Failure Probability Analysis of Sensitive Equipment Due to Voltage Sags Using Fuzzy-Random Assessment Method

The impact of voltage sags on equipment is usually described by equipment failure probability, and it is in general very difficult to assess and predict because of uncertainties with both the nature of voltage sags at the power supply side and the voltage tolerance level of equipment. By defining the equipment failure event caused by voltage sags as a fuzzy-random event, a fuzzy-random assessment model incorporating those uncertainties is developed. The model is able to convert the probability problem of a fuzzy-random variable to that of a common random variable with the use of -cut set and it is thus valuable in theoretical analysis and engineering application. The validity of the developed model has been verified by Monte Carlo stochastic simulation using personal computers as the test equipment.

[1]  Yu Ge Dong,et al.  Simulation of Fuzzy reliability indexes , 2003 .

[2]  Chang-Hyun Park,et al.  The Influence of Generator Scheduling and Time-Varying Fault Rates on Voltage Sag Prediction , 2008, IEEE Transactions on Power Delivery.

[3]  Hermann W. Dommel,et al.  A fuzzy logic application to represent load sensitivity to voltage sags , 1998, 8th International Conference on Harmonics and Quality of Power. Proceedings (Cat. No.98EX227).

[4]  E. Jaynes.  Information Theory and Statistical Mechanics , 1957 .

[5]  J.V. Milanovic,et al.  Severity Indices for Assessment of Equipment Sensitivity to Voltage Sags and Short Interruptions , 2007, 2007 IEEE Power Engineering Society General Meeting.

[6]  Math Bollen.  Method of critical distances for stochastic assessment of voltage sags , 1998 .

[7]  J.A. Martinez,et al.  Voltage sag stochastic prediction using an electromagnetic transients program , 2004, IEEE Transactions on Power Delivery.

[8]  Arup Kumar Goswami,et al.  Stochastic Estimation of Balanced and Unbalanced Voltage Sags in Large System , 2008, 2008 First International Conference on Emerging Trends in Engineering and Technology.

[9]  J.V. Milanovic,et al.  Methodology for assessment of financial losses due to voltage sags and short interruptions , 2007, 2007 9th International Conference on Electrical Power Quality and Utilisation.

[10]  Yian-Kui Liu,et al.  Fuzzy Random Variables: A Scalar Expected Value Operator , 2003, Fuzzy Optim. Decis. Mak..

[11]  Chan-Nan Lu,et al.  Estimation of Sensitive Equipment Disruptions Due to Voltage Sags , 2007, 2007 IEEE Power Engineering Society General Meeting.

[12]  Huibert Kwakernaak,et al.  Fuzzy random variables - I. definitions and theorems , 1978, Inf. Sci..

[13]  J.V. Milanovic,et al.  Probabilistic assessment of financial losses due to interruptions and voltage sags - part II: practical implementation , 2006, IEEE Transactions on Power Delivery.

[14]  Baoding Liu,et al.  Inequalities and Convergence Concepts of Fuzzy and Rough Variables , 2003, Fuzzy Optim. Decis. Mak..

[15]  Chan-Nan Lu,et al.  A Voltage Sag Index Considering Compatibility Between Equipment and Supply , 2007, IEEE Transactions on Power Delivery.

[16]  Math Bollen,et al.  Understanding Power Quality Problems: Voltage Sags and Interruptions , 1999 .

[17]  E. Liu,et al.  Assessment of interruption cost to high tech industry in Taiwan , 2001, 2001 IEEE/PES Transmission and Distribution Conference and Exposition. Developing New Perspectives (Cat. No.01CH37294).

[18]  Honggeng Yang,et al.  Stochastic estimation trip frequency of sensitive equipment due to voltage sag , 2008, APCCAS 2008 - 2008 IEEE Asia Pacific Conference on Circuits and Systems.

[19]  Chang-Hyun Park,et al.  Stochastic Estimation of Voltage Sags in a Large Meshed Network , 2007, IEEE Transactions on Power Delivery.

[20]  J.V. Milanovic,et al.  Probabilistic assessment of financial losses due to interruptions and voltage sags-part I: the methodology , 2006, IEEE Transactions on Power Delivery.

[21]  Huibert Kwakernaak,et al.  Fuzzy random variables--II. Algorithms and examples for the discrete case , 1979, Inf. Sci..