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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 |