The MCCB Electrical Clearance and Creepage Distance Measurement Uncertainty Evaluation

Abstract:

The electrical clearance and creepage distance are two key parameters for the design of MCCB (molded case circuit breaker, MCCB), which directly influences the electrical safety performance of MCCB. In this paper, the electrical clearance and creepage distance measurement uncertainty were evaluated through the uncertainty sources analysis, the mathematical model establishment and the components uncertainty calculation, to ensure the accuracy of the measurement results.

• Periodical: Applied Mechanics and Materials (Volume 742)
• Pages: 86-89
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.742.86
• Online since: March 2015
• Authors: Jing Zhang, Feng Wang, Jun Gang Zhou*, Ming Long Hu
• Keywords: Creepage Distance, Electrical Clearance, Measurement Uncertainty Evaluation, Molded Case Circuit Breaker

References

[1] ISO/IEC GUIDE 98-3：2008. Uncertainty of measurement-Part3: Guide to the expression of uncertainty in Measurement[S].

[2] ISO/IEC GUIDE 98-3/SUPP. 1. Uncertainty of measurement-Part3/ Supplement 1: Propagation of distributions using a Monte Carlo method[S].

[3] Weiping Wang, Yongbin Zhao. Research on uncertainty in motor test data fitting [J]. MICROMOTORS，2011，44(8)：92-94 (in Chinese).

[4] An Wang，Duifeng Yan，Dong Cui, et al. Treceability and uncertainties assessment of short -time alternating current RMS measuring system in higher power laboratories[J]. High Voltage Apparatus，2014，50(4)：72-76 (in Chinese).

[5] Paul D. Hale, C.M. Jack Wang. Calculation of pulse parameters and propagation of uncertainty [J]. IEEE Transactions on Instrumentation and Measurement, 2009, 58(3): 639-648.

DOI: 10.1109/tim.2008.2005560

[6] James R. Hockenberry, Bernard C. Lesieutre. Evaluation of uncertainty in dynamic simulation of power system models: the probabilistic collocation method [J]. IEEE Transactions on Power Systems, 2004, 19(3): 1483-1491.

DOI: 10.1109/tpwrs.2004.831689

[7] Ian A. Hiskens, Jassim Alseddiqui. Sensitivity, approximation, and uncertainty in power system dynamic simulation [J]. IEEE Transactions on Power Systems, 2006, 21(4): 1808-1820.

DOI: 10.1109/tpwrs.2006.882460

[8] Yan Wan, Sandip Roy, Bernard Lesieutre. Uncertainty evaluation trough mapping identification in intensive dynamic simulations [J]. IEEE Transaction on Systems, Man and Cybernetics-Part A: Systems and Humans, 2010, 40(5): 1094-1104.

DOI: 10.1109/tsmca.2010.2044172

[9] Jun Li, Yong Zhu. Monto-Carlo simulation applied on measurement uncertainty evaluation [J]. Chinese Journal of Scientific Instrument, 2006, 27(6): 1282-1283 (in Chinese).

[10] Zhiguo Ma, Yitai Ma, Wei Lu. The application of uncertainty analysis theory in thermal efficiency testing for boiler [J]. Proceedings of the CSEE, 2005, 25(3): 125-129 (in Chinese).

[11] Yuan Zhao, Zhijian Shen, Niancheng Zhou, et al. Probabilistic uncertainty analysis of Monte-Carlo simulation for bulk power system reliability evaluation [J]. Proceedings of the CSEE, 2008, 28(28): 61-67 (in Chinese).

[12] Jianhua Chen, Wenchuan Wu, Boming Zhang, et al. Quantitative evaluation of parameter uncertainty in power system dynamic simulation [J]. Power System Technology, 2011, 34(11): 18-23 (in Chinese).