Cha na n Sin gh and A lex Sp rintson · Also dep end on supp orting com muni cation syste ms for...

Modeling of Catastrophic Failures in Power Systems

Chanan Singh and Alex Sprintson

Department of Electrical and Computer EngineeringTexas A&M

Chanan Singh and Alex Sprintson Modeling of Catastrophic Failures

Motivation

Recent events such as the Northridge earthquake and HurricaneKatrina have resulted in a significant and long-lasting damage ofdistribution and transmission systems.

Modeling and predicting the performance of these systems in order toprepare for and recover from such events is a top priority.


Distribution and Transmission Systems

Have become more complex and interdependent, both in terms ofphysical components and in terms of management tools;

Are critically dependent on the distribution infrastructure, such aspoles and lines for reliable supply of electric power.

Also depend on supporting communication systems for control,monitoring, and management of power grids.

Dealing with failures of multiple network elements in a particular areaor region due to extreme environmental conditions has so far receivedlittle attention.


Our goal

Develop tools for understanding and improving the reliability andperformance of power systems during catastrophic events such ashurricanes, and earthquakes;

This will include a set of analytical and statistical models for complexpower systems that will allow:

! Probabilistic prediction of the performance of power systems duringsignificant or massive outages due to natural catastrophes;

! E!cient allocation of critical resources such as back-up lines orgeneration for improving survivability and resilience to massive failuresand outages;

! Fast recovery and system restoration after catastrophes.


Impact of natural disasters

Studies of power outages during hurricanes1 have found that mostpower outages during hurricanes are due to

! Physical damage to poles and lines in the distribution system due totrees falling on lines,

! Wind-born debris damaging poles and lines,! Flooding of distribution facilities.

Due to the nature of the damage, power outages during hurricanestend to be geographically uneven outside of the area of highest winds.

1P.J. Vickery, L.A. Twisdale, P. Montpellier, and A.C. Steckley. Hurricane vulnerability and risk analysis of the VINLEC

transmission and distribution system. Technical report, 1996.R.A. Davidson, Liu H., I.K. Sarpong, P. Sparks, and D.V. Rosowsky. Electric Power Distribution System Performance in CarolinaHurricanes. Natural Hazards Review, 4(1):36-45, 2003.


Modeling system reliability

SAIDI (System-Average Interruption Duration Index)

SAIFI (System-Average Interruption Frequency Index)! has been adapted as a measure of system reliability after adverse

events such as large storm

Disadvantage: if we average over the whole year, the failureprobabilities may be diluted because of the low probability ofoccurrence of catastrophic events.

Goal: Develop conditional indices, i.e., the probabilities and extent ofdamage given that an event has occurred.

Statistical methods can be used to estimate the risk of outages overtime or in di"erent places.

! This approach directly estimates the quantity of interest, such as thenumber of outages in di"erent feeders or di"erent geographical areas.


Objective 1

Developing a multi-scale hurricane system damage modelingframework.

! First, develop the structural reliability model for estimating theprobability of main failure modes of poles and lines during huricanes

! Then, the marginal probability of individual trees being blown over willbe estimated as a function of hurricane wind speed and tree height

! Then, the conditional probabilities of impacted power poles breakingand impacted line being pulled o" poles will be estimated based on thedesign strength of poles and the estimated characteristics of the treeimpacts

! This combined model will yield detailed estimates of the number ofpoles broken or down and the number of spans of distribution linedown for an urban area.

! This will enable to forecast outage risk and damage from approachinghurricane


Statistical Model

Max. gust wind speeds

Duration of strong wind

Number of transformers

Number of switches

Number of poles

Miles of overhead line

Fractional soil moisture

Mean annual precipitation

Standardized precipitation index

Land cover/land use

Statistical

Model

Outage and

Damage Risk

Estimates

Hurricane Simulation

!! Max. Gust Wind Speed

!! Duration of Strong Wind


Objective 2

Developing an analytical approach.! Goal: need to account for dependencies due to massive failures and

limitation of resources for repair and restoration.

We will build on the previous work on Markov Cut Set approaches.! Combination of Markov’s method and minimum Cut Set methods

Explore both sequential simulation and sampling of states

We conjecture that sequential approach may be more appropriate asthe sampling again is harder to apply when dependencies are involved.

Employ aggregate response approach based on GMDH method.


Test systems

Use virtual cities Micropolis (pop. 5,000) and Mesopolis (pop150,000)

! Consists of a number of Geographic Information System (GIS) overlaysthat represent the city (i) a realistic road network; (ii) individual housesand commercial buildings with assigned uses and occupancies;

Check TFMR value (Should be 37.5 for 4 homes X 7kW = 28 kW)

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Student Version of MATLAB


Mesopolis

Mesopolis model! Hurricane wind fields are simulated based on pressure transects of past

storms measured by Air Force “Hurricane Hunter”


Potential benefits

Improving system reliability! Once the system is analyzed, we can develop strategies for improving

its reliability.

" For example, for hurricanes the key factor is the tree-trimming plan ofthe utility company.

" Identify the components or subsystems whose improvement will lead tohighest benefit to reliability improvement.

" Develop strategies for crew deployment for restoration and repairs


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