Failure Modes and Effects Analysis for Domestic Electric Energy Meter Using In-Service Data

Transcription

1 IOP Conference Series: Earth and Environmental Science PAPER OPEN ACCESS Failure Modes and Effects Analysis for Domestic Electric Energy Meter Using In-Service Data To cite this article: Ning Li et al 08 IOP Conf. Ser.: Earth Environ. Sci View the article online for updates and enhancements. This content was downloaded from IP address on 5/07/08 at 08:5

2 Failure Modes and Effects Analysis for Domestic Electric Energy Meter Using In-Service Data Ning Li, Jincheng Yang, Yongquan Sun, Gang Wang, Jiahai Zhang and Chun Liu Xinjiang Institute of State Grid Electric Power Research, Wulumuqi, 80000, China. Institute of Sensor and Reliability Engineering, Harbin University of Science and Technology, Harbin 50080, Heilongjiang, China. Yantai Dongfang Wisdom Electric Co., Ltd, Yantai 64000, Shandong, China. Corresponding author Abstract. Field operation data for domestic electric energy are valuable for both manufactures and users, from this point of view, the main, failure numbers, installed time, and lifetime were analysed based on in-service data. The result could provide a reference for maintenance and reliability improvements.. Introduction State Grid Corporation of China proposed a large program to establish a strong intelligent grid. the quality and reliability of intelligent electrical meter is not only related to electrical safety of tens of thousands homes, but also have severe impacts on the reliable operation of national intelligent grid. Because field environment of the electric energy meter is complex, considering the effects of temperature and humidity changes, thunder and lightning, power system fluctuation, and electromagnetic interference, the risk of the electric energy meter failure is increased accompany with the long-term operation []. When State Grid bought electric energy through open tender, they identified that the average lifetime of electric energy meter should be not below 0 years under specified working conditions, and also pointed out that the relevant lower limits value of m is h[]. Reliability test is an important method to investigate product reliability, and relevant reliability standards, such as JB/T Reliability Requirements and Reliability Compliance Test for electric energy meter, were published. The limitation is that long testing time, at least 60h, is needed when 4 electric energy were put in a test. Testing time could be shorten by improving testing stress []. Luo Ranran (0) investigated reliability of electric energy, combining accelerated life test, and accelerated degradation testing and reliability prediction based on component stress [4]. Bao Jin (04) pointed out that high accelerated life test was a quick and efficient method to test the potential failure of electric energy meter at the research stage. Physical analysis of failure must be conducted before conducting high accelerated stress test to make sure the consistent of failure mechanism [5]. Accelerated life test generally is carried out on basic components, and we need to ensure the failure mechanism is not to be changed. Statistical analysis on the reliability of electric energy meter based on field failure/lifetime data could reflect reliability level of product. However, little relevant search results could be found, except Li yuxuan (0) analyzed the failure model and Content from this work may be used under the terms of the Creative Commons Attribution.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by Ltd

3 impact of single-phase intelligent electrical meter. Comprehensive analysis of failure records generated in the operation process could help to improve product reliability, but scarce data can be collected and applied to support reliability study for new developed electric energy [6]. This is the motivation of our research to conduct failure statistical analysis based on in-service data.. Description and Identification for In-Service Data of Domestic Electric Energy Meter In-Service data were collected from two provinces in China, one is located in the south of China and denoted as F province, which is characteristic as high temperature, high humidity, and salt mist. The other is in the north of China and denoted as H province, which is famous for its low temperature in winter. The intelligent electric energy were inspected at a certain time-point during its operation, the failure records for fault were collected from Jan. 05 to Dec, and generated from 46 places for F province and from 4 places for H province. 05. The numbers of the were shown in Table. Table. The number of quantity F Province H Province Single phase 4 59 Three phases 79 8 total All the failures were classified into four modes according to failure reasons, which were equipment quality, performance quality, outer factors, and natural hazard. In terms of the intrinsic quality, the equipment quality further divided into several elements for from F and H provinces listed in Table and Table separately. Table. Failure modes of equipment quality for from F province interface Capacitor Failure of Leapyear Wireless module switch corruption 485 interface Solder joint Time conversion Error excess short circuit Infrared data Automatic interface 9 Clock failure 7 Display unit fault Clear 4 foreign matter in the meter Metering chip 0 Clock chip 8 Rosin joint 5 unstart Relay System halted 9 6 Memory loss 4 7 Out of battery 5 8 Power data mutation Crystral Pulse interface Stop go 0 Communication protocol conformance 6 Shunt running 4 Physical abuse Instrument transformer Carrier module Combined error tolerance

4 Table. Failure modes of equipment quality for from H province 485 interface 4 Physical abuse 7 Display unit fault Solder joint short circuit 5 Wireless module corruption 8 Carrier module Metering chip 6 Error excess 9 other Three types of users were identified in the record, which were Industrial users, commocial users, and residential useres. The number of each type were shown in Table 4. It's clear to see that a large number of users was not identified. Table 4. Types and number of users User types single-phase users single-phase users F Province H Province F Province H Province Industrial users commocial users residential useres blank Statistic Analyais of In-Service Data Inspect data were recorded from Jan.05 to Dec.05, and the failure numbers during one month were simplely computed and were presented in Fig.. Fig. of per month

5 In terms of the Entry-into-service time, the inspected meter s intalled date were collected and shown in Table 5. Table 5. intalled time of inspected Time Installed date for three-phase Installed date for single-phase F Province H Province F Province H Province Befor blank 0 After indentification of in the catalogy of equipment quality, the failure number in each failure mode were further given, as listed in Table 6 and Table7 seperately considering two different provinces. 4

6 Table 6. for each from F province threephase singlephase threephase singlephase interface Failure of Leapyear 7 switch interface Time conversion data Automatic Clear 4 9 Clock failure foreign matter in the meter Clock chip unstart System halted Memory loss 7 8 Stop go Out of battery Communication protocol conformance Power data mutation Capacitor Solder joint short circuit Infrared interface Metering chip Physical abuse Relay Crystral Pulse interface Shunt running 40 6 Wireless module corruption Error excess Display unit fault Rosin joint 50 8 Instrument transformer Carrier module Combined error tolerance Table 7. for each from H province three-phase single-phase three-phase single-phase 485 interface 0 6 Error excess 6 Solder joint short Display unit 0 7 circuit fault 6 Carrier Metering chip 8 module Physical abuse other Wireless module corruption 0 5

7 The lifetime of all the inspected are shown in Fig.. It is clear to see that the operation time of most of Single-phase and Three-phase are shorter than 500 days from H province, meanwhile, the operation time for most of are no more than 000 days for F province. 500 Single Phase 0000 Single Phase 000 Three Phase Three Phase 500 H Province F Province Fig. lifetime statistical analysis for 4. Conclusion This paper conduct analysis and statistical analysis based on in-service data. Domestic Electric Energy Meter in-service data are described and identified, some basic information including and main users were provide. For further, certain statistic analysis on failure numbers, installed time, and lifetime were conducted. The main contribution of this paper was to provide basic statistic reference in terms of domestic electric energy field operation data, which could be useful for further analysis on maintenance and reliability improvements. Acknowledgments This research was supported by National Grid Science and Technology project (544JL600) and Natural Science Foundation of Heilongjiang Province (QC06068). References [] Xue Yang, Zhang Penghe, Wang Yatao. Study and exploration on reliability assessment method for smart [J]. Electrical measuement and instrumentation, 06, 5(): [] Yang Hongqi, Liu Shaoqing, Huang Jinyong. Reliability Prediction method of smart meter [J]. Electronic product reliablity and environmental testing, 06, 4():65-7. [] Bao Jin, Zhou Chao, Tian Zhenqi. The application of highly accelerated life test in smart electricity meter reliability study[j]. Electrical measuement and instrumentation, 04, 5(9):7-4. [4] Luo Ranran, Zuo Jia, Tian Chengming. Reliability Assessment Method Research of Electronic electric energy meter [J]. Electrical Measurement & Instrumentation, 0, 50(A):-5. [5] Bao Jin, Zhou Chao, Tian Zhenqi. The application of highly accelerated life test in smart electricity meter reliability study [J]. Electrical measuement and instrumentation, 04, 5(9):7-4. [6] Li yuxuan. Failure modes and effects analysis in single-phase smart electricity [D].North China Electic Power University, 0. 6