Root Cause Localization on
Power Networks
Zhen Chen, ECEE, Arizona State University
Joint work with Kai Zhu and Lei Ying
Locate Root Cause on Power Networks
• Questions we are interested:
• How to locate the root cause on power networks?
• Which information can be used to locate the root cause
of cascading failures on power networks?
• Objective: develop high-performance algorithms
to find the root cause of cascading failures on
power networks.
Similarities to Information Source Detection
• Cascading failures on power networks:
• The failure of some transmission lines leads to the outage
of other transmission lines in the power network.
• Similar to an information diffusion process.
Related work:
Information
detection
Can
we use source
existing
information source
(Shah&Zaman’10’11’12; Luo,Tay&Leng’12; Prakash,
detection algorithms to locate the root
Vrekeen&Faloubsos’12; Zhu&Ying’12’13;…)
cause of cascading failures?
Propagation of Cascading Failures
• The spread of cascading failures:
• Ohm’s and Kirchhoff’s laws.
• Information does not necessarily diffuse according to
physical topology of the power network.
• The power network topology does not capture the
dependency of the transmission lines.
Related work: Cascading failures in power networks
(Chen,Throp&Dobson’05; Bernstein,Bienstock,
Hay,etc’2014; Xiao&Yeh’11;…)
Can not directly apply information
source detection algorithms to power
networks!
Correlation Network ( Zhang,Liu,Yao,etc’13)
• It models the influence of one transmission line to
the others.
• DPij : the change of power flow on line j due to
the outage of line
i .
• The correlation network: a weighted complete
graph with each node representing a
transmission line.
1
Measure the
wij =
DPij
DPji
correlation of failures
+ n
n
between two
å DPkj å DPik
transmission lines.
k=1
k=1
Diffusion on the Correlation Network
a
e
b
d
c
4-bus power system
Correlation network
Diffusion on the Correlation Network
a
e
b
d
c
Greedy Algorithm
a
Step 1: calculate the normalized average
infection time:
0.6
0.5
0.9
e
1
3
b
2
0.5
0.8
d
0.7
0.5
0.6
0.5
c
4
t avg = 2.5
Greedy Algorithm
a
Step 2: Include a node into the initial
infection spreading tree.
0.75
Step 3: Add the node with infection
time observed to the infection
0.9 spreading tree
0.6
0.5
e
1
3
2
0.5
0.8
b
5.25
d
0.7
0.5
0.5
0.5
c
4
Step 4: Add other
Step
5: Calculate
infected
nodes to the
the cost.
infection spreading
tree. Assign infection
time.
td = tc + mcd = 5.25
ta = tb - mab = 0.75
Experiments
Power systems:
• IEEE 300-bus system
• Electricity transmission network of Great Britain (2224
buses)
Cascading failures traces:
1. Remove a transmission line randomly
2. Calculate the new power flow allocation
3. Remove overloaded transmission lines
4. Repeat Step 2 and Step 3 until no new overloaded
transmission lines.
Experiments
CR: Cost-based ranking
TR: Tree-based ranking
Application to Other Networks
The greedy algorithm can be used to
detect source on weighted graphs.
The network of air traffic volume between US airports (Dianati’15)
13
Conclusions
• Combined the correlation network with
information source detection
• Developed a cost-based approach and proposed
a greedy algorithm to locate the root cause
• Evaluated performance on IEEE 300-bus power
network and Great Britain power network.
Thank you!
Q&A