness of the terrain provided by the database CORINELand Cover. The results provided by the micro-climatic study defined critical points in terms of theirability to cool the cable.13662
Energies 2015,8, 14. Results for a Specific Overhead LineTo study the influence of each variable on the thermal balance of the algorithms, real time data ofthe ambient and conductor temperature, humidity, wind speed and direction and sun radiation wereaveraged every 8 min during an entire year—from September 2013 to September 2014— in a 132-kVoverhead line with a LA 280 Hawk type conductor [20] located in northern Spain (Figure 2a).Table 1describes the variables and the equipment used to measure them. The meteorologicalstation is placed in the electricity tower and the TMS attached to the conductor (Figure 2b).With the set of values generated, the steady and dynamic thermal states and the associatedconductor temperatures according to CIGRE (TCI GREc,ss and TCIGR Ec,ds ) and IEEE (TI EEEc,ss and TIEEEc,ds ) arecalculated and compared with the conductor temperature measured by the TMS (TTMSc). A largeamount of data was processed, and a statistical approach is used to study the individual influence ofthe variables.SBsSBeWS018.1?km11.1?km7.2°W7.1°W7.0°W 6.9°W6.8°W43.1°N43.2°N43.3°NSBs:?Starting?substationSBe:?Ending?substationWS01:?Weather?station(a)WeatherstationTMSEnergy supplyData?logger(b)Figure 2. Description of the line and the system components. (a) 132 kV overhead transmission linelocated in northern S (b) System components of the conductor temperature and meteorologicaldata monitoring at the tower.13663
Energies 2015,8, 1Table 1. Technical data of the measuring equipment.Measurement Measuring EquipmentConductor Temperature (TTMSc) TMS Accuracy: 0–120 °CConductor current (IT MSc)TMS Accuracy: 100–1500 ASolar Radiation (Qs)Pyranometer. Accuracy: 0–1100 W/m2±0.5%Wind Speed (uw) Vane Anemometer. Accuracy: 0–60 m/s±0.3 m/sWind Angle Relative Direction (φw) Vane Anemometer. Accuracy: 0–360°±2°Ambient Temperature (Ta) Thermometer. Accuracy: (–20)–80 °C±0.3 °CHumidity Hygrometer. Accuracy: 0%–100% ±3%Figure 3a,b and Table 2provide information regarding the frequency and cumulative frequencyof the deviation between the estimated and measured temperatures. Both standards are in goodagreement for steady and dynamic balances and underestimate the measured temperature TTMScin a15% of cases.The steady state assumption does not take into account the thermal inertia of the conductormaterials and, thus, it can not model the transition between the set of values. This fact generatespeaks in the estimated conductor temperature, which do not, in reality, exist. These mistakes arecorrected if the dynamic balance is used, Equation (2). For instance, Table 2indicates that the numberof samples with deviations to conductor temperature lower than 5 oC increases from around 80% to85% when the dynamic analysis is used.Table 2. Cumulative frequency of differences between temperatures obtained using CIGRE(TCI GREc,ss and TCIGREc,ds ) and IEEE (TI EEEc,ss and TIE EEc,ds ) standards and TT MScfor an entire year.Deviation CIGRE S.S. IEEE S.S. CIGRE D.S. IEEE D.S.Temperature Cum.Freq.1Cum.Freq.2Cum.Freq.3Cum.Freq.4(oC) (%) (%) (%) (%)–5 0.00 0.00 0.00 0.00–4 0.01 0.01 0.01 0.00–3 0.16 0.12 0.14 0.04–2 0.94 0.87 1.04 0.75–1 3.93 3.81 4.08 3.680 14.77 13.54 15.58 13.701 35.34 33.41 39.74 37.372 51.05 49.57 56.97 55.673 63.37 62.65 69.81 69.204 72.89 72.70 78.70 79.145 79.62 80.12 84.87 86.026 84.51 85.30 89.37 90.597 87.76 88.91 92.35 93.568 90.22 91.51 94.37 95.769 92.38 93.30 95.95 97.24.... ..... ..... ..... .....25 99.93 99.96 100.00 100.00(1) Steady state cumulative frequency with CIGRE.(2) Steady state cumulative frequency with IEEE.(3) Dynamic state cumulative frequency with CIGRE.(4) Dynamic state cumulative frequency with IEEE.13664
Energies 2015,8, 1Deviation?to?conductor?temperature?(°C)-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 1011 1213 14 1516 1718 19 20Frequency?(%)0510152025Tc,ssCIGRE-TcTMSTc,ssIEEE-TcTMSTc,dsCIGRE-TcTMSTc,dsIEEE-TcTMS(a)Deviation to conductor temperature?(°C)-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 1011 1213 141516 1718 1920Cumulative frequency (%)0102030405060708090100Tc,ssCIGRE-TcTMSTc,ssIEEE-TcTMSTc,dsCIGRE-TcTMSTc,dsIEEE-TcTMS(b)Figure 3. Frequency and cumulative frequency of differences between temperatures obtained usingCIGRE (TCIGREc,ss and TCIGREc,ds ) and IEEE (TI EEEc,ss and TIE EEc,ds ) standards and the measured conductortemperature (TTM Sc) for an entire year. (a) F (b) Cumulative frequency.As an example, a representative day (30 August 2014) is shown in Figure 4. Figure 4a showsthe deviation between the conductor temperature estimated by CIGRE and IEEE steady state balance(TCI GREc,ss &TIEEEc,ss ) and the measured temperature (TTMSc). Figure 4b shows the same deviation for thedynamic state balance. Finally, the measured weather parameters are also represented in Figure 4c.Comparing Figure 4a,b ,some differences between steady and dynamic balance can be observed.First of all, the dynamic balance models the transient states obtaining smoother curves with lessdeviations to the conductor temperature giving a better fit than the steady state. Secondly, theconsideration of the thermal inertia of the conductor materials makes the slope of the dynamic curvescloser to the slope of the TT MSccurve.13665
Energies 2015,8, 100:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 00:00Temperature?[?C]101520253035TTc,ssCIGRETc,ssCIGRE using?measured?radiationTc,ssIEEETc,ssIEEE using?measured?radiationTMSc(a)00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 00:00Temperature?[?C]101520253035TTMSTc,dsCIGRETc,dsCIGRE using?measured?radiationTc,dsIEEETc,dsIEEE using?measured?radiationc(b)00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 00:00φw-?IcTMS -?Qs010020030040050060070080090010001100Wind?Direction?[?]Conductor?current?[A]Measured?Solar?Radiation?[W/m2]CIGRE?Estimated?Solar?Radiation?[W/m2]IEEE?Estimated?Solar?Radiation?[W/m2]00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 00:00Ta-?uw02.557.51012.51517.52022.52527.5Ambient Temperature?[?C]Wind?speed?[m/s](c)Figure 4. Comparison of conductor temperature obtained using IEEE (TIEEEc,ss and TIE EEc,ds ) and CIGRE(TCI GREc,ss and TCIGREc,ds ) standards with the measured conductor temperature (TTMSc) for a single day.(a) Steady state balance (30 August 2014); (b) Dynamic state balance (30 August 2014); (c) Weatherconditions (30 August 2014).From these figures, one can conclude that CIGRE and IEEE estimated temperatures differ morewhen the influence of radiation is appreciable (from 8:00 to 21:00). These differences betweenstandards are due to the distinct ways to calculate the solar heat gain. CIGRE estimates thedirect, diffuse and reflected radiation while IEEE only includes the direct radiation. This is thereason why the CIGRE estimated radiation is higher than the IEEE estimated one, as shown inFigure 4c. This effect makes the CIGRE estimated temperature to be higher than the IEEE estimatedone. In addition, a systematic overstimation of the conductor temperature appears in both modelswhen there is no solar radiation (i.e., at night). This deviation, around 2 oC , might be due to theradiative cooling calculation. The equation used to evaluate this effect considers the ground and skytemperature to be equal to the ambient temperature [15,16] but during clear nights this assumptionobtains worse estimated conductor temperatures because of radiation to deep space [19].Figure 4a,b also show the error made if the estimated radiation is used instead of the onemeasured by the pyranometer. Temperatures obtained using the measured radiation fit better withthe conductor temperature TTMSc. Additionally, the frequency and cumulative frequency of thedeviation using estimated and measured radiation are plotted in Figure 5. The correction made using13666
Energies 2015,8, 1the measured radiation is clearly shown. The number of samples with deviations higher than 5 oCdecreases from 15% to 5%. However, the number of samples which underestimate the conductortemperature increases 10% (from 15% to 25%). This makes the use of the measured radiationrecommendable, but the increase of the underestimated values should also be taken into account.Deviation to conductor temperature?(°C)-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 1011 1213 141516 1718 1920Frequency (%)051015202530Tc,dsCIGRE-TcTMSTc,dsIEEE-TcTMSTc,dsCIGRE-TcTMS (using measured rad.)Tc,dsIEEE-TcTMS (using measured rad.)(a)Deviation to conductor temperature?(°C)-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10111213 1415 1617 18 1920Cumulative frequency (%)0102030405060708090100Tc,dsCIGRE-TcTMSTc,dsIEEE-TcTMSTc,dsCIGRE-TcTMS (using measured rad.)Tc,dsIEEE-TcTMS (using measured rad.)(b)Figure 5. Frequency and cumulative frequency of differences between temperatures obtained usingCIGRE (TCIGREc,ds ) and IEEE (TI EEEc,ds ) standards and TT MScfor an entire year, with estimated andmeasured radiation. (a) F (b) Cumulative frequency.As the dynamic thermal balance provides a better estimated temperature, the dynamic methodwill be used to study the influence of the wind on the estimated temperature. This influence isreported in previous studies [21] and the wind seems to be the most critical variable for the differencebetween the estimated and measured temperatures. In Figure 6a, it can be seen that as the wind speeddecreases, this difference increases. If the influence of the other variables are minimized by selectingonly the cases without solar radiation (Qs= 0 W/m2), low radiation losses qr(TT MSc-Ta&2oC) andlow current (IT MSc&200 A, the LA-280 maximum current to 80 oC is 600 A), the wind speed influenceis clearer, as seen in Figure 6b.13667
Energies 2015,8, 1As reported in the standards, the overestimation of the conductor temperature at low speeds isdue to the difficulty of having accurate equations to model the convective effect. This fact can makethe estimated temperature even 20 oC higher than the measured one.Wind speed uw(m/s)0 2.5 5 7.5 10 12.5 15 17.5Deviation to conductor temperature?(°C)-10-5051015202530Tc,dsCIGRE-TcTMSTc,dsIEEE-TcTMS(a)Wind speed uw(m/s)0 2.5 5 7.5 10 12.5 15 17.5Deviation to conductor temperature?(°C)-10-5051015202530Tc,dsCIGRE-TcTMSTc,dsIEEE-TcTMS(b)Figure 6. Deviation of conductor temperature obtained using CIGRE (TC IG REc,ds ) and IEEE (TI EEEc,ds )standards to the measured conductor temperature (TTMSc)vs. wind speed for an entire year.(a) F (b)Qs= 0 W/m2,TTM Sc-Ta&2°C and ITMSc&200 A.Going deeper into the influence of the wind is to consider how the deviation to conductortemperature is modified by the wind direction. If the temperature deviation is plotted against theangle between the wind and axis of the conductor φw(Figure 7), it can be seen that the lower thewind angle, the higher the deviation is, i.e., in cases with wind blowing parallel to the conductor,standards generally overestimate the conductor temperature.Finally, Table 3shows the cumulative frequency of the deviation to conductor temperature lowerthan 5 oC obtained by the different methods. On the one hand, including the thermal inertia of theconductor materials improves the accuracy of the estimated temperature around 5%. On the otherhand, replacing the theoretical radiation by the measured one continues improving the accuracy.In this case, 94.7% of the samples have a deviation lower than 5 oC.13668
Energies 2015,8, 1Wind angle φw(°)0 10 20 30 40 50 60 70 80 90Deviation to conductor temperature?(°C)-10-505101520253035Tc,dsCIGRE-TcTMSTc,dsIEEE-TcTMSFigure 7. Deviation of conductor temperature obtained using IEEE (TI EEEc,ds ) and CIGRE (TC IGREc,ds )standards to the measured conductor temperature (TTMSc)vs. the angle between the wind and theaxis of the conductor (φw).Table 3. Cumulative frequencies of deviation to conductor temperature lower than 5 oC for thestudied cases.Cumulative CIGRE IEEE CIGRE IEEE CIGRE IEEEFrequency S.S. S.S. D.S. D.S. D.S. (Using D.S. (UsingMeasured Rad.)Measured Rad.)(%) 79.6 80.1 84.9 86.0 94.7 94.75. ConclusionsThis paper presents the steady and dynamic thermal balances of an overhead power lineproposed by CIGRE [15] and IEEE [16] standards. The estimated temperatures calculated by thestandards are compared with the averaged conductor temperature obtained every 8 min during anentire year. The conductor is a LA 280 Hawk type, used in a 132-kV overhead line and located innorthern Spain.A good monitoring system of the weather conditions surrounding power lines provides veryimportant information to control the conductor temperature. The evaluation of the optimal place forthe location of the weather station has been carried out using both historical data and a meso-scalemodel. The results provided by the micro-climatic study defined critical points in terms of their abilityto cool the cable.Regarding the type of heat balance, the dynamic method gives a better approach to the conductortemperature. The steady and dynamic state comparison shows that the number of cases withdeviations to conductor temperature higher than 5 oC decreases from around 20% to 15% when thedynamic analysis is used (Table 2).As some of the most critical variables for the IEEE and CIGRE thermal balances are speed anddirection of the wind, ambient temperature and solar radiation, their influence on the conductortemperature is studied. Both standards give very similar results with slight differences due to thedifferent way to calculate the solar radiation gain and the convection losses.CIGRE estimates the direct, diffuse and reflected radiation while IEEE only includes the directradiation. Focusing on a single day (Figure 4), the estimated temperatures present more differenceswhen the influence of the radiation is appreciable, making the CIGRE estimated temperature to13669
Energies 2015,8, 1be higher than the IEEE estimated one. Worth noting also is the significant difference betweenthe estimated and the measured temperature if there are large deviations between the estimatedand the measured solar radiation (Figure 4c). If the measured radiation on site is used instead ofthe theoretical one suggested by the standards, the deviation to conductor temperature can alsobe decreased (Figure 5b). For example, using the estimated radiation, 15% of the samples presentdeviations higher than 5 oC, while using the measured radiation this percentage decreases to 5%.Considering the wind, both standards provide better results for the estimated conductortemperature as the wind speed increases (Figure 6a,b) and the angle with the line is closer to 90o(Figure 7), giving the maximum deviation to the measured temperature for low wind speeds andquasi-parallel flows. As reported in the standards, the overestimation of the conductor temperatureat low speeds is due to the difficulty of having accurate equations to model the convective effect. Thisfact can make the estimated temperature to be even 20 oC higher than the measured one.In conclusion, as the algorithms and the input data are improved, from steady state analysiswith estimated radiation to dynamic balance with measured radiation, the accuracy of the estimatedtemperature can increase up to 15% (Table 3).Acknowledgments: This work was supported by the Spanish Government under the R+D initiative INNPACTOwith reference IPT-0000 and Spanish R+D initiative with reference ENE-R. The authorswould also like to acknowledge Viesgo for its support.Author Contributions: Alberto Arroyo, Pablo Castro, Raquel Martinez, Mario Manana, Alfredo Madrazo,Ramón Lecuna and Antonio Gonzalez contributed to this paper. Alberto Arroyo and Pablo Bernardo: definitionof the methodology, Raquel Martinez, Mario Manana and Antonio Gonzalez: test execution. Alfredo Madrazoand Ramon Lecuna: review.Conflicts of Interest: The authors declare no conflict of interest.Abbreviationsφw: angle between wind and axis of conductor (°).c: specific heat capacity (J/kg °C).ITMSc: conductor measured current (A).m: conductor mass per unit length (kg/m).qc: convective cooling (W/m).qr: radiative cooling (W/m).qs: solar radiative heating (W/m).qj: joule heating (W/m).qm: magnetic heating (W/m).Qs: solar radiation (W/m2).Ta: ambient air temperature (°C).Tc: theoretical conductor temperature (°C).TCI GREc,ss : steady state conductor temperature estimated by CIGRE (°C).TCI GREc,ds : dynamic state conductor temperature estimated by CIGRE (°C).TIEEEc,ss : steady state conductor temperature estimated by IEEE (°C).TIEEEc,ds : dynamic state conductor temperature estimated by IEEE (°C).TTMSc: measured conductor temperature (°C).uw: wind speed (m/s).References1. Nykamp, S.; Molderink, A.; Hurink, J.; Smit, J. Statistics for PV, wind and biomass generators and theirimpact on distribution grid planning. Energy 2012,45, 924–932.2. Jorge, R.S.; Hertwich, E.G. Environmental evaluation of power transmission in Norway. Appl. Energy 2013101, 513–520.13670
Energies 2015,8, 13. Hall, J.F.; Deb, A.K. Prediction of overhead transmission line ampacity by stochastic and deterministicmodels. IEEE Trans. Power Deliv. 1988,3, 789–800.4. Reding, J.L. A method for determining probability based allowable current ratings for BPA’s transmissionlines. IEEE Trans. Power Deliv. 1994,9, 153–161.5. Pytlak, P.; Musilek, P. Modelling precipitation cooling of overhead conductors. Electr. Power Syst. Res. 2011,81, .6. Cho, J.; Kim, J. H.; Lee, H.J.; Kim, J.Y.; Song, I.K.; Choi, J.H. Development and improvement of an intelligentcable monitoring system for underground distribution networks using distributed temperature sensing.Energies 2014,7, .7. Holyk, C.; Liess, H.D.; Grondel, S.; Kanbach, H.; Loos, F. Simulation and measurement of the steady-statetemperature in multi-core cables. Electr. Power Syst. Res. 2014,116, 54–66.8. Hosek, J. Dynamic thermal rating of power transmission lines and renewable resources. In Proceedings ofthe ES1002 Workshop, Paris, France, 22–23 March 2011; pp. 1–3.9. Heckenbergerova, J.; Hosek, J. Dynamic thermal rating of power transmission lines related to wind energyintegration. In Proceedings of the 11th International Conference on Environment and Electrical Engineering(EEEIC), Venice, Italy, 18–25 May 2012; pp. 798–801.10. Popelka, A.; Jurik, D.; Marvan, P. Actual line ampacity rating using PMU. In Proceedings of the 21stInternational Conference on Proceedings of the Electricity Distribution (CIRED), Frankfurt, Germany,6–9 June 2011.11. Puffer, R.; Schmale, M.; Rusek, B; Neumann, S.; Scheufen, M. Area-wide dynamic line ratings basedon weather measurements. In Proceedings of the Conference on Cigre Session 44, Paris, France,26–31 August 2012.12. Abdelkader, S.; Morrow, D.J.; Fu, J; Abbott, S. Field measurement based PLS model for dynamic ratingof overhead lines in wind intensive areas. In Proceedings of the International Conference on RenewableEnergies and Power Quality, Bilbao, Spain, 20–22 March 2013.13. International Council on Large Electric Systems, CIGRE. Guide for Application of Direct Real-Time MonitoringS Technical Brochure 498; CIGRE: Paris, France, June 2012.14. Michiorri, A.; Taylor, P.C.; Jupe, P.C.; Berry, C.J. Investigation into the influence of environmental conditionson power system ratings. Proc. Inst. Mech. Eng. A J. Power Energy 2009,223, 743–757.15. International Council on Large Electric Systems, CIGRE. Guide for Thermal Rating Calculation of OverheadLines; Technical Brochure 601; CIGRE: Paris, France, December 2014.16. IEEE Std 738-2012: IEEE Standard for Calculation the Current-Temperature Relationship of Bare OverheadConductors; IEEE Standard Association: Washington, U.S.A. 23 December 2013.17. Silva, A.A.P.; Bezerra, J.M.B. Applicability and limitations of ampacity models for HTLS conductors.Electr. Power Syst. Res. 2012,93, 61–66.18. Schmidt, N.P. Comparison between IEEE and CIGRE ampacity standards. IEEE Trans. Power Deliv. 1999,14, .19. International Council on Large Electric Systems, CIGRE. Guide for Selection of Weather Parameters for BareOverhead Conductor ratings; Technical Brochure 299; CIGRE: Paris, France, 2006.20. EN . Conductors for overhead lines. Round wire concentric lay stranded conductors. (ISBN 9780 580 84034 0)21. Abbott, S.; Abdelkader, S.; Bryans, L.; Flynn, D. Experimental validation and comparison of IEEE andCIGRE dynamic line models. In Proceedings of the 45th International Universities Power EngineeringConference (UPEC), Cardiff, UK, 31 August–3 September 2010; pp. 1–5.(C) 2015 licensee MDPI, Basel, Switzerland. This article is an openaccess article distributed under the terms and conditions of the Creative Commons byAttribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).13671
CitationsCitations5ReferencesReferences3The dynamic rating is very sensitive to the wind speed as seen in the equations provided by Cigre[1]and IEEE algorithms[2,3]. This sensitivity makes the choice of the anemometer an important matter not to be taken lightly[4,5]. Article · May 2016 · Applied Thermal Engineering+1 more author...ABSTRACT: Dynamic thermal rating (DTR) is more accurate and can better utilize the transmission/distribution capacity of an electric power system compared to static thermal rating. It is beneficial to integrate DTR into power system planning problems where modeling the DTR is vital. This paper presents a new modeling method for DTR that consists of three sequential steps: A multivariate polynomial regression between the DTR and its four affecting factors, an hourly normalization, and an autoregressive integrated moving average (ARIMA). Three types of polynomial regressions were developed based on the analysis of the heat balance model for calculation of the DTR. For the purpose of comparison, several other modeling methods for the DTR were designed based on a widely used wind speed modeling method. The performance of the different modeling methods was verified using case studies from Austin, USA and Wawa, Canada. The results show that the model of the DTR obtained using the proposed method is superior in terms of both probability distribution and fitting accuracy.Article · Jan 2016 Article · May 2016 · Applied Thermal Engineering+1 more author...ABSTRACT: Electricity generation is changing as new, renewable and smaller generation facilities are created, and classic topologies have to accommodate this distributed generation. These changes lead to the creation of smart grids in which advanced generation, information and communication technologies are needed. Information metering is important, and one of the most important grid parameters to be measured and controlled is the temperature of overhead conductors due to their relation to the maximum allowable sag of the line. The temperature and current of an overhead conductor and the weather conditions surrounding the cable are measured every 8 min for more than a year. With these data, the accuracies of the different algorithms presented in the standards (CIGRE TB601 and IEEE 738) are studied by implementing them in MATLAB(R). The use of precise measurements of solar radiation and low wind speeds with ultrasonic anemometers, improves the accuracy of the estimated temperature compared with the real measured conductor temperature. Additionally, using dynamic algorithms instead of assuming a steady state analysis increases the accuracy. However, an equilibrium between the accuracy and mathematical complexity should be obtained depending on the specific needs.Article · Sep 2016 +1 more author...ABSTRACT: Dynamic Line Rating (DLR) enables rating of power line conductors using real-time weather conditions. Conductors are typically operated based on a conservative static rating that assumes worst case weather conditions to avoid line sagging to unsafe levels. Static ratings can cause unnecessary congestion on transmission lines. To address this potential issue, a simulation-based dynamic line rating approach is applied to an area with moderately complex terrain. A micro-scale wind solver — accelerated on multiple graphics processing units (GPUs) — is deployed to compute wind speed and direction in the vicinity of powerlines. The wind solver adopts the large-eddy simulation technique and the immersed boundary method with fine spatial resolutions to improve the accuracy of wind field predictions. Statistical analysis of simulated winds compare favorably against wind data collected at multiple weather stations across the testbed area. The simulation data is then used to compute excess transmission capacity that may not be utilized because of a static rating practice. Our results show that the present multi-GPU accelerated simulation-based approach — supported with transient calculation of conductor temperature with high-order schemes — could be used as a non-intrusive smart-grid technology to increase transmission capacity on existing lines.Article · Feb 2017 · Applied Thermal EngineeringChapterJuly 2016+2 more authors…ArticleSeptember 2016 · Applied Thermal Engineering · Impact Factor: 2.74+3 more authors…ArticleApril 2015+5 more authors…ArticleApril 2014+6 more authors…Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.This publication is from a journal that may support self archiving.Last Updated: 17 Feb 17