Energy and Power Engineering, 2013, 5, 557-560
doi:10.4236/epe.2013.54B106 Published Online July 2013 (http://www.scirp.org/journal/epe)
Characteri stic Analysis o f DS18B20 Temperature Sensor
in the High-voltage Transmission Lines’ Dynamic
Capacity Increase
Song Nie1, Yang-chun Cheng1,2, Yuan Dai3
1Beijing Key Laboratory of High Voltage & EMC, North China Electric Power University, Beijing, China
2State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,
North China Electric Power University, Beijing, China
3Electric Power Research Institute of Guangdong Power Grid Corporation, Guangzhou, China
Email: niesong0104@sina.com, chych@ncepu.edu.cn, daiy1997@163.com
Received February, 2013
ABSTRACT
Dynamic capacity increase in high voltage electric power transmission line is currently the most economical method for
solving electric power transmission bottleneck nowadays. DS18B20 temperature sensor is applied to the dynamic ca-
pacity increase of high voltage transmission lines to measure the conductor temperature and ambient temperature. The
paper is focused on the experiment of DS18B20 both in the laboratory and outside. From the result of the lab tempera-
ture measurement data analysis, using 4 DS18B20’s is the most suitable plan, considering both accuracy and economi-
cal efficiency. In the experiment outside, we get four groups of conductor (uncharged) temperature and four groups of
ambient temperature. The data proved that DS18B20 has good stability, and small measurement error. It is suitable for
measuring the temperature of conductor and ambient in dynamic capacity increase, and helpful to improve the accuracy
of the calculation of capacity increasing.
Keywords: DS18B20 Temperature Sensor; Measurement Error; Dynamic Capacity Increase; Data Analysis
1. Introduction
In recent years, with the development of China's sus-
tained and rapid, power consumption will also continue
to increase. In economically developed areas, due to the
partial slow speed of grid construction, the bottleneck
problem of the power system transmission capacity of
transmission lines has become increasingly prominent.
However, because of the limited line level of thermal
stability, transient stability level and the level of dynamic
stability, about a quarter of the transmission line trans-
mission capacity was significantly lower than the level of
foreign. In order to improve the transmission capacity of
transmission lines, the comment measures are UHV
technology, flexible AC transmission technology, the
series compensation technology, dynamic reactive power
compensation with the rod back and compact transmis-
sion, heat-resistant wires of large cross-section and other
technologies,etc.[1]. Among various researches, increas-
ing the heat capacity of transmission line has been care-
fully studied by most electrical departments and has been
proven to be an effective and the most economical way
to enhance the current capacity of transmission lines.
The current capacity of transmission line is depend on
its heat-balanced equation. The line’s current capacity is
related to various factors like sunlight intensity, wind
velocity and direction, characteristics of line ( such as
diameter, aging and AC resistance , etc.) and ambient
temperature. The maximum current capacity of transmis-
sion line is changing with the line’s temperature. How-
ever, the maximum current capacity can only be calcu-
lated by the conductor temperature model. It can’t be
monitored online. It is important to get conductor tem-
perature exactly by online monitoring. The error of con-
ductor temperature is mainly from two aspects. One is
measurement error, sensor error and sensor acquisition
device error; the other is the algorithm error, reduce from
the regularity of distribution of the sample to. Therefore,
this paper is focused on analyzing the validation and pre-
cision of DS18B20 temperature sensor which we decided
to use on measuring temperature, by doing experiments
in laboratory and outside.
2. Temperature Sensor Experiments
2.1. Laboratory Experiments
1) Experimental equipments
DS18B20 temperature sensor: measuring range-55
Copyright © 2013 SciRes. EPE
S. NIE ET AL.
558
-125 , accuracy±0.5; two standard mercury ther-
mometer: measuring range-5-150, accuracy±0.1,
it is the standard value; GDWJS-250 alternating wet heat
test box: temperature range -40-150,temperature
volatility±0.5, temperature uniformity±2.[2]
2) Experimental methods
In the laboratory, eight DS18B20‘s and a mercury
thermometer are put into an alternating wet heat test box,
and the data collection equipments are put outside the
box. According to the measuring range of each tempera-
ture sensor, We choose 0,20,30,40 ,60 and
80 as test points. When it reaches to the test tempera-
ture and stipulated time, we record the temperature of
mercury thermometer and data collection equipments in
each test points for four or five times, every minutes. We
use SPSS software to process data, and do the Levene-
test and t-test to the sample[3].
2.2. Field Experiments
1) Experimental equipments
We Beijing Key Laboratory of High Voltage & EMC
designed the temperature measurement device. As it is
showed in Figure 1, there are four sensors in the device.
Sensor 1 is for measuring the temperature of transmis-
sion line. Sensor 2 is for measuring the reference wire.
Sensor 3 and 4 are for ambient temperature.
2) Experimental methods
In the experiment of measuring real transmission line,
we hang the four temperature measuring devices on wire
between two towers, which span is 187.8 meters, Figure
2 shows how the devices are hanged on the wire, we re-
cord a data every 5 minutes, testing for 24 hours. We use
SPSS software and Excel to process data, and do Time
series analysis and Paired-samples T-test to the sample.
3. Analysis on Experimental Data of
Temperature Sensor
3.1. Analysis on Experimental Data of
Laboratory Ex pe r i me n t s
Because of different measuring points and the differences
among temperature measurements, it is difficult to analy-
sis overall. We unify the experiment data at first. Let the
measured values minus the standard to get the measure-
ment error and then analyze the measurement error. The
obtained experimental data were histogram, interval es-
timation, normal distribution hypotheses test. From Fig-
ure 3, the sample of single sensor of measurement error
is not entirely belonging to the normal distribution.
Figure 1. Temperature measurement device.
Figure 2. Device suspension sche me s.
Figure 3. DS18B20’s data histograms.
Copyright © 2013 SciRes. EPE
S. NIE ET AL. 559
Table 1 [3] shows that the columns asymptotically
significant (both sides) values greater than 0.05, based on
hypothesis testing knowledge to know, the original as-
sumption that the sample (each sensor measurement de-
viation) from a normally distributed population.
Single DS18B20 has no significant impact on measur-
ing error, and multiple DS18B20’s 95% upper and lower
limits of the confidence interval of the difference are
significantly lower than single DS18B20. So that multi-
ple sensor measurement value of mean values can sig-
nificantly reduce error. Using four DS18B20’s is the
most suitable plan, Considering both accuracy and eco-
nomical efficiency.
3.2. Analysis on Experimental Data of Field
Experiments
According to the curve of real transmission line measur-
ing data [4], when DS18B20 enter the stable operation,
data are changing smoothly, have no mutation, and hard
real time. According to weather conditions, from the
trend of the curve can be seen, the sensor mounted just
needs a stable period of time, in order to accurately re-
flect the conductor and the ambient temperature; data for
temperature measurement devices 0E is selected 11:13 as
the start value, 0A is selected 11:16, 08 is selected 10:58,
and 0C is selected 10:55s the start value. The standard of
selecting the start value is the inflection point of the
temperature started to climb. Each group extracts 253
data according to time sequence. Figure 4 is the curve of
real transmission line measuring data.
We do paired sample T-test on the difference between
sensor 3 and 4 of these four measured devices. The sig-
nificance probability of measurement device 0E, 0A and
08 are greater than 0.05, so that there are no significant
differences between paired sample data.[3] However
measurement device 0C is different from others, the rea-
son maybe anthropic factor. The difference between the
sensor 3, 4 were less than 0.2°C, in line with project re-
quirements error which is lower than 0.3℃.[4]
4. Conclusions
We could make conclusions from the analyzing above.
Single DS18B20 has no significant impact on measuring
error, and multiple DS18B20's 95% upper and lower
limits of the confidence interval of the difference are
significantly lower than single DS18B20. So that multi-
ple sensor measurement value of mean values can sig-
nificantly reduce error. Using four DS18B20’s is the
most suitable plan, considering both accuracy and eco-
nomical efficiency.
Table 1. DS18B20 statistics.
NO. 0 1 2 3 4 5 6 7
Mean value -0.3192 -0.3817 -0.2983 -0.4358 -0.3108 -0.2192 -0.2942 -0.1983
Standard deviation 0.41444 0.44767 0.37261 0.39113 0.30974 0.49959 0.42007 0.34016
Lower limit -0.4942 -0.5707 -0.4557 -0.601 -0.4416 -0.4301 -0.4715 -0.342
Mean 95%
confidence
interval Upper limit -0.1442 -0.1926 -0.141 -0.2707 -0.18 -0.0082 -0.1168 -0.0547
Asymptotically significant
(both sides) 0.667 0.367 0.353 0.478 0.131 0.155 0.916 0.502
Figure 4. Curve of real transmission line measuring data.
Copyright © 2013 SciRes. EPE
S. NIE ET AL.
560
According to the curve of real transmission line meas-
uring data, when DS18B20 enter the stable operation,
data are changing smoothly, have no mutation, and hard
real time. The significance probability of measurement
device 0E, 0A and 08 are greater than 0.05, so that there
are no significant differences between paired sample data.
However measurement device 0C is different from others,
the reason maybe anthropic factor.
According to the analysis above, DS18B20 is suitable
for measuring the temperature of conductor and ambient
in dynamic capacity increase, and helpful to improve the
accuracy of the calculation of capacity-increasing.
5. Acknowledgement
This research is supported by Guangdong Power Grid
Corporation, key project “Research on forecasting and
monitoring method of real-time dynamic capacity expan-
sion in high-voltage transmission line”.
REFERENCES
[1] L. J. Ren, “Dynamic Overhead Transmission Line Rating
Based on Tension Measurement,” Doctor Degree thesis,
Shanghai, China, 2008.
[2] A. Q. Song, Y. C. Cheng and Y. Dai, “Characteristics of
Temperature Sensor Using in the Dynamic Capacity of
the High Voltage Line,” The Chinese Society of Electrical
Engineering Professional Committee of the high voltage,
Jinan, China, 2011.
[3] J. M. Su, “SPSS12.0 for Windows Application and De-
velopment Guide,” Publishing House of Electronics In-
dustry, Beijing, 2004.
[4] M. K. Du, “Excel the data processing and preliminary
statistics,” The Electronic Industry Press, Beijing, 2011.
Copyright © 2013 SciRes. EPE