2009 IEEE JTCMeeting Atlanta, GA

Slides:



Advertisements
Similar presentations
Chapter 13 Transmission Lines
Advertisements

Sampling: Final and Initial Sample Size Determination
Protection against Lightning Overvoltages Overvoltages due to lightning strokes can be avoided or minimized in practice by (d) shielding the overhead lines.
Point estimation, interval estimation
Copyright © Cengage Learning. All rights reserved. 7 Statistical Intervals Based on a Single Sample.
C82MCP Diploma Statistics School of Psychology University of Nottingham 1 Overview Central Limit Theorem The Normal Distribution The Standardised Normal.
The Physics of Lightning
Calculating Separation Distance and Surge Current
ISE 352: Design of Experiments
Coupling of Electromagnetic Fields to Circuits in a Cavity
All rights reserved Surge Arrester.
Evidence Based Medicine
Continuous Random Variables and Probability Distributions
Probability theory 2 Tron Anders Moger September 13th 2006.
Ch5 Continuous Random Variables
Cross-talk in strip RPCs D. Gonzalez-Diaz, A. Berezutskiy and M. Ciobanu with the collaboration of N. Majumdar, S. Mukhopadhyay, S. Bhattacharya (thanks.
Baseband Demodulation/Detection
FEEE Ensuring Enhanced Education 1 D O : Point of lightning stroke S 0 : Rate of rise at O, kV/µs I 0 : Lightning stroke current, kA X :Distance in which.
OPENING QUESTIONS 1.What key concepts and symbols are pertinent to sampling? 2.How are the sampling distribution, statistical inference, and standard.
12/4/2002 The Ground Conundrum - Class 20 Assignment: Find and research papers on this subject, be prepared to defend research.
FEEE Ensuring Enhanced Education UnUn kV17,52436 BILkV UpUp kV57,279,9117,6 1 1.The simple protection method The maximum distance: Table 1. BIL.
CY1B2 Statistics1 (ii) Poisson distribution The Poisson distribution resembles the binomial distribution if the probability of an accident is very small.
The simulation experiment of induction lightning overvoltage on the distribution line Huang Ying1,2, Zeng Rong1, Yu Zhanqing1, Lu Guojun3, Liu Yu3, Wang.
Copyright © Cengage Learning. All rights reserved. 7 Statistical Intervals Based on a Single Sample.
Presented by: Nguyen Phan Thanh Southern Taiwan University of Science and Technology.
EE 2353 HIGH VOLTAGE ENGINEERING Faculty Name :A.JAIBUNISHA Faculty Code : EE 58 Designation : LECTURER Department : EEE.
Alexandre Piantini University of São Paulo Lightning Transients in Medium- Voltage Power Distribution Lines V Russian Conference on.
Analysis of the lightning performance of power distribution networks in rural and urban areas Alberto Borghetti Dept. of Electrical, Electronic and Information.
Managerial Economics & Decision Sciences Department random variables  density functions  cumulative functions  business analytics II Developed for ©
High Voltage Engineering
Lesson 8: Ideal Transformer Theory and Operation
MATB344 Applied Statistics
A systematic study of cross-talk limitations in RPC timing
ANTENNA THEORY by Constantine A. Balanis Chapter 4.5 – 4.7.2
Different Types of Data
Probability and Statistics
Engineering Probability and Statistics - SE-205 -Chap 4
High Voltage Engineering
Petr Kašpar, Ondřej Santolík, and Ivana Kolmašová
Youngjune, Han Chapter 4 Time Response Youngjune, Han
Hypothesis Testing: One Sample Cases
High Voltage Engineering
Analysis of the Amplitude and Frequencies of the Voltage Magnification Transients in Distribution Networks due to Capacitor Switching Mohamed Saied Electrical.
5. Conductors and dielectrics
Chapter 2 Simple Comparative Experiments
APPROACHES TO QUANTITATIVE DATA ANALYSIS
Microwave Engineering
Sampling rate conversion by a rational factor
Review Data: {2, 5, 6, 8, 5, 6, 4, 3, 2, 1, 4, 9} What is F(5)? 2 4 6
OSHA’s Final Rule: Electric Power Generation, Transmission, and Distribution Electrical Protective Equipment Presented to: RESAP Area Administrators Presented.
Introduction to Probability and Statistics
Discrete Event Simulation - 4
Use of Lightning Data for Electricity Transmission Operations
Microwave Engineering
LIGHTNING AND INSULATIONS COORDINATION
Protection against over voltages
CONTINUOUS RANDOM VARIABLES AND THE NORMAL DISTRIBUTION
Summary (Week 1) Categorical vs. Quantitative Variables
INTERCONNECTED SYSTEM GENERATING CAPACITY RELIABILITY EVALUATION
Parametric Methods Berlin Chen, 2005 References:
NOTICE The authors who don’t receive oral notifications before March 20th are required to present the paper using poster. The size of display board is.
Statistical analysis and its application
CHAPTER – 1.2 UNCERTAINTIES IN MEASUREMENTS.
NOTICE The size of display board is 1m (width)×2.40m(height). A size 0.95m (width)×1.40m(height) of your poster is recommend. In the poster, please edit.
C H A P T E R 11 A.C. Fundamentals.
Random Variables A random variable is a rule that assigns exactly one value to each point in a sample space for an experiment. A random variable can be.
Chapter 5 Continuous Random Variables and Probability Distributions
HIGH VOLTAGE ENGINEERING Presented By P.Sindhu Asst.Prof EEE Dept
CHAPTER – 1.2 UNCERTAINTIES IN MEASUREMENTS.
Presentation transcript:

2009 IEEE JTCMeeting Atlanta, GA IEEE St 1410 Revision Lightning Induced Voltages A. Borghetti C.A. Nucci M. Paolone

Lightning Performance of Distribution Lines To get the ___ we need: Statistical distribution of lightning current parameters (peak, rise time, …) Incidence model Induced-voltage model Statistical approach

1. Statistical Distribution of Lightning Current Amplitude For our purposes the two approaches are equivalent . 5 . 1 IEEE: Ip  20kA Ip = 61.1 kA ln Ip = 1.33 Ip > 20 kA Ip = 33.3 kA ln Ip = 0.605 Cigré: . 5 1 . 2 . 5 . 1 . 2 . 3 . 4 . 5 . 6 . 7 . 8 . 9 . 9 5 . 9 8 . 9 9 . 9 9 . 5 9 9 . 9 9 9 . 9 5 9 9 . 9 9 [kA] 1 . 1 . 1 . 1 .

Lightning Performance of Distribution Lines To get the ___ we need: Statistical distribution of lightning current parameters (peak, rise time, …) Incidence model Induced-voltage model Statistical approach

Lightning Performance of Distribution Lines To get the ___ we need: Statistical distribution of lightning current parameters (peak, rise time, …) Incidence model Induced-voltage model Statistical approach

2. Incidence model IEEE WG lateral attractive distance Direct stroke Nearby stroke rs dl rg h It is just worth mentioning that other expressions exist: Eriksson Rizk Dellera and Garbagnati (LPM)

Lightning Performance of Distribution Lines To get the ___ we need: Statistical distribution of lightning current parameters (peak, rise time, …) Incidence model Induced-voltage model Statistical approach

3. Induced voltage calculation model (present) Rusck simplified formula v return stroke velocity Assumptions: single-conductor infinitely long lines above a perfectly cond. ground step current waveshape h=0,75 Too simple: not adequate in many cases!

3. Induced voltage calculation model (revised) Return stroke model: Modified Transmission Line Exponential Decay (MTLE) or any other type LEMP: Uman and McLain and Cooray-Rubinstein Coupling model: Agrawal extended to the case of lossy ground Note: the shield wire is simply one of the conductors of the n-conductor system 9

Lightning Performance of Distribution Lines To get the ___ we need: Statistical distribution of lightning current parameters (peak, rise time, …) Incidence model Induced-voltage model Statistical approach

4. Statistical approach (present) a) The assumed range of peak values of lightning current Ip at the channel base, from 1 kA to 200 kA, is divided in 200 intervals of 1 kA. b) For each interval i, the probability pi of current peak value Ii to be within interval i is found as the difference between the p for current to be  than the lower limit and the p for current to reach or exceed the higher limit. These ps are obtained by using the formula seen before c) For each interval i, also two distances form the line (in m) are calculated: 1) the minimum distance ymin,i (using the IEEE incidence model) for which lightning of peak current Ii (in kA) will not divert to the line, and 2) the maximum distance ymax,i at which lightning may produce an insulation flashover (using the Rusck formula), i.e. an induced voltage equal to the line critical flashover voltage CFO (in kV), multiplied by a factor equal to 1.5 (to take into account the turn-up in the insulation volt-time curve for short front-time surges). d)

4. Statistical approach (present) a) The assumed range of peak values of lightning current Ip at the channel base, from 1 kA to 200 kA, is divided in 200 intervals of 1 kA. b) For each interval i, the probability pi of current peak value Ii to be within interval i is found as the difference between the p for current to be  than the lower limit and the p for current to reach or exceed the higher limit. These ps are obtained by using the formula seen before c) For each interval i, also two distances form the line (in m) are calculated: 1) the minimum distance ymin,i (using the IEEE incidence model) for which lightning of peak current Ii (in kA) will not divert to the line, and 2) the maximum distance ymax,i at which lightning may produce an insulation flashover (using the Rusck formula), i.e. an induced voltage equal to the line critical flashover voltage CFO (in kV), multiplied by a factor equal to 1.5 (to take into account the turn-up in the insulation volt-time curve for short front-time surges). d)

4. Statistical approach (revised) Inputs: lightning current parameters, return stroke velocity, line and ground data Random generation of events ( Ip tf x y) (e.g. > 10 000) Selection of indirect lightning events by using a lightning incidence model Induced overvoltage calculation using advanced models (and relevant tools) Counting of the n events generating overvoltages greater than the insulation level (e.g. 1.5·CFO) Plot the graph: No. of flashovers/100 km/year vs CFO where No. of flashovers/100 km/year = (n/ntot)·ng·S·100/L (with ng = annual ground flash density, S = striking area, L=line length) correlated

4. Statistical approach (revised) Inputs: lightning current parameters, return stroke velocity, line and ground data Random generation of events ( Ip tf x y) (e.g. > 10 000) Selection of indirect lightning events by using a lightning incidence model Induced overvoltage calculation using advanced models (and relevant tools) Counting of the n events generating overvoltages greater than the insulation level (e.g. 1.5·CFO) Plot the graph: No. of flashovers/100 km/year vs CFO where No. of flashovers/100 km/year = (n/ntot)·ng·S·100/L (with ng = annual ground flash density, S = striking area, L=line length) correlated

4. Statistical approach (revised) The revised method and the new Statistical approach  equivalent as far as an ‘infinitely long line’ is concerned improved in the new version when distribution systems having realistic configurations are analyzed.

 Fig. 5 of rev. 1410 (Modelling details in Appendix B)

Validation – Scale Model, Univ Validation – Scale Model, Univ. of Tokyo and Real Lines, IIE, Cuernavaca From Ishii et al. CIGRE Colloquium SC33, Toronto, 1997 From De La Rosa et al, IEEE Trans. on PWDR, 1988

 Fig. B.3 in Appendix B of rev. 1410 – “Check”

 Fig. B.4 in Appendix B of rev. 1410 – Effect of shield wire

 Fig. B.5 a, b in Appendix B of rev. 1410 – Effect of SA Ideal ground σg = 1 mS/m

LIOV-EMTP computer code G LIOV-line n-port Concept at the basis of the interface The LIOV code calculates: LEMP Coupling The EMTP : calculates the boundary conditions makes available a large library of power components u (x,t) i(x,t) L'dx C'dx x x+dx + - i(x+dx,t) L u (x+dx,t) i (x,t) E (x,h,t)dx s G -u (L,t) (0,t) Link between LIOV and EMTP Data exchange between the LIOV code and the EMTP at the boundary conditions

The LIOV-EMTP code (http://www.liov.ing.unibo.it) i (0,t) LIOV V, I