1 Abstract .edu
Final Project Report
Senior Design
“Statistical Analysis of Power System Faults”
May99-15
April 22,1999
Advisor:
Dr. James McCalley
Team Members:
|Corey K. Proctor |___________________________ |
|Chris Dennison |___________________________ |
|Alvina Hendradi |___________________________ |
|Greg Bahl |___________________________ |
| | |
Table of Contents
1 Abstract 4
2 Problem Statement 4
2.1 What is risk? 4
2.1.1 Events 4
2.1.2 Consequences 4
3 Objectives 5
4 End product description 5
5 Assumptions and Limitations 5
6 Technical Approach 5
6.1 Overview of the research including: 5
6.1.1 Description of the data 5
6.1.2 Description of the statistical method 11
6.1.3 Description of the power line 11
6.2 Description of the process used to determine the associated risk 11
6.2.1 Statistical model 11
6.3 Determine a recommendation for the power line 14
6.4 Identify areas of further study 14
7 Evaluation of Project Success 14
8 Summary of Testing 15
9 Recommendations for continued and/or additional work 15
10 Final Budgets 15
10.1 Budget Chart 16
11 Final Timeline 17
12 Lessons Learned 17
13 Group Members: Team MAY 99-15 17
14 Summary and Conclusions 18
15 Appendix 18
15.1 Proposed Technical Solution 18
15.2 Progress to Date 19
15.2.1 Research 19
15.2.2 Risk Calculation 20
15.2.3 Power Flow 22
15.2.4 MATLAB 23
15.3 Design Flow Chart 23
16 References 24
Figures
Figure 1 Locations for weather data collectors 7
Figure 2 System one-line 11
Figure 3 Load duration curve 12
Figure 4. One line diagram 23
Figure 5. Design flow chart 24
Tables
Table 1 Data collector tolerances 6
Table 2 Included data and units 6
Table 3 Location key for Iowa map 7
Table 4 Weather data sites 7
Table 5. Transmission line characteristics 20
Equations
Equation 1 Number of outages per year 12
Equation 2 Probability for transformer fault 12
Equation 3 Risk equation for day and night values 13
Equation 4 Probability equations 13
Equation 5 Summation of risk during the day and night 14
Equation 6 Total risk equation 14
Equation 7 Decision rule 14
Equation 8. Heat balance equation 21
Equation 9. Reduction of tensile strength 22
Equation 10. Risk calculation formula 22
Abstract
The purpose of this project is to study a power line to determine the risk of an outage. In order to make this decision two kinds of analysis will be conducted using statistical data including wind velocity, temperature, and outage probability. The first analysis to be done will be the study of the overloading risk of the current power line without any changes. The second analysis is the benefit assessment, which will analyze the cost and benefits associated with reinforcement to the given power line. These results will then be used to make an assessment of risk of the power line and compare that to an upgraded system.
Problem Statement
A power line transports electricity for most of its life, barring outages or normal switching operations. This means that the power line is subject to all seasons, all types of weather, and all outage cases.
The effects of weather in a statistical setting have not been fully explored in the past operation of the power system. A conservative estimate has been used to determine the safe operating point of the power line. Because of deregulation in the power system industry, construction has slowed or stopped while the load is increasing and slowly approaching the lower limit of the estimate.
This project will look at weather and outage statistics to try to determine a new operating limit of a power line. It will also determine the amount of risk associated with running the power line over the current limits and study reinforcements that will reduce this risk.
1 What is risk?
In order to understand the problems of running a power delivery system, a definition of risk is needed.
Risk is the product of an event’s probability and its resultant impact or consequence.
1 Events
Load growth, weather, and/or outages are events that may cause overloading of a transmission line. Outages can be forced or scheduled. Forced outages are faults, miss-operation, or miss-coordination. The utility and the consumer plan scheduled outages. Scheduled outages have minimal impact or are financially rewarding to the consumer, allowing the utility to do scheduled maintenance or reduce loading during peak load conditions.
2 Consequences
Consequences of these events affect the consumer’s ability to live and do business. Depending on the type of consumer, there could also be financial losses. There will also be a cost to the utility due to these consequences, these costs include damage to the system and loss of profits.
Objectives
Develop a report that will outline the following items
• Overview of the research including:
- Description of the data
- Description of the statistical method
- Description of the power line
• Description of the process used to determine the associated risk
• Determine a recommendation for the power line
• Identify areas of further study
End product description
Our project is a risk assessment of an actual transmission line. This risk assessment took in account real statistical weather data, a transmission line in a real power system. The client for this project will take full ownership of this report and all information that is obtain from this project.
(Including a summary of capabilities and ownership)
Assumptions and Limitations
Concerning some line characteristics data, due to the limitation of source, the data needed for the Matlab Program input cannot be an exact and accurate values. For example, the value for the absorption coefficient of the conductor. Since this value is not available for our conductor, the coefficient was approximated by doing a sensitivity test (seeing how a small percentage of increasing the value may affect the risk). This test was described in the Technical Approach section.
Technical Approach
(Including descriptions of possible alternatives considered and reasons for selection.)
1 Overview of the research including:
1 Description of the data
1 Weather
Weather is considered one of the largest influences on a power line. It influences the temperature of the conductor by environmental factors that include wind speed, wind direction, temperature, and the amount of solar radiation. One of the purposes for this project was to determine what was available for weather data and how can it be used to further this study past a worse case scenario.
The challenge was to find real and meaningful data that could be used in the current MATLAB code as described in SECTION ---- . In order for the data to be meaningful it has to account for the following:
1. Describe a location close or exactly at the power line.
2. Describe a location that closely resembles the power line corridor. This condition will exclude most National Weather Service Sites because of their location at an airport. This is a very different environment compared to the protected corridor of the power line. The airport sensors are in a wide open field while a normal power line corridor is usually protected.
3. Take measurements at a reasonable rate and at the same time. Preferably as many times as can be recorded.
4. Have reasonable accuracy at the low end of operation. This is especially important when measuring low wind speeds. Again NWS weather stations are not up this task because of stall speeds around 4-5 ft/sec. The apparatus is tuned to the high end, because of the importance of safety and not pure collection (Seppa, Teppani O.).
This list presents a challenge because it limits one of the largest collectors of wind data: The National Weather Service! In order to get reasonable data an outside source was required. The High Plains Climate Center was found to have digital records for weather stations throughout the Midwest. The HPCC collects data from its own set of data recorders spread throughout the state. The location and data acquired fit the most requirements.
The HPCC source was able to provide data from sites along highways and interstate roadways. This location can be assumed to be close to the power line corridor conditions. Each weather site takes readings every hour and this data is then stored in a digital format. The following tolerances from the HPCC web page show the accuracy of the data (HPCC weather page).
Table 1 Data collector tolerances
|Sensor |Variable |Instal. Ht. |Accuracy |
|Thermistor |Air Temperature |1.5m |0.25( C |
|Thermistor |Soil Temperature |-10cm |0.25( C |
|Si Cell Pyrometer |Radiation-Global |2m |2% |
|Cup Anemometer |Wind Speed |3m |5% (0.5m/s-1.64ft/s start up) |
|Wind Vain |Wind Direction |3m |2( |
|Coated Circuit |Relative Humidity |1.5m |5% |
|Tipping Bucket |Precipitation |0.5m to 1m |5% |
Originally obtaining free data was considered, but due to the lack of quality (only able to get monthly averages) this data was purchased. The amount of data was limited to stations in Iowa and only for a ten-year period. The ten-year period was used to ensure a good statistical setting. After ten years this data was not available at most stations. In the future more station data from outside of Iowa could be purchased to gain a better understanding of the weather.
The data obtained from the HPCC was more then what is currently used in the MATLAB code and further study could be used to determine the usefulness of the extra data. The data received includes:
Table 2 Included data and units
|Month |NA |
|Day |NA |
|Year |NA |
|Hour |1:00-24:00 |
|Air Temperature |(F |
|Ground Temperature |(F |
|Relative Humidity |% |
|Wind Speed |MPH |
|Wind Magnitude |MPH |
|Wind Direction |( |
|Vector Sd. |( |
|Radiation Flux |Kcal m-2 |
|Precipitation |in |
Currently the only data used in the statistical setting was the data for wind speed and air temperature.
Figure 1 shows the state of Iowa and the location of the weather collectors in the state. Table 3 gives the latitude and longitude of each station.
[pic]
Figure 1 Locations for weather data collectors
Table 3 Location key for Iowa map
[pic]
Data was ordered from the following sites because ten years worth of data was available, Table 4.
Table 4 Weather data sites
|Crawfordsville |
|Castana |
|Chariton |
|Gilbert |
|Gilmore |
|Nashua |
|Sutherland |
2 Power-flow data
A single contingency analysis has been completed. A firm understanding of the local power system was required to minimize the number of contingencies to only contingencies that affect the transmission line.
The utility has provided us with the power flow case for the transmission network. They have also given us specific information about the transmission facilities, loads, and approximate costs for the particular reinforcements. To solve the power flow for the different cases and contingencies, IPFLOW developed by EPRI was used. These power flows solutions gave currents for the four cases.
The four cases are types of reinforcements. The first is no change and is called the "base case". The second is "re-building" the transmission line. The third is called "add new transmission line". The final case is adding "micro-generation" at the location of the load.
Each case was analyzed for current conditions, five, ten, fifteen, and twenty years of load growth for only the substation. The client has provided an estimate about 3.5 percent load per year growth for the load in question. This portion of the project has given us the currents flowing down the transmission line under different contingencies. These currents will be used to directly determine the risk. Higher current lead to a higher risk.
To re-conductor the two sections with conductor that improves the current carrying capacity of the transmission line, the structure would have to be either modify structurally or rebuild. Due to time limitations and project goals we have decide that rebuilding is the only choice that we will have time to explore.
The client's planning department suggested, under increased loading, ACSR 1431 kcmil wire for the replacement conductor. This wire would increase the current carrying capacity to 1272 amperes.
Adding a new transmission line was considered to reduce the loading of the south section under a single contingency. This line will be in parallel with the north line but come for substation 1's bus 1.
Adding a combustion turbine generator was considered to reduce the load.
3 Matlab inputs
Deterministic Data:
• Diameter
As obtained from the conductor description table, the equivalent diameter of the conductor used, which is 636.0 AWG-kcmil with AAC type, is 1.090 inches.
• emissivity coefficient
Obtained from the web, assuming the conductor material is Aluminum Alloy A3003, oxidized with temperature above 900 F, the emissivity coefficient is 0.40.
• absorption coefficient
By doing a sensitivity test due to unavailability of sources, the value cannot be determined accurately. The default value of the software is 0.5 .
|Coefficient |Mean PDT |Deviation PDT |Probability |Risk |
|0.5 |61.4691 |15.3185 |0.022398 |0.011034 |
|0.6 |61.6269 |15.2749 |0.022369 |0.010986 |
|0.7 |61.7835 |15.2315 |0.02234 |0.010937 |
|0.4 |61.3099 |15.3626 |0.022425 |0.01108 |
For 10 % changes of the coefficient:
The PDT mean value will change by = 0.25%
The probability will change by = 0.13%
The risk will change by = 0.435%
Impacts’ Data:
• Safety margin of sag = 37.3871 inches
This is an IEEE standard.
• Sag increasing rate
The default value suggests 0.6 inches/Celsius. By doing a sensitivity test, apparently changes made in Impact’s Data column will not change other output except the risk.
|Rate |Mean PDT |Deviation PDT |Probability |Risk |
|0.7 |61.4691 |15.3185 |0.022398 |0.011034 |
|1.7 |61.4691 |15.3185 |0.022398 |0.039949 |
|1.9 |61.4691 |15.3185 |0.022398 |0.068864 |
|2 |61.4691 |15.3185 |0.022398 |0.083322 |
The risk starts to change if the rate reaches 1.7 in/(C. Below that value there is no changes whatsoever. Apparently the change rate is somewhat varies. For example the risk of 1.8 rate is the same as the risk when the rate is 1.7. Therefore it cannot be determined. However, just for a suggestion, if the sag increasing rate falls between 0 to 1.7 in/(C, than the risk will not be affected by the value. According to Hua Wan’s report on page 19 “The example consists of a 1000 ft. “Drake” conductor 795 kcmil 26/7 ACSR, and for every 1(C temperature increase, the sag of the line increases by 0.6 in.”. Assuming that the sag increasing rate does not vary too much among the conductors, then a number between 0 to 1.7 is a valid input for the value of the sag increasing rate.
• Conductor Tensile Loading
Suggested default value from the program is 60%.
|Loading |Mean PDT |Deviation PDT |Probability |Risk |
|70 % |61.4691 |15.3185 |0.022398 |0.011242 |
|61% |61.4691 |15.3185 |0.022398 |0.011055 |
|59% |61.4691 |15.3185 |0.022398 |0.011012 |
The effect of 9% increase of the tensile loading resulted 1.69% increase of risk.
Since this is proven to give a very small change of the risk, then the conductor tensile loading can be assumed to be around 60%.
• Loss of strength in fully annealing condition = 56%
(Obtained from IEEE Transactions on Power Delivery, Vol.11, No. 1, January 1996,”Effect of Elevated Temperature Operation on the Tensile Strength of Overhead Conductors”.)
• Parameter of Strength Reduction Curve
According to the default value:
|A |14.8 |
|B |140 |
|C |-7500 |
|D |7.5 |
|Rr |86 |
Rr = the percentage reduction in cross-sectional area during wire drawing.
This data is obtained from the IEEE Transactions on Power Delivery, Vol.11, No. 1, January 1996,”Effect of Elevated Temperature Operation on the Tensile Strength of Overhead Conductors”. Below is the complete table:
|Metal |R (%) |T ((C) | t(h) |A’ |B’ (K) |C’ (K) |D’ |-C’/A’ |
|Aluminum |86.0 |80-200 |0.1-700 |14.8 |140 |7500 |7.5 |507 |
| |92.1 |80-200 |0.1-250 |11.4 |110 |6000 |7.5 |526 |
| |84.8 |75-150 |50-550 |10.3 |170 |5800 |7.5 |563 |
| |91.3 |50-150 |48-2160 |8.98 |130 |5000 |7.5 |557 |
The reason why the first row is chosen for the parameter values is because it has the widest range of temperature and time. Similarly for this project, the same values are chosen.
2 Description of the statistical method
3 Description of the power line
The particular transmission facilities are double circuit and where build in different years. The north and 43 % south section were built or rebuilt in 1993. These portions were rebuilt because of a new load and galloping problems near the south substation. The remaining 57 % of the south section is the original transmission line and is believed to have been built about 1974, when substation 2 was built.
The load is currently fed from two substations. Substation 1 that feed the north line, it comes from a split bus. Bus 1 is feed by the 161/69 kV transformer and feed bus 1, which feeds bus 2. Bus 2 in turn feeds the north line. Substation 2 feeds the south line. A 161/69 kV transformer feeds this substation's 2 69 kV bus.
[pic]
Figure 2 System one-line
2 Description of the process used to determine the associated risk
1 Statistical model
A statistical model was developed to understand the effects of weather on a power system. The statistical model was developed to describe the effects of weather during two different times. The mean and standard deviation was found for wind speed and temperature for night (20:00 to 8:00) and day times (8:00 to 20:00). The method used to get the mean and standard deviation can be found in section XXX. The mean and standard deviation where then inputted into the MATLAB program as probabilistic data.
Our client supplied a model of the power system. The model of the power system gave important information on the safety and liability of the power line. The model was tested using the EPRI IPFLOW software. The current described by the software was then inputted in to the MATLAB code and the program output a Risk value.
In order to understand the statistical nature of the system a model or a system of equations was developed to explain the problem. This model was developed with a few very important assumptions. Without these assumptions the problem and the amount of uncertainty would quickly grow and become unmanageable. The following list will detail the assumptions made.
Assumptions:
1. Wind and weather are statistically independent. This assumption needs further study, but due to the nature of the MATLAB code it could not be changed in time. The code is currently undergoing changes to reflect the statistical dependence.
2. N-1 situation for outages (only one bus or line out per study).
3. Outages in N-1 conditions only to two busses from the critical load.
This assumption could be made from simulation of the power line in IPFLOW showed that outages of further busses did not have an effect on the studied line.
4. Assumed load duration curve.
The load duration curve was not available, so from empirical data the curve is as follows.
[pic]
Figure 3 Load duration curve
5. Annual growth for the factory site is set to 3.5% per year.
The growth rate for this area was from the utility.
6. Equation 1 was used to determine the number of outages per year.
[pic]
Equation 1 Number of outages per year
7. ( = 0.012 per year was used to model the probability of a transformer fault in Equation 2.
[pic]
Equation 2 Probability for transformer fault
8. The following equations hold for only the power line studied. However little change would be needed in order to study another line. The changes involved would be to study the IPFLOW solution to determine the number of N-1 conditions are needed to accurately describe the effects to the line studied. With each N-1 condition a new Ii and Pi would need to be calculated and inserted into the equations.
The equations that follow develop the statistical setting used to determine the risk of operating the power system during each operating condition.
The Total risk was determined by taking an average of both the daytime and nighttime risk. The risk associated with day and night values is determined by Equation 3 this was accomplished in
the MATLAB code.
[pic]
Where Xk is the current System Operating Condition.
Equation 3 Risk equation for day and night values
The Ii values where determined from the IPFLOW solution for each operating condition.
I0= Current of no fault
I1= Current of outage of north power line
I2= Current of outage of south power line
I3= Current of outage of transformer for Substation 1
I4= Current of outage of transformer for Substation 2
The probability (Pi) for each system operating condition was determined by Equation 4.
[pic]
Equation 4 Probability equations
Where P0, P1, P2, P3,and P4 are described as follows.
P0= Probability of no fault
P1= Probability of outage of north power line
P2= Probability of outage of south power line
P3= Probability of outage of transformer for Substation 1
P4= Probability of outage of transformer for Substation 2
The risk of outages for night and day are determined by the following equations.
[pic]
Equation 5 Summation of risk during the day and night
[pic]
Equation 6 Total risk equation
The above statistical model was run for the basecase and each upgrade proposed for the power line. From this data a best solution was determined. This solution was based on the lowest annualized cost. Equation 7 is the decision rule that was used to determine the best solution.
[pic]
Equation 7 Decision rule
Mean and Standard Deviation
The weather data was compiled into a useable Mean and Standard Deviation using Microsoft Excel. The data was received in a text file format. The data was cut down to include only June-August. This was done in order to simplify the project. The data was also cut because the only system power flow study available was for the summer peak. A few assumptions where made in order to get the data needed.
Assumptions:
1. Day hours include 8:00 to 20:00 hours
2. Night hours include 20:00 to 8:00 hours
3. Mean and standard deviation for all Day hours equal and same for night hours.
The mean and standard deviation was found by using a pivot table in Excel, a copy of a pivot table can be found in Appendix XXXXX. The data was then split into day and night sections. Excel was used to determine the mean and standard deviation of each section. These values where then weighted by percentage according to the distance to the power line. One value for standard deviation and mean was found from the weighted value.
The mean and standard deviation was entered in to the probabilistic data of the MATLAB program to determine the night and day values of risk.
3 Determine a recommendation for the power line
4 Identify areas of further study
For this section please see section 9 below.
Evaluation of Project Success
(Including specific items evaluated at the end of the project to determine how successful the project was. Explanation should include whether each milestone was exceeded, fully met, partially met, or not met. )
Summary of Testing
(Including a description of how the end product was tested to ensure that it performs the necessary functions correctly and completely and the results of that testing including any Test Evaluation Reporting Forms. )
Recommendations for continued and/or additional work
Due to limitations of the conductor characteristics data, more research should be performed to obtain a more accurate and exact values. By doing research in the Material Science department or asking questions to the related producer of the wire/conductor, may resulted a real characteristics value.
Final Budgets
(of both money and human effort including explanation and other sources of funding. If the process cost exceeded $100, what additional sources of funds were used? Any equipment or other items that was contributed by some other source should be indicated in the budget section of the report. The original estimates should be included and compared to the design document budgets for both money and human effort. )
1 Budget Chart
[pic]
Final Timeline
(including a Gantt chart spanning two semesters with tasks grouped by project phase; show appropriate "hard" and "soft" milestones; indicate handoffs between tasks/activities. This should be compared to the planned schedule that was presented in the design review document. )
Lessons Learned
Efficient time management: The importance of committing to an agenda
The importance of regular meeting times
Managing four busy schedules
Increased understanding of software tools: MATLAB, IPFLOW, PowerPoint, and various other programs.
Increased understanding of statistical data manipulation.
Increased understanding of real-world costs and inefficiencies.
Evaluating individual strengths and assigning work appropriately.
Teamwork: cooperation, collaboration, joint efforts... "bringing it all together".
Further experience gained with oral presentations.
Group Members: Team MAY 99-15
Corey K. Proctor
321 South 5th Apt. 245
Ames, IA 50010
(515) 233-8452
cproctor@iastate.edu
Chris Dennison
Friley 4525 Meeker
Ames, IA 50012
(515) 296-3783
cdenniso@iastate.edu
Alvina Hendradi
221 N Sheldon Ave Apt. 4
Ames, IA 50010
(515) 292-2443
alvina@iastate.edu
Greg Bahl
3829 Phoenix St
Ames, IA 50010
(515) 268-8890
gregbahl@iastate.edu
Client/ Advising Professor
Dr. James McCalley
1113 Coover
Ames, IA 50011
(515) 294-4844
jdm@iastate.edu
Summary and Conclusions
Risk assessment will provide a great benefit to the planners and system operators. We feel that it will allow the utility companies to fully use the transmission system in the upcoming competitive environment. This project is a complete success because we have full filled all of our objectives as stated in the project plan and have gain great knowledge from work on it .
Appendix
1 Proposed Technical Solution
The proposed solution takes into account how risk it affects the planning of the power delivery system in order to develop better loading strategies for a power line. The flow chart in appendix Error! Reference source not found. will outline the steps that need to be taken in order to reach the final conclusion.
To start this project, research was done by each of our team members to get the data that will be used in the analysis process. The data was obtained from weather stations, library, Internet, and information given by the utility company. The data obtained from our research includes weather data, line characteristics, actual power flow data, and types of reinforcements that will be suitable for the study.
In order to make our data useful for the analysis of the power line; the raw data needs to be processed. By processing, the format of our data should fit into our software-input format. Temperature data was obtained from many weather stations and the data needs to be interpolated about the power line.
The processed data will then be inputted into our software, which includes IPFLOW and MATLAB risk simulator [1]. The result of the calculations done by these two software packages will be combined and produce the risk associated with a condition. Feasibility will also be considered by considering the company’s goals.
In order to determine the optimal solution reinforcements will be considered. The reinforcements possible are:
• Leave power line as is
• Re-conducting
• Re-building or adding new transmission lines
• Increasing power line voltage
• Adding micro-generation
This is not a complete list as more ideas surface in the process of researching. The annualized reinforcement cost and the risk will be added together. The feasibility and the total cost (reinforcement cost plus risk) will be analyzed and compared against other reinforcements with their risk. These changes will be compared using the following criteria:
• Cost of reinforcement
• Risk
• Feasibility
A decision will be made with this information regarding the best course of action in order to reduce the risk to the power line. This study will determine the lowest cost of a feasible alternative for the power line.
2 Progress to Date
1 Research
1 Weather
Historically utilities have used the IEEE standard of 40º C and 2.0 ft/sec wind speed. This is a very conservative scenario in the life of most power lines. Consider the Des Moines mean temperature of 24.8º C for the month of July, this is about 2 whole standard deviations away from the IEEE calculated mean [2].
Finding rigorous statistical weather data has been a real challenge. Most of the data available through standard methods (i.e. library and free web-sites) only provide monthly averages and standard deviation. The statistical method used in determining this data does not provide a rigorous statistical model.
Another problem experienced in researching data has been determining how location affects the usefulness of the data. The location of the data station to the power-line also affects the usefulness of the data (See section 15.2.2.1 for details).
Our group has found a data source. The source of data is from the High Plains Climate Center based in Nebraska. This site has collected data over the high plains area for about ten years. The source is not as rigorous as we would like, but due to the lack of time this source will be used.
2 Transmission Line
Alliant Gas and Electric has provided system, power flow, and transmission line data and suggested a specific transmission line to study.
The transmission lines that Alliant suggested feeds Oakridge substation with two 69 kV transmission lines. A portion of these particular transmission lines is double circuited with a 34.5 kV transmission line that is build to 69 kV standards. These transmission lines are made up of two types of conductors that Table 5 shows.
Table 5. Transmission line characteristics
| |Conductor type |Structure type |Sheild wire |miles |ampacity @75 deg C |
|North Section A |T2 (2-336) ACSR |double circuit |3/8" EHS |0.6 |917 |
|South Sections B |T2 (2-336) ACSR |double circuit |3/8" EHS |2.6 |917 |
| |T2 (2-336) ACSR |single circuit |3/8 EHS |1.3 |917 |
| |636 ACSR |double circuit |5/16" EHS |4.2 |789 |
The transmission line to the south, is restricted by the 4.2 miles of 636 ACSR conductor because of it lower ampacity. This section will be a good section to focus our study on.
3 Load
Oakridge substation supplies power to Cedar River Paper Company in Cedar Rapids Iowa. Cedar River Paper Company is the largest recycling paper mill in the United States. They recycle between 500 and 600 tons of paper waste every day into a large blender called a pulper. There the paper is made into a liquid form and then spread onto a mesh screen conveyer where a series of presses remove excess water with vacuums and rollers. There are two machines that perform this process around the clock. The Cedar River Paper Company is a non-interruptible customer and their load fluctuates little throughout the year.
The south line also feed Kirkwood Community College via a tap. This load is interruptible during peak load conditions.
2 Risk Calculation
According to the definition of risk above, risk is the product of the event’s probability and its resultant impact. The risk calculation will be described below.
1 Weather analyses
For determining the risk of conductor’s sag, air temperature and wind speed level will be the two most significant factors that will influence the conductor. To get this data, the location of the stations that will monitor the transmission line should be determined. The sites should include shielded and heavily forested locations where there would be little or no beneficial effects from meteorological conditions. These sites should be reviewed carefully to insure that the sample data would not be biased or skewed. Some sites should be in areas of moderate to high-sustained wind, and some sites should be on open farmlands and on exposed hilltops [3]. In summary, the site that will be selected should represent the weather around the transmission line accurately. The transmission line should be monitored continuously to provide precise data. By determining the mean and the standard deviation, a plot of the wind velocity vs. time and a plot of temperature vs. time can be obtained. As a reminder, the longer the scope of time of the data, the better the sample data is. After the data is obtained from the station monitor, then we shall proceed to the next step.
2 Conductor characteristics
The conductor’s characteristics necessary to calculate the risk are:
• Factor considering skin effect
• Current through the conductor / ampacity
• The dc resistance of the conductor
• Temperature coefficient of resistance
• Solar absorptivity of surface
• External diameter of the conductor
• Thermal conductivity
3 Determining the conductor’s temperature
The conductor’s temperature during a specified amount of time is determined from several different heat transfer equations. The heating effect on the transmission line is canceled out by the cooling effect. The heat balance equation used in calculating conductor temperature is [4]:
Pj + PM + PS + Pi = Pc + Pr + Pw
Equation 8. Heat balance equation
Pj : Joule heating
PM : magnetic heating
PS : solar heating
PI : corona heating
Pc : convective cooling
Pr : radiative cooling
Pw : evaporative cooling
Knowing all the values of parameter, conductor temperature can be calculated by solving the above equation.
4 Calculate the reduction in tensile strength
The reduction of tensile strength of the conductor was used as the index for the present study that is expressed by:
W = exp(C(ln t – A – BT))
Equation 9. Reduction of tensile strength
W : Reduction of tensile strength in %
T : Conductor Temperature ((C)
t : Time when the conductor is in the condition of temperature T
A,B,C : Constants characteristics of the conductor material
5 Calculate the risk of sagging
Regarding the thermal overload, the thermal risk is the expected impact when the conductor is running on a particular current level. Mathematically, it is the product of:
1. The probability that the conductor temperature may exceed the limit.
2. The resultant impact whenever the conductor is running beyond its temperature limit.
For this case, the event is thermal overload of a conductor and the risk is the expectation of costs that may result. Given the current I, we may compute thermal overload risk as the probability of the temperature being greater than (MDT, times its related impacts:
Equation 10. Risk calculation formula
( :conductor temperature which is influenced by current I together with the ambient
conditions
P[(/I] :the probability density function (pdf) of ( given current I
R[I] :the risk regarding the line loading
3 Power Flow
Alliant's planning department has given us a their power flow case in PSSE version 23. IPFLOW will be used to do the analysis of the power flow through the line in question under different conditions. The transmission lines that we are studying reside in the one-line diagram shown in Figure 4.
[pic]
Figure 4. One line diagram
Currently we are experiencing difficulties with loading the raw data file obtained from Alliant. Initially they gave us version 24 that IPFLOW will not read. Alliant used PSSE to convert to version 23 and also has problems loading because it exceeds 12 thousand buses.
4 MATLAB
A Matlab risk simulator that was written at Iowa State University will be used to perform the risk calculation [1]. The data obtained from our research will be processed and inputted into the Matlab program.
3 Design Flow Chart
[pic]
Figure 5. Design flow chart
References
[1] Wan, Hua. “Thermal Risk Simulator”, Graduate paper.
[2] Weather Post. Washington Post (1993). [One-line]. Available;
[3] D. Douglas, A. Edris, G. Pritchard. “Field Application of a Dyanamic Thermal Circuit Rating Method”, IEEE Trans. on Power Delivery, vol. 12, No. 2, pp. 823-828, 1997.
[4] Y. Mizuno, H. Nakamura, K. Adomah, K. Naito. “Assesment of Thermal Deterioration of Transmission Line Conductor by Probabilistic Method”, IEEE Trans. on Power Delivery, vol. 13, No. 1, pp.266-271, 1998.
-----------------------
[pic]
1
................
................
In order to avoid copyright disputes, this page is only a partial summary.
To fulfill the demand for quickly locating and searching documents.
It is intelligent file search solution for home and business.