Case Study on Applications of Measurement Equipment for Dynamic Line Rating

IN

DEGREE PROJECT ELECTRICAL ENGINEERING, SECOND CYCLE, 30 CREDITS

STOCKHOLM SWEDEN 2020 ,

Case Study on Applications of Measurement Equipment for Dynamic Line Rating

LINUS DAHLGREN

KTH ROYAL INSTITUTE OF TECHNOLOGY

SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE

Examiner: Patrik Hilber (KTH)

Supervisors: Kateryna Morozovska (KTH), Erik Lejerskog (Ellevio AB)

School of Electrical Engineering and Computer Science (EECS)

KTH Royal Institute of Technology

Abstract

Dynamic Line Rating is the concept of rating the ampacity of power lines depending on the thermo- dynamic conditions of the lines. It is an alternative to Static Line Ratings, which are set to maintain network safety requirements under unfavourable operating conditions. Thus Dynamic Line Rating allows the network operator to utilise unused capacity of the lines, and operate network assets closer to their limits while still maintaining network safety requirements.

This thesis explores the potential of Dynamic Line Rating through an analysis of a pilot project from Ellevio AB and Heimdall Power. The goal is to ascertain the ideal placement of monitoring equipment for Dynamic Line Rating purposes, determine an estimate of the transmission capacity increase, and study the economic impact of Dynamic Line Rating implementation.

Data from the measurement equipment used in the pilot project has been analysed to ensure that it is operating correctly. This analysis showed that the line temperature measurements were malfunctioning.

Because of this, the analysis of Dynamic Line Rating in this thesis is strictly weather based.

The most critical spans along a line have been identified through a method that has been developed for

this thesis. The capacity increase is estimated to 23% when Dynamic Line Rating is implemented. An

economic analysis quantified the potential value of this capacity increase from both the perspective of a

wind power producer and a DSO. This analysis also showed that DLR show a correlation towards wind

power generation and no correlation towards the general load profile of the line.

Sammanfattning

Dynamisk gradering ¨ ar ett alternativt s¨ att att gradera utrustning i elkraftsystem (luftledningar, transfor- matorer). Den r˚ adande normen ¨ ar en statisk gradering som ¨ ar satt f¨ or att s¨ akerst¨ alla att systemet h˚ aller sig inom s¨ akerhetsgr¨ anserna ¨ aven vid of¨ ordelaktiga f¨ orh˚ allanden. En dynamisk gradering ber¨ aknar last- barheten kontinuerligt utifr˚ an realtids eller prognosticerade m¨ atv¨ arden. Detta till˚ ater drift n¨ armare de faktiska s¨ akerhetsgr¨ anserna, vilket ¨ okar utrustningens ¨ overf¨ oringskapacitet. Det ¨ ar s˚ aledes ett attraktivt forskningsomr˚ ade inom elkraftmarknaden, d˚ a utbyggnad av ¨ overf¨ oringskapacitet i nul¨ aget ofta ¨ ar f¨ orenat med stora kostnader och l˚ anga handl¨ aggningstider.

I Oktober 2019 p˚ ab¨ orjade Ellevio AB tillsammans med Heimdall Power ett pilotprojekt om dynamisk lastbarhet. M¨ atutrustning utvecklad av Heimdall Power monterades p˚ a en regionalledning, f¨ or att ut- forska potentialen hos b˚ ade m¨ atutrustningen och den dynamiska lastbarheten f¨ or ledningen i fr˚ aga. Det h¨ ar examensarbetet har som en del i pilotprojektet haft som m˚ al att besvara fr˚ agor g¨ allande den po- tentiella ¨ okningen i ¨ overf¨ oringskapacitet och de ekonomiska konsekvenserna av detta, samt analysera m¨ atutrustningens tillf¨ orlitlighet och optimala placering l¨ angs ledningen.

En metod f¨ or att finna de mest kritiska punkterna l¨ angs ledningen har tagits fram i samband med examensarbetet. Metoden ¨ ar baserad p˚ a en litteraturstudie av tidigare metoder, och anv¨ ander tre faktorer f¨ or att g¨ ora en riskskattning av varje span p˚ a ledningen. Metoden applicerades sedan p˚ a ledningen i pilotprojektet f¨ or att finna de mest kritiska punkterna f¨ or att placera m¨ atutrustning.

Analysen av m¨ atutrustningens tillf¨ orlitlighet visade att temperaturm¨ atningarna hos Heimdalls neuroner inte fungerade korrekt. Detta har senare bekr¨ aftats genom tester fr˚ an Heimdall.

Den potentiella ¨ overf¨ oringskapaciteten ¨ okade i genomsnitt med 23% d˚ a dynamisk gradering implementer- ades. En positiv korrelation mellan den dynamiska graderingen och lastprofilen fr˚ an vindkraftsproduktion i omr˚ adet har ocks˚ a p˚ avisats. Den ekonomiska analysen visade att det potentiella ekonomiska v¨ ardet fr˚ an denna ¨ okning beror p˚ a m¨ angden nyetablering av vindkraft, och att v¨ ardet fr˚ an implementering av dy- namisk gradering d˚ a som mest ¨ ar 4.1 MSEK.

Sammanfattningsvis har det h¨ ar examensarbetet p˚ avisat b˚ ade m¨ ojligheter och utmaningar kring dynamisk

gradering. Vidare beh¨ over ovanst˚ aende analyser g¨ oras ˚ anyo d˚ a mer m¨ atdata samlats in, f¨ or att kunna

bekr¨ afta eller f¨ orkasta slutsatserna i rapporten.

Acknowledgements

I would like to extend my gratitude to my supervisor Kateryna Morozovska, not only for the support throughout the project, but also for the help with choosing a topic and enabling business contacts for the thesis.

I would also like to thank Erik Lejerskog for the opportunity to work on an interesting project, and for the close feedback and fruitful discussions that helped shape this thesis.

My gratitude also goes out to the collegues at Ellevio and Heimdall Power, for the patience, support and expertise throughout the project.

Finally, I would also like to extend a heartful thanks to Bo Flodin, for encouraging me and always keeping

my spirits high.

Contents

1 Introduction 13

2 Purpose and structure of the report 14

3 Previous work 15

4 Dynamic Line Rating 18

4.1 Joule heating . . . . 18

4.2 Convective cooling . . . . 19

4.3 Solar heating . . . . 22

4.4 Radiative cooling . . . . 23

4.5 Thermal calculations . . . . 23

4.5.1 Steady state . . . . 23

4.5.2 Dynamic state . . . . 24

4.6 Sag theory . . . . 24

4.7 Types of Dynamic Line Rating . . . . 26

5 Pilot study 28 5.1 Test area . . . . 28

5.2 DLR Calculations . . . . 28

5.3 DLR Results . . . . 29

5.3.1 Solar heating . . . . 29

5.3.2 Radiative cooling . . . . 30

5.3.3 Convective cooling . . . . 30

5.3.4 Dynamic Line Rating . . . . 31

6 Validation of measurement devices 33 6.1 Weather station . . . . 33

6.1.1 Temperature measurements . . . . 33

6.1.2 Wind measurements . . . . 33

6.1.3 Solar irradiance measurements . . . . 34

6.2 Heimdall Neuron . . . . 35

6.2.1 Line current measurements . . . . 35

6.2.2 Line temperature measurements . . . . 36

6.2.3 Angle of inclination . . . . 38

7 Economic analysis 39 7.1 Power transmission calculations . . . . 39

7.2 Results from windpower script . . . . 40

7.3 Net Present Value analysis . . . . 41

7.3.1 Input Values . . . . 41

7.3.2 Analysis method . . . . 43

7.3.3 NPV analysis results . . . . 44

8 Identification of critical spans 46 8.1 Method for finding critical spans . . . . 46

8.2 Proposed methodology for identifying critical spans . . . . 47

8.2.1 Macroclimatic analysis . . . . 47

8.2.2 Microclimatic analysis . . . . 48

8.2.3 Distance-to-ground analysis . . . . 48

8.2.4 General guidelines . . . . 49

8.3 Application of methodology on VL-X . . . . 49

9 Discussion 52

10 Conclusions 55

11 Future work 56

A Appendix 57

A.1 Main script . . . . 57

A.2 IEEE thermal calculation script . . . . 58

A.3 Cigre thermal calculation script . . . . 60

A.4 Wind Power Injection Script . . . . 62

A.5 Risk factor calculation script . . . . 65

List of Figures

1 Comparison of thermal conducitivity calculated by the different standards . . . . 20

2 Comparison of the IEEE and Cigre calculation methods for dynamic viscosity of air . . . 21

3 Inclined catenary span [1] . . . . 25

4 Solar heating thermal component from IEEE-738 and Cigre-601 calculation methods . . . 30

5 Radiative cooling thermal component from IEEE-738 and Cigre-601 calculation methods . 30 6 Convective cooling component from IEEE-738 and Cigre-601 calculation methods . . . . . 31

7 Dynamic Line Rating from IEEE-738 and Cigre-601 calculation methods for first week of November . . . . 31

8 Dynamic Line Rating for the entire measurement period with IEEE-738 and Cigre-601 calculation methods . . . . 32

9 Ambient temperature from weather station and control measurements in Arvika and Karlstad 33 10 Wind rose plots for November . . . . 34

11 Wind rose plots for January . . . . 34

12 Global radiation measured in weather station and Karlstad for November . . . . 35

13 Global radiation measured by weather station, Karlstad and estimated by IEEE-738 for November . . . . 35

14 Line current measurements from all neurons and a nearby substation . . . . 36

15 Measured and estimated temperature, and current 6/11 . . . . 37

16 Measured and estimated temperature for November 2019 . . . . 37

17 Load on VL-X at 230MW of added windpower . . . . 40

18 Relative SE and relative transmitted power from windpower indices . . . . 42

19 ROI as a function of windpower for DLR and SLR . . . . 45

20 Residual NPV as a function of windpower for DLR and SLR . . . . 45

21 Orto-photo of VL-X . . . . 50

22 Estimation of total risk along the entire line . . . . 51

23 The most vulnerable span along the line . . . . 51

List of Tables

1 List of variables . . . . 11

2 Greek symbols . . . . 12

3 Coefficients for Natural Convection . . . . 21

4 Forced convection coefficients in Cigre-601 method . . . . 22

5 Checklist table for which monitoring systems support which DLR methods . . . . 27

6 Data for VL-X conductor of type Ibis . . . . 28

7 Input variables for DLR calculations . . . . 29

8 ENS, ET and SE for different levels of added windpower . . . . 41

9 Correlation between ampacity from DLR and load profiles . . . . 41

10 Parameters of the NPV analysis . . . . 42

11 Distribution of investment costs for onshore wind farms . . . . 43

12 Total estimated risk for each span along the line . . . . 51

Nomenclature

List of abbreviations

ACSR ANM CFD CIGRE CSM CTE CTGM DLR DSO DTOC EPE EPRI ERCOT FSM GE

IEA-ETSAP IEEE IRENA LE MEMS MESAN NPV NYPA OHL ROI RTTR SAW SER SGDP SLR SMHI SPE TM TSO USi WF WM WRF

Aluminium Conductor Steel Reinforced Active Netowrk Management

Computational Fluid Dynamics

Conseil International des Grands R´ eseaux ´ Electriques Conductor Sag Measurement

Conductor Temperature Evaluation Clearance-to-ground Measurement Dynamic Line Rating

Distribution System Operator Delayed Time Over-current Experimental Plastic Elongation Electric Power Research Institute Electric Reliability Council of Texas Full-scale Monitoring

General Electric Company

Institute of Electrical and Electronics Engineers

International Energy Agency - Energy Technology Systems Analysis Program International Renewable Energy Agency

Linear Elongation

Micro-electric-mechanical System Mesoscale Analysis

Net Present Value

New York Power Authority Overhead Line

Return of Investment Real Time Thermal Rating Surface Acoustic Wave Seasonal Line Rating

Smart Grid Demonstration Program Static Line Rating

Sveriges Meteorologiska och Hydrologiska Institut Simplified Plastic Elongation

Tension Monitoring

Transmission System Operator Underground Systems, Inc Weather Forecast Rating Weather Model Rating

Weather Research and Forecasting

List of variables

Symbol Description Unit

C

Net Present Value from Economic Analysis SEK

C

Residual Net Present Value SEK

c(T ) Specific heat capacity at temperature T J/kg · K

D Conductor diameter m

d Strand diameter m

dtg

Distance to ground limit Dimensionless

dtg

Distance to ground when T

is 50

C Dimensionless

EN S ENS reliability index M W/year

ET Transmitted power index M W/year

ET

Relative transmitted power index %

Gr Grashof number Dimensionless

H Horizontal component of tension N

H

Elevation above sea level m

I Current A

I

Additional load from wind power generation kA

I

Global solar radiation intensity W/m

K

Elevation adjustment factor for solar heat flux Dimensionless

k

Skin effect factor Dimensionless

m Mass of unit length of conductor kg/m

N Added windpower Dimensionless

N u Nusselt number Dimensionless

N u

Nusselt number for perpendicular wind flow Dimensionless

N u

Nusselt number adjusted for wind angle Dimensionless

P Produced windpower M W

P

Corona heating W/m

P

Joule heating W/m

P

Magnetic heating W/m

P

Solar heating W/m

P r Prandtl number Dimensionless

q

Convective cooling W/m

q

Forced convective cooling low wind component W/m

q

Forced convective cooling high wind component W/m

q

Natural convection component W/m

q

Radiative cooling W/m

q

Evaporative cooling W/m

Q

Estimated solar heat flux, adjusted for elevation W/m

R(T ) Resistance at temperature T Ω

R(T

) Tabulated resistance at T

Ω

R(T

Tabulated resistance at T

Ω

R

DC-resistance Ω

R

Conductor surface roughness R

= d/[2 · (D − d)] Dimensionless

ROI Return of investment from Economic Analysis %

ROI

Desired return of investment in Economic Analysis %

risk

Unweighed distance-to-ground risk factor Dimensionless

risk

Microclimate risk factor Dimensionless

risk

Total risk factor Dimensionless

S Span length m

SE Sold electricity index SEK/year

T Temperature

C

T

High measurement temperature

C

T

Low measurement temperature

C

T

Ambient temperature

C

T

Film temperature around conductor

C

T

Conductor surface temperature

C

V

Operating voltage of VL-X kV

w Conductor weight per unit length kg/m

Y Years in operation Dimensionless

Z

Rate of inflation %

Z

Real interest rate %

Z

Nominal interest rate %

Table 1: List of variables

Greek symbols

Symbol Description Unit

α Solar absorptivity Dimensionless

α

Linear temperature coefficient of resistance K

β

Linear temperature coefficient of specific heat capacity K

δ Angle of wind incidence dimensionless

η Percentage of wind park production through VL-X %

ε Emissivity Dimensionless

Γ

Investment cost SEK

γ

Turbine cost SEK

γ

Construction cost SEK

γ

Other cost SEK

Λ Maintenance cost SEK

λ

Thermal conductivity of air W/m · K

µ

Dynamic viscosity of air kg/m · s

ρ

Air density kg/m

σ

Stefan-Boltzmann constant, 5.67 · 10

W/m

· K

θ Solar angle of incidence dimensionless

Υ Measurement uncertainty for DTG risk calculations m ζ

Quadratic temperature coefficient of resistance K

Table 2: Greek symbols

1 Introduction

As generation and consumption patterns are changing, power system owners are facing new challenges that need to be met to ensure a reliable and cost effective electric power supply [2]. In addition to a need for improved capacity to meet the ever-increasing demands of the consumers, the nature of generation is changing. Traditionally, power has been generated by sources with adjustable output (hydro, nuclear, carbon, oil etc.) and the power grid has been built around these generation centers. As generation is shifting towards renewable sources, which by nature are decentralized and intermittent [3], so does the demands on the power system.

According to [4], load flow analysis often confirms that additional wind power cannot be connected at the distribution level due to risk of overloading the existing lines. In these cases, costly grid infrastructure investments will have to be undertaken to be able to connect wind farms.

However, new technology may allow for a different solution that would allow grid owners to postpone or omit such major investments. Currently, the norm is to assign seasonal ampacity ratings to lines, which are commonly referred to as Static Line Ratings. These are based on worst-case scenarios and set to ensure that the line stays within its thermal limits. However, a different type of line rating called Dynamic Line Rating could be implemented as an alternative to utilise previously unused capacity [5].

Dynamic rating is in addition to OHLs also an interesting area of study for other components in the power system such as cables and transformers [2], but the major focus in this thesis will be about OHLs.

DLR uses real-time (or forecasted) measurements of different variables of interest to calculate to permis- sible ampacity dynamically. This allows for a more accurate estimation of the grid transfer capacity. It also increases the overall transfer capacity without having to upgrade existing lines, or build new lines which are costly and time-consuming projects [6]. Furthermore due to the natural synergy between wind power generation and heat dissipation from convective cooling, DLR helps increase the capacity for wind energy integration. In addition to utilizing untapped transfer capacity, DLR also prevents premature aging and dangerous sag-limit violations due to high conductor temperatures for when the conditions are worse than the assumption for static rating [7]. However, the ethical advantage of facilitating renewable generation must be weighed against the fact that a higher load on the lines yields higher losses.

Due to its potential economical and environmental benefits, DLR is identified as one of eight smart grid metrics by the U.S. Department of Energy [8]. Both IEEE and CIGRE have developed standardized methods and models for temperature calculations with varying weather conditions, a topic which will be discussed later in the thesis [9],[10].

[8] argues that although some progress has been made regarding the theoretical foundation of DLR, the

key challenges are still centered on quantifying the benefits of DLR through pilot studies in operational

grids. Ellevio AB are performing such a study in collaboration with Heimdall Power in the south-west of

Sweden, and this thesis will largely be centered around quantifying the effect of DLR, and the behaviour

of the measuring equipment through said pilot study.

2 Purpose and structure of the report

The purpose of this report and master thesis as a whole is to investigate the potential and consequences of implementing Dynamic Line Rating, through participation in and analysis of a case study by Ellevio AB and Heimdall Power. This investigation will include verifying that the measurement devices used for data collection are operating correctly. It will also include calculations of the Dynamic Line Rating through values from measurements in the study, and a comparison of methods and standards used for DLR calculations.

To analyse the consequences of DLR implementation, a comparison of how much additional wind power can feasibly be connected to the grid with DLR will be made. The basis of this comparison will be an economic analysis from the perspective of a grid owner, and a wind power producer in the area.

The report will also contain a literature study to ascertain the optimal placement of measurement equip- ment, and an application of the methodology found through said literature study on the line in the pilot study.

In conclusion, the purpose of the report is to answer the following questions:

• What is the increase in capacity from implementing DLR?

• What are the location of the critical spans along the line?

• What is the economic benefit from implementing DLR?

Section 3 contains a brief summary of previous work in the field. Section 4 contains a theoretical explana-

tion of the basic concepts of Dynamic Line Rating. Section 5 contains the methods for, and results from

the DLR modeling. Section 6 contains the results from the verification of the data from the measurement

devices used in the project, and Section 7 contains the method and results from the economic analysis of

DLR implementation. Section 8 contains the summary of a literature study regarding previous work on

the identification of critical spans. It also includes a proposed methodology for finding critical spans, and

the results from the application of said methodology on the line in the pilot project. Section 9 contains

a discussion of the results found in Section 5-8, Section 10 contains the conclusions that can be drawn

from the project, and Section 11 contains suggestions for future work that can build upon the work done

in this project.

3 Previous work

A new thermal rating approach: The real time thermal rating system for strategic overhead conductor transmission lines by Murray W. Davis was published in 1977, and is widely considered as the first mention of DLR in academia. There had been earlier considerations, but the development of SCADA and improved sensor technology in the late 1970’s made DLR a real possibility [11].

In the late 1990’s, San Diego Gas & Electric installed a CAT-1 unit on a 230kV transmission line that was a bottleneck for the system. The monitoring data was integrated into the EMS where it could be used by operators in decision making [12].

Since 2008, E.ON and Central Networks UK uses a weather based DLR with automatic calculations based on Cigre TB207 on a 132kV line from Skegness to Boston. Additionally, four Power Donuts are used for direct monitoring of line temperature. The DLR system controls the wind farm generation, so that if the line current reaches 95% of the dynamic rating, the wind farm output is curtailed. There is also a secondary protection system in place, where a DTOC relay trips if the generation is not successfully curtailed. Initially, E.ON expected the capacity to increase around 30% from the implementation of DLR. The potential capacity increase proved even greater, but the ampacity was limited to ensure safe operation of other components along the line. [13]. The temperature measurements from the Power Donuts were used for validation of the weather based rating system. In a follow-up report from 2009, it is shown that the theoretical temperature estimations from the weather data follow the actual conductor temperature closely. Furthermore it is concluded that the conservative assumptions about wind direction and solar radiation that was made in the project provided a good safety margin. It was concluded that DLR is a cost efficient alternative to more drastic reinforcement procedures in order be able to connect additional distributed generation to the grid[14].

In 2010, the Belgian TSO Elia implemented DLR on a 70kV network in the Ardennes region using devices developed by Ampacimon. They chose to implement an ANM (Active Network Management) solution which involved Elia retaining the right to curtail the wind farms’ output when necessary to guarantee safe network operation. However, it was acknowledged that curtailment destroys the economic value of wind power plants, and thus a solution was needed to minimize the necessary curtailment. Expanding the existing grid in a reasonable timeframe was not an option due to regulations and budget concerns, which lead to the option of implementing DLR to increase the capacity of the existing lines. The data output from the Ampacimon DLR computation is used in the EMS to determine if curtailment is necessary. If that is the case, the optimal power flow is calculated, and the wind farms receive new set points. This process repeats itself every 15 minutes, which is motivated by the thermal inertia of the conductors. This Dynamic Line Rating solution together with ANM resulted in a capacity increase of nearly 75%. Elia concluded that this was a valid and quick solution to increase the capacity of a saturated network, with positive effects for wind power integration [15].

In 2012, the German TSO Amprion GmbH performed a pilot study with two separate methods for DLR estimation, based on direct temperature measurements and weather station measurements. The temper- ature of the line was measured with two SAW units. It is mentioned in the technical report that the cost of the pilot systems and technical effort was quite high, and that the effort of installation and necessary line outage made this approach unfeasible from an economical perspective. The Amprion pilot study used the temperature sensors to measure the validity of weather based DLR. By comparing the calculated line temperature with the measured one, an accuracy of the DLR model was calculated. Different types of scenarios relating to different levels of shading etc were compared, and the cumulative frequency of the deviation between measured and calculated temperature was calculated. The most dangerous type of deviation is a so called positive deviation, which implies that the measured temperature is higher than the calculated temperature. This means that the DLR model underestimates line temperature, which could lead to dangerous situations if the DLR model is allowed to control the ampacity of the line unchecked.

It was concluded that a safety margin of 5 K was necessary to account for this estimation error [16].

The Amprion pilot study also takes the dynamic system wide aspects of an increased capacity into

consideration. It mentions that higher line ampacity would affect both the system stability, as well as the

protection systems. In the latter, a high nominal current would make distinguishing fault and operational

currents from each other more difficult. The study also contained a comprehensive checklist of necessary

measures that had to be completed before DLR was applicable for the overhead line. In conclusion, the

study found that the goal of a capacity of 3150A was attainable 80% of the time [16].

Vattenfall has a Dynamic Line Rating pilot ongoing on a 145kV line on the west coast area of Sweden since 2014. The DLR method is weather based, and both IEEE-738 and a precursor of Cigre-601 called Cigre TB207 are used and compared. Conductor temperature is also measured to validate the weather based calculations. Furthermore, the DLR is calculated with commercial software, where two different softwares have been tested. One developed by USi that calculates the DLR from a single span weather measurement, and one from GE where the weather measurements support the calculations performed in a line-and-terrain model. Both these softwares use the IEEE-738 method, whereas Vattenfall performed the Cigre TB207 calculations themselves. The different calculation methods are compared as 1 minute averages and 15 minute averages. The result shows that at 1 minute averages, 99% of all data points are within a ±15% deviation. For a 15 minute average, 99.4% of the data points are within a ±10%

deviation. It is thus concluded that the methods are sufficiently equivalent and that a 15 minute sliding average is recommended [4].

Vattenfall also investigated whether MESAN-computed weather data from SMHI could be used as an alternative to local weather measurements. Findings showed that more than 40% of the rates calculated by data from MESAN were 20% higher than those calculated with local weather. To investigate the effect of shading, the wind speed and angle of attack were scaled to 50% and 25%. The calculations showed that 98% of the differences were within ±30%. The explanation for this was that the MESAN values does not consider the sheltered environment in a line corridor. The poor accuracy of this method shows that MESAN-computed weather data is not sufficient for DLR calculations [4].

It was also determined that the terrain model DLR calculations underestimated the single span calcula- tions by more than 10% almost 50% of the time. This is an indication that the single span calculations likely overestimate the transmission capacity. Furthermore the study showed that 0.2% of the calculated values underestimated the actual conductor temperature with more than 3 K. This corresponds to a change in sag which is less than 0.1m. In conclusion, the study showed that Cigre TB207 and IEEE-738 give equivalent results for DLR calculations. The DLR calculations are a correct estimation of the trans- mission capacity, and a significant increase in capacity from the SLR can be seen. This capacity can be utilized if the load current can be regulated [4].

Two studies of weather based DLR are compared in [17]. The studies were performed in an area in North Wales in the U.K, and along Snake River Plane in Idaho U.S. The U.S study used weather data from a number of meteorological stations combined with lookup tables for wind data generated by CFD-simulations. It then used the IEEE-738 method to calculate the steady-state current carrying capacity of the OHL. In the U.K. study, an inverse distance interpolation technique was used instead of CFD-simulations to estimate wind speed, wind direction, solar radiation and ambient temperature [18]. The U.K. study also used Monte Carlo simulations to account for the uncertainties in the system.

[17] considered this a key point as understanding the uncertainties of key variables would be essential for success. However the U.K. method suffered from its crude wind estimation technique, and contained large modelling errors in the summer when convective cooling is the dominating factor in the cooling of the line. In conclusion both studies showed promising results, but suffered from inaccuracies in the wind modelling, which decreased the confidence in the solution.

As mentioned in Section 1, the US Department of Energy has identified DLR as one of eight key metrics in smart grid transmission and distribution infrastructure [8]. In a report from 2014, the New York Power Authority (NYPA) and Oncor Electric Delivery Company both demonstrated DLR technologies as part of the Smart Grid Demonstration Program (SGDP) from the US Department of Energy. The NYPA’s DLR project was focused on assessing a range of both prototype and commercially available DLR products, whereas ONCOR focused on demonstrating the commercial viability of established DLR technologies, and a full-scale automatic integration in real-time system operations [19]. NYPA used conductor temperature sensors developed by the Electric Power Research Institute (EPRI), as well as a video sagometer, a weather station and the ThermalRate system developed by Pike Electric Inc [20]. Oncor partnered with Nexans, who amongst other things have developed the line tension measurement system CAT-1, one of the most established DLR-solutions on the market. CAT-1 loadcell devices were installed on 345kV and 138kV line segments, along with auxiliary communication and computation devices also provided within the CAT-1 package [21].

The NYPA encountered reliability issues with the EPRI DLR-system, whereas Oncor encountered fewer

complications with the Nexans system. Both complications were in a large degree related to low load

situations, where the temperature difference between conductor and ambient air was small, which lead to large measurement errors which affected the estimation of effective wind speed. In the end, both projects revealed an increase in available capacity with the help of DLR. The NYPA also found a positive correlation between wind farm output, dynamic rating and line loading. The Oncor project was largely focused around integrating the Nexans DLR system with the ERCOT control room. DLR has been implemented before, but this is the first time the dynamic ratings have been automatically incorporated into the system state estimator tool [19].

E.ON installed DLR-systems in its regional grid in 2013. The 50kV network at ¨ Oland was a bottleneck for wind power development in the area, and it was determined that expanding the network with a new 130kV cable would have been too expensive. The DLR system on ¨ Oland employs a wide range of measurement equipment. It contains weather stations, direct temperature measurement solutions, a CAT-1 for strain-measurement and also direct measurements of the sag using sensors. One of the most interesting points about the ¨ Oland system is that it is a comprehensive full-scale implementation, with automatic control of the wind farm power as well as protection system integration. Up until 2018, the wind park was expected through simulations to be curtailed for about 10h/year. With the DLR system in place, it has yet to have been curtailed at all due to high temperatures in the conductors [22]. There have however been both planned maintenance on the OHLs which have lead to curtailment, as well as software issues with different parts of the system. In 2018, Sweden experienced an unusually warm summer. This resulted in several spikes in the dynamic load, where the wind farm had to be curtailed. In a system with static ratings, extreme weather conditions such as this could have resulted in potentially dangerous loading situations.

The E.ON DLR project on ¨ Oland showed that there are financial benefits for the wind power producer in using DLR for grid connection. For the grid owner there are advantages but also higher losses, as well as equipment and maintenance costs for the new system components. The project showed that the challenge is often not primarily in the DLR implementation, but rather the integration with protection and communication systems [23].

In 2015, Idaho National Laboratory began a DLR study in cooperation with AltaLink, Alberta TSO.

The study used meteorotical and operational data to calculate the dynamic ampacity with DLR, with the help of a weather based DLR called GLASS, which was developed by Idaho National Laboratory. GLASS uses the IEEE-738 standard. It also uses CFD to increase the accuracy of wind data inbetween weather stations. The largest error from CFD modelling was 13%, which would translate to an approximate ampacity error of 6% as it is roughly proportional to the square root of the wind speed. There were several areas being studied, and the minimum mean capacity increase that was observed was 22%. However it is worth noting that since there were no direct measurements on the line, it is difficult to validate the accuracy of the DLR estimation [8].

In 2015-2016, a study in Northern Ireland showed promising results regarding the use of machine learning

algorithms to forecast weather variables for DLR implementation. The authors argued that the results

proved the feasibility of computing line-rating forecasts using machine learning [24].

4 Dynamic Line Rating

Dynamic Line Rating is a collection of procedures that allows power system operators to operate power lines closer to the thermal limits of the conductor. Whereas a static or seasonal line rating uses con- servative estimates of ambient variables (such as ambient temperature, solar radiation and wind etc), a dynamic rating uses continuously measured or forecasted variables. With knowledge of these variables, the operator can accurately determine the permissible ampacity of a line in the present or imminent future [25].

This can be done in a number of different ways which will be discussed in Section 4.7, however the foundation of all dynamic rating is knowledge and control of the heat balance. For overhead lines, the following components affect the heat balance:

• Joule heating P

• Solar heating P

• Convective cooling q

• Radiative cooling q

• Magnetic heating P

• Corona heating P

• Evaporative cooling q

There are two leading models for calculation of line heat balance, developed by IEEE and CIGRE. Both use some or all of the aforementioned components, however the components are not calculated in the same way for both models. Hence the most intuitive way to present these models is by a side by side comparison for each thermal component. This section will contain the fundamental differences between the methods. A more comprehensive comparison can be found in [25],[26].

Cigre model

P

+ P

+ P

+ P

= q

+ q

+ q

(1)

IEEE model [9]

P

+ P

= q

+ q

(2)

The Cigre model has three components that the IEEE-model does not have, namely magnetic heating (P

), corona heating (P

) and evaporative cooling (q

). However, it is difficult to measure humidity along the line for long distances hence evaporative cooling is often omitted in the heat balance calculations.

Furthermore for conductors made from aluminium alloy the magnetic and corona heating are sufficiently insignificant to be omitted as well [27]. This leaves four components; joule heating (P

), solar heating (P

), convective cooling (q

) and radiative cooling (q

).

4.1 Joule heating

Joule heating can be described as the influx of thermal energy as a result of resistive losses in the conductor. The definitions for joule heating per unit length differ between the IEEE and Cigre models.

In the IEEE-738 model, the joule heating is described as:

P

= I

· R(T ) [W/m] (3)

where I is the RMS-value of the current, and R(T ) is the electrical resistance of the conductor as a function of temperature T .

R(T ) = R(T

) − R(T

) T

− T

· (T − T

) + R(T

) [Ω/m] (4)

In (4), R(T

) and R(T

) are values of the conductor resistance at high and low temperature. These

values are either given by the conductor manufacturer, or can be found in handbooks for ACSR con- ductors. They are widely accepted within the community and include magnetic effects, skin effect and lay ratios. They enable calculations of the resistance at every temperature T between T

and T

through linear interpolation [9].

The Cigre-601 method does not use empirical values to calculate R(T ) as the IEEE-738 method, but instead uses the DC-resistance R

, which gives:

P

= I

· R

(T ) [W/m] (5)

The DC-resistance is calculated from the resistivity of the materials at the given temperature, the cross- sectional area and the conductor temperature. The resistivity of a material at temperature T

is:

R(T

) = R

· [1 + α

(T

− 20) + ζ

(T

− 20)

] [Ω · m] (6)

The ζ-component is the quadratic temperature coefficient which only becomes significant at temperatures higher than 130

C. Hence for DLR-calculations at standard temperatures, it may be omitted.

As the conductor is energised with alternating current, the skin effect factor is present in the AC resistance.

In these cases the Joule heat gain is:

P

= k

· I

· R

(T ) [W/m] (7)

where k

is the skin effect factor. For standard applications of conductor diameter and load frequency, the skin effect factor is below 2% [10].

4.2 Convective cooling

Both the IEEE-738 and Cigre-601 methods divides convective cooling into two types: Natural Convection (in some cases denoted Free Convection) and Forced Convection. Natural Convection occurs during low wind speeds. The conductor heats surrounding air, which rises and is replaced by cool air. During higher wind speeds, air is blown past the conductor and thus carries heat away. Both methods also recommend that only the larger one of the components is used (and not both), as this is conservative [9],[10].

There are two equations that describes Forced Convection according to the IEEE-738 method. One that is accurate at low wind speeds but underestimates the heat loss at high wind speeds, and one that is accurate at high wind speeds but underestimates the heat loss at low wind speeds. The method recommends that both equations are solved for, and the larger one is used. Equation (8) is more accurate at low wind speeds, and (9) at high wind speeds.

q

= K

[1.01 + 1.35 · N

] · λ

(T

− T

) [W/m] (8) q

= K

· 0.754 · N

· λ

(T

− T

) [W/m] (9) T

is the surface temperature of the conductor, T

is the ambient temperature around the conductor, K

is the wind direction factor. It is a function of δ which is the angle between the wind direction and the conductor axis. The wind direction factor can be seen in (10) and is at its maximum when the wind is perpendicular to the conductor axis, i.e. at δ = ±

.

K

= 1.194 − cos(δ) + 0.194 cos(2δ) + 0.368 sin(2δ) (10) N

is the Reynolds number which is given by (11), and describes the magnitude of convective heat loss.

N

= D · ρ

· V

µ

(11) where D is the diameter of the conductor, V

is the wind velocity, ρ

is the air density and µ

is the dynamic viscosity of air, calculated according to the IEEE-738 method. ρ

and µ

are calcu- lated using the mean film temperature of the conductor boundary layer; T

and the elevation H

. The calculations for these variables can be seen in (12) - (14).

T

= T

+ T

2 [

C] (12)

ρ

= 1.293 − 1.525 · 10

· H

+ 6.379 · 10

· H

1 + 0.00367 · T

[kg/m

] (13)

µ

= 1.458 · 10

· (T

+ 273)

T

+ 383.4 [kg/m · s] (14)

λ

is the thermal conductivity of air, calculated according to the IEEE-738 method. It too is a function of T

and can be seen in (15).

λ

= 2.424 · 10

+ 7.477 · 10

· T

− 4.407 · 10

· T

[W/m ·

C] (15) The Natural Convection (or Free Convection) is described in (16) with some of the parameters used to calculate Forced Convection [9].

q

= 3.645 · ρ

· D

· (T

− T

)

[W/m] (16)

The convective cooling according to the Cigre-601 method is seen in (17)

q

= π · λ

· (T

− T

) · N u [W/m] (17) where λ

is the thermal conductivity of air calculated by the Cigre-601 method at the film temper- ature T

as seen in (18) and N u is the Nusselt number, which is calculated in different ways depending on type of convection.

λ

= 2.368 · 10

+ 7.23 · 10

· T

− 2.763 · 10

· T

[W/m ·

C] (18) The two methods have two different ways to calculate the thermal conductivity at film temperature, hence the different subscripts for λ

. As seen in Figure 1, these are not interchangable. The IEEE-738 method consistently estimates the thermal conductivity higher than the Cigre-601 method. Due to this discrepency, the methods should employ individual calculation of thermal conductivity according to their own standard, and a general expression that applies for both methods should not be used.

Figure 1: Comparison of thermal conducitivity calculated by the different standards

As mentioned previously, the Nusselt number is calculated differently depending on the type of convection.

In the case of Natural Convection, the Nusselt number is calculated as seen in (19).

N u

= A · (Gr · P

)

(19)

where Gr is the Grashof number (20), and P r is the Prandtl number (21).

Gr = D

· (T

− T

) · g (T

+ 273) ·

f

(20)

Figure 2: Comparison of the IEEE and Cigre calculation methods for dynamic viscosity of air

P r = c

· µ

λ

(21)

The dynamic viscosity of air is also calculated differently from the IEEE-738 method, as seen in (22).

However for the range of temperatures that is relevant for the applications in this report, the two methods are interchangable as seen in Figure 2.

µ

= (17.239 + 4.635 · 10

· T

− 2.03 · 10

· T

) · 10

[kg/m · s] (22) The coefficients A and m in (19) vary depending on the product of the Grashof and Prandtl number.

The different cases can be seen in Table 3. For forced convection, the Nusselt number for perpendicular

Range of (G

· P

) A m

From To

10

10

1.02 0.148

10

10

0.850 0.188

10

10

0.480 0.250

10

10

0.125 0.333

Table 3: Coefficients for Natural Convection wind flow is calculated in a similar manner, with Equation (23).

N u

= B · N

(23)

The Reynolds number is calculated in the same way as for the IEEE-738 method as in (11). B and n depends on the roughness R

, and the Reynolds number, and can be seen in Table 4. There is a third set of values for B and n, that applies for smooth conductors. However as the conductors in this project are stranded, this set is omitted.

The Nusselt number for perpendicular wind flow is then adjusted for wind direction factor. Hence the Nusselt number for forced convection is calculated according to the equation seen in (24).

N u

=

( (0.42 + 0.68 · (sin(δ))

) · N u

if δ ≤ 24

(0.42 + 0.58 · (sin(δ))

) · N u

if δ > 24

(24)

Stranded Conductors Stranded Conductors

R

≤ 0.05 R

> 0.05

N

B n B n

100 - 2,650 0.641 0.471 0.641 0.471

2,650 - 50,000 0.178 0.633 0.048 0.800

Table 4: Forced convection coefficients in Cigre-601 method

The Cigre-601 method makes a point to mention that turbulence has a considerable effect on the con- vection cooling rate, but is very difficult to assess for real overhead lines. At high winds, considerable errors can occur in dynamic rating calculations due to factors such as variability of wind, obstacles and bundled conductors.

In conclusion the Cigre-601 method recommends the use of the higher of the natural and forced convection values determined in Equation (19) and Equation (23). Since the convective cooling factor is proportional to the Nusselt number, it is convenient to compare which Nusselt number is the largest, and then use that for the calculation of the convective cooling factor by inserting the largest Nusselt number into (17) [10].

4.3 Solar heating

Solar heating is the heating component that comes from the sun shining onto the conductor. The heat energy transfer from solar heating depends on a number of factors such as the sun’s position, the heat intensity, absorptivity of the conductor and the surface area of the conductor. The expression for solar heating according to the IEEE-738 method can be seen in (25) [9].

P

= α · Q

· sin(θ) · D

[W/m] (25)

where α is the absorptivity of the conductor. The absorptivity depends on the condition of the conductor.

A new, shiny conductor will reflect most of the sun’s energy whereas an older, blackened conductor will absorb the sun’s energy instead. α lies in the range of 0 ≤ α ≤ 1 where 0 indicates full reflection and 1 full absorption. The angle θ is effective angle of incidence for the sun rays and depends on a trigonometric function of solar altitude and solar azimuth. Q

is the solar heat flux adjusted for elevation

Q

= K

Q

[W/m

] (26)

where:

K

= 1 + 1.148 · 10

· H

− 1.108 · 10

· H

(27) is the elevation adjustment factor, and Q

is the heat flux intensity at sea level, which depends on the solar altitude and the quality of the atmosphere (if the conductor is located in an industrial or clear atmosphere).

D

is the projected area of the conductor, which depends on not only the diameter of the conductor, but also it’s orientation compared to east-west. Further derivations for how to calculate solar altitude, solar azimuth, heat flux intensity at sea level etc can be found in [9].

The IEEE-738 method estimates the solar heat flux from the type of atmosphere the conductor is in, and the sun’s position in the sky. Hence there aren’t any actual measurements for these calculations, but the heat flux is calculated from the latitude, day of the year and time of day. It does not take into account variable factors such as cloud coverage.

The Cigre-601 method calculates the solar heat gain from the outer diameter of the conductor, the absorptivity of the surface and the global radiation intensity, as seen in (28).

P

= α · I

· D [W/m] (28)

According to the Cigre-601 method, measurement devices for global radiation intensity are reliable and

indexpensive, and suitable for line monitoring systems and dynamic line rating calculations. However, the

user should be adviced that the global radiation received by the conductor may vary along the line due to factors such as shading, cloud coverage, orientation of the line, reflectance from ground, etc [10]. Global radiation consists of both direct radiation and diffuse radiation. While the estimation-based method of IEEE-738 only considers direct radiation, the global radiation measurements of Cigre-601 considers both direct and diffuse solar radiation.

4.4 Radiative cooling

Radiative heat transfer occurs due to the emission of electromagnetic waves into the surrounding medium.

The conductor has a higher temperature than the surrounding medium, and therefore it radiates thermal energy at a rate that is proportional to the temperature difference to the power of 4 [27]. The equation for the radiative cooling component according to the IEEE-738 method can be found in (29).

q

= 17.8 · D · ε



( T

+ 273

100 )

− ( T

+ 273 100 )



[W/m] (29)

Emissivity - denoted by ε - is the radiative material property which describes the rate the conductor emits heat. It increases with age according to the relation shown in (30) [28].

ε = 0.23 + 0.7 Y

1.22 + Y (30)

where Y is the number of years in operation. It is necessary to mention that the aforementioned equation is a rough estimate, as the aging process is complicated and difficult to assess with absolute precision.

The equation presented in the IEEE-738 method is in fact a numerical representation of a more general representation of radiative heat transfer, which employs the Stefan-Boltzmann constant. This represen- tation is presented in the Cigre-601 method seen in (31) [10].

q

= π · D · σ

· ε · [(T

+ 273)

− (T

+ 273)

] [W/m] (31) A comparison of the (29) and (31) shows that

17.8 · D

· ε



( T

+ 273

100 )

− ( T

+ 273 100 )



≈ π · D · σ

· ε · [(T

+ 273)

− (T

+ 273)

] =⇒

17.8

100

≈ σ

· π =⇒

1.78 · 10

≈ 1.78128 · 10

(32)

It’s clear that the two representations are equivalent as the discrepancy from the approximation in the IEEE-method is as little as 0.128 · 10

. Hence it can be said that

q

= q

(33)

and that, since the Cigre method is slightly less approximative, for this project

q

= q

[W/m] (34)

4.5 Thermal calculations

Regardless of which method is used, the thermal balance equation is the same. Henceforth the calculations will yet again be presented in a general state, as they are applicable for both methods. When calculating the thermal behaviour of the line, there are two cases to consider. The steady state, which can be considered a momentary observation of the system behaviour, and the dynamic state which considers the thermal behaviour of the system for some period of time dt [25], [9], [10].

4.5.1 Steady state

The steady state method utilises the basic thermal balance presented in 4 and seen in (2). To calculate the ampacity, (3) can be inserted into (2) which gives:

I

· R(T ) + P

= q

+ q

(35)

The ampacity is then solved for as a function of the thermal components:

I =

s q

+ q

− P

R(T ) [A] (36)

4.5.2 Dynamic state

The steady state method is a quick and simple way to determine the permissible ampacity for a given set of weather parameters, however it fails to take into consideration the thermal inertia of the line [25].

If one instead would like to calculate the temperature of the line, or the time for a temperature shift of the line during dynamic conditions, the thermal inertia of the line must be considered. The change in temperature can then be expressed with the equation in (37)[10].

m · c · dT

dt = P

+ P

− q

− q

(37)

The mass m and specific heat capacity c determines the amount of energy required to heat a unit length of the conductor one degree

C (or K). The mass density can be considered constant for the range of relevant temperatures for this project. The specific heat capacity is temperature dependent as seen in (38), however for the ranges of conductor temperature expected in this project (≤ 200

C), it can also be considered constant [29].

c(T ) = c

· [1 + β

· (T − 20)] [J/kg · K] (38) The Cigre-601 method suggests that this equation can be solved numerically to calculate the temperature of the line, with 5-15 minute time steps as suitable. However, it is not suitable to use this method for fault current applications. In these cases, due to the short time-frame of a high current fault, it is more suitable to consider adiabatic conditions. As this is not relevant for the project, this method of calculation is omitted [10].

4.6 Sag theory

The sag of the line is a parameter of great interest for power system operators. It is the distance from the fastening point of the line, to the vertex of the catenary of the line. This is directly connected to the distance-to-ground of the line, which in Sweden is regulated by [30]. The minimum allowable distance to ground is 7 + s meters for populated areas and 6 + s for unpopulated areas. s is an additional safety margin dependent on the voltage of the system. S = 0.4m for a directly earthed 132kV system, which would yield total safety limits of 7.4m and 6.4m respectively.

In this section, a method to calculate the sag of a line will be presented. This method is presented in Cigre TB324. Cigre makes a point to underline that regardless of the calculation method, a clearance buffer is still required to account for modeling undercertainties when designing new lines, or uprating old ones [1].

When subjected to uniform loading per unit length, an overhead line takes the form of a catenary between the support points of the surrounding towers. The shape of the catenary is a function of the weight of the conductor per unit length, w, and the horizontal component of tension, H. The sag itself is a function of these parameters, as well as the span length , S and elevation difference between the fastening points.

The catenary equation can be seen in (39), where y(x) is the height of the catenary above the vertex, and x is the horizontal distance from the vertex.

y(x) = H w · h

cosh ( w · x H ) − 1 i

[m] (39)

The sag Ψ is found at x = S/2. The expression for sag can thus be seen in (40).

Ψ = H w ·



cosh ( w · S 2 · H ) − 1



[m] (40)

Figure 3: Inclined catenary span [1]

This is correct for level spans, where the fastening points are at the same height in both ends. For inclined spans, the vertex will be shifted off-center, and as such the new position of the vertex must be calculated.

The equation for calculating the vertex for an inclined span is seen in (41).

X

= S 2 − H

w sinh





h/2

H

w

sinh



 [m] (41)

X

is the distance from the lowest fastening point to the vertex, and h is the difference in elevation between the fastening points. A representation of an inclined span can be seen in Figure 3. The variable that in the figure is denoted as D has in this report been called Ψ, in order to avoid confusion with the diameter of a conductor.

The sag can now be calculated depending on the reference point. The sag with respect to the lowest fastening point can be found by inserting X

into (39). To get the sag from the highest fastening point, one must instead insert X

= S − X

[1].

To accurately account for the dynamic conditions of an OHL, the elongation of the line during such conditions must be estimated. To do so, Cigre TB324 has grouped such methods into three types of models; Linear Elastic (LE) model, Simplified Plastic Elongation (SPE) model, and Experimental Plastic Elongation (EPE) model. For the scope of this project, the LE-model will be sufficient and as such it is the only model that will be presented here. The curious reader is referred to [1] for a more rigorous description of SPE and EPE models.

The LE model omits two types of elongation factors, which are ”settlement and strand deformation”

and ”long-time or high tension plastic strain”. Because of this, the LE model is an underestimation of the actual sag (and require clearance buffers for safe operation). The parameter of interest to estimate for this project is the linear thermal strain of an ACSR conductor. The composite coefficient of linear thermal expansion for an ACSR conductor can be calculated according to (42).

α

= α

 E

E

  A

A

 + α

 E

E

  A

A



[K

] (42)

The thermal elongation can then be calculated as seen in (43)

∆L = α

· ∆T

· L [m] (43)

The sag of the conductor can then be estimated by (44).

Ψ =

r 3 · S · (L − S)

8 [m] (44)

In a Master Thesis from 2011, Elisabet Lindberg estimated the correlation between temperature and distance to ground on a 130kV line belonging to Vattenfall. It was determined that the distance to ground decreased (i.e. the sag increased) by 3.7 cm per degree. However, the line in question had a diameter of 39.2mm whereas the line in this project has a diameter of 19.9mm. Furthermore the sag- thermal relationship also depends on the span length. Thus the results are not directly applicable for the area in this project [31].

4.7 Types of Dynamic Line Rating

There are many types of methods for line rating, some of which fall within the scope of Dynamic Line Rating. Kateryna Morozovska and Patrik Hilber have proposed a categorization of line rating methods in [32]. The categories are:

• Static line rating (SLR): A standard fixed rating, which is specified by international or national standards, and conservative weather assumptions.

• Seasonal rating (SER): A summer/winter rating or in some cases summer/autumn/winter rating, where the rating is set separately for a span of time based on a conservative estimate of the typical weather conditions of the period.

• Weather model (WM): A rating based on averaged weather data for several years. More accurate than the seasonal rating.

• Weather forecast (WF): A rating based on real time weather that data that is collected near the conductor and used to create a weather forecast. The ratings are set according to the forecast.

• Conductor temperature evaluation (CTE): When the conductor temperature is measured with the help of a temperature sensor.

• Tension monitoring (TM): When the tension of the line is monitored, by placing load cells in series with the insulator strings. Most tension systems requires installation of weather monitoring equipment for further evaluation of system parameters and calculation of the ampacity of the line.

• Conductor sag measurement (CSM): A more advanced system that can actually measure the sag of the line. By placing such equipment in the worst case parts of the power system, it can be operated within safety margins.

• Clearance-to-ground measurement (CTGM): A new type of monitoring system for OHLs that does not measure sag, but clearance-to-ground through imaging hardware either mounted on the line or on the ground below the line. It is a more relevant measurement as it gives direct information about the clearance to ground, which is a limiting factor in power system operation.

• Full scale monitoring (FSM): This method can be a combination of several of the aforementioned methods. The main feature of this category is the placement of sensors along the line. However with present technology, this is expensive.

Given these categories, the different types of DLR mentioned in this report can be assigned to the correct

category. This categorization can be seen in Table 5.

Monitoring System Name WF CTE TM CSM CTGM FSM

ADR Sense X

CAT-1 [21] X X

EPRI Sensor [20] X X X

EPRI Sagometer [20] X

Heimdall Neuron X X

Power Donut [33] X X

SAW Technology [16] X

ThermalRate

[20] X

Table 5: Checklist table for which monitoring systems support which DLR methods

1

These types of measuring equipment all estimate sag by using a 3-axis accelerometer to determine the inclination of

the line at their point of suspension.

5 Pilot study

Ellevio AB has in collaboration with Heimdall Power launched a pilot study to determine the viability of using DLR as a cost efficient way to improve the transmission capacity. The area of the study is located in the south west of Sweden, where Heimdall Neurons are mounted on a 24km long 130kV regional line. A weather station is placed in the nearest substation to measure ambient temperature, wind speed, wind direction and global radiation. A collection of scripts have been developed in the statistical computing language R to analyse the potential increase in transmission capacity, and validate the Neuron measurements. This section contains an explanation of the test area, the calculation methods and the scripts used to estimate the Dynamic Line Rating and thermal components using both the IEEE-738 and the Cigre-601 calculation methods, and a discussion regarding said results.

5.1 Test area

The line in the pilot study is located in the south-west of Sweden. It connects generation from nearby wind farms and imported power from Norway to an adjacent urban center. Future wind power integration into the network is impeded by the capacity of the line in question, which in this report will be referred to as VL-X. A pre-study for the pilot determined that the terrain height for the line varies within 58-208 meters above sea level. The wind direction is mostly from south-west, but is highly variable close to the ground due to shading from terrain and vegetation. The line is an ACSR-conductor of the type Ibis. The relevant parameters for the line can be seen in Table 6.

Diameter

Area # threads Thread Core Line Weight Tensile strength DC-resistance Heat capacity

Al Fe

mm

Al Fe mm mm mm mm kg/km kN Ω/km J/K · kg

234 26 7 3.14 2.44 7.32 19.9 812 70.53 0.1434 788.6

Table 6: Data for VL-X conductor of type Ibis

In addition to the conductor data presented in Table 6, there is additional data used as input variables for the DLR calculations. These can be seen in Table 7. As mentioned in Section 4, the absorptivity and emissivity are used to determine solar heating and radiative cooling respectively. The latitude is an input parameter in the estimation of solar heating used in the IEEE-738 method. The elevation and line orientation are used in both IEEE-738 solar heating estimations and convective cooling calculations.

The atmosphere type is relevant for the solar heating estimation for the IEEE-738 method, and is set to clear as the line is located in a rural area with low air pollution levels. The aluminum temperature coefficient is used for the entire conductor (although part of it is steel) to simplify calculations. It is used to estimate the linear temperature dependence for the resistivity of aluminum.

5.2 DLR Calculations

As shown in Section 4.7 there are many methods for DLR. In this section, the reasoning behind the choice of calculation method used in the project, and an explanation of said method is presented.

The choice of method is based on the available data for the line. The available measurement equipment measures the weather conditions (ambient temperature, wind speed and direction, global radiation) and the line temperature and sag. This allows for three types of DLR; WF, CTE or CSM. Early in the project, either of these methods were a possibility. However during the analysis and validation of the measurement devices seen in Section 6, it was determined that the line temperature measurements of the Neuron were not working correctly. Furthermore the angle measurement from which the sag of the line was derived was in need of calibration. Neither of these issues were fixed before the end of the project, hence neither CTE nor CSM was applicable. They are however still interesting methods, and exploration of said methods are mentioned in Section 11 as areas for future study.

The method of elimination thus yielded a weather based DLR method as the only possible alternative for

the duration of the project. It is based on the thermal components calculated either from the IEEE-738