May99-15 Senior Design Project
| Statistical Analysis of Power System Faults
May99-15 Senior Design Project |
|
Note: The Design Review document is a refinement of the Project Plan. It has the same sections and requirements; however, because the Design Review is prepared several weeks after the Project Plan, the Design Review should contain much more detail--demonstrating that the team now has a much better understanding of the project. Ideally, the Design Review document will contain a detailed description and schedule for what will be done, when, and by whom during the second semester.
Contents of this Page:
Abstract
Problem Statement
and Background
What is Risk?
Proposed Technical Solution
Design Objectives
Progress to Date
-Research
-Risk Calculation
-Power Flow
-Matlab
Future Work
Budgets Chart
Proposed Schedule
References
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.
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.
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.
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.
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.
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 8.1 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:
Develop a report that will outline the following items
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 6.2.1 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
The transmission lines that we will study feeds 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 1 shows.
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.
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:
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 : 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 : 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 3. Risk calculation formula
Theata :conductor temperature which
is influenced by current I together with the ambient
conditions
P[Theata/I] :the probability density function (pdf) of ? given current
I
R[I] :the
risk regarding the line loading
IPFLOW will be used to do the analysis of the power flow through the line in question under different conditions.
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.
Our group is also considering a JAVA interface that would allow an interface to the MATLAB code via the Internet. This would allow utilities that do not have MATLAB the chance to use the risk simulation software in their power system planning.
[1] Wan, Hua. “Thermal Risk Simulator”, Graduate paper.
[2] Weather Post. Washington Post (1993). [One-line]. Available; http://www.weatherpost.com/historcial.html
[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.
© Copyright Iowa State University 1998
