Energy and Power Engineering, 2013, 5, 954-957
doi:10.4236/epe.2013.54B183 Published Online July 2013 (http://www.scirp.org/journal/epe)
Risk Identification based on Hidden Semi-Markov
Model in Smart Distribution Network
Fangyuan Chang, Wanxing Sheng, Tianshu Zhang, Yu Zhang, Xiaohui Song
Power Distribution Department, China Electric Power Research Institute, Beijing, China
Received April, 2013
ABSTRACT
The smart distribution system is the critical part of the smart grid, which also plays an important role in the safe and
reliable operation of the power grid. The self-healing fu n ction of smart distribution network will effectively improve the
security, reliability and efficiency, reduce the system losses, and promote the development of sustainable energy of the
power grid. The risk identification process is the most fundamental and crucial part of risk analysis in the smart distri-
bution network. The risk control strateg ies will carry out on fully recognizing and understanding of the risk events and
the causes. On conditio n that the risk incidents and their reason are iden tified, the corresponding qualitativ e / quantita-
tive risk assessment will be performed based on the influences and ultimately to develop effective control measures.
This paper presents the concept and methodology on the risk identification by means of Hidden Semi-Markov Model
(HSMM) based on the research of the relationship between the operating characteristics/indexes and the risk state,
which provides the theoretical and practical support for the risk assessment and risk control technology.
Keywords: Risk Identification; Hidden Semi-Markov Models; Smart Distribution Network
1. Introduction
With the continued growth of the economy, the social
demand for electricity is at a critical turning point. In the
future, both the scale and the complexity of structure are
increasing for the smart distribution network. Along with
the tremendous benefit in the development of the distri-
bution network, the greater potential risk is also need to
bear.
The distribution network locates at the end of the
power system, which directly connects to the end-users.
Therefore, the grid reliability and the user reliability are
directly related. According to incomplete statistics, 80%
to 95% of the user's ou tage is cau sed by the failure o f the
distribution network [1]. With the rapid development of
the national economy and the improving demand for in-
dustrial and residential load, the challenges of reliability
increases as well. Distribution network failure, which
causes the power outages, will interrupt the users and
brings economic losses. In order to analyze and prevent
the accidents, the risk identification, risk assessment, and
the appropriate risk control measures dominates very
significant meaning nowadays.
In recent years, the study of distribution network risk
analysis focuses on the risk assessment, which based on
the uncertainties from distribution network, qualitatively
or quantitatively gives a comprehensive measure of the
likelihood and severity, while the risk identification is
usually overlooked and affects the validity and accuracy
of the risk assessment. The risk identification process is
to determine the risk factors in the particular system and
define its characteristics. The risk identification is the
foundation and critical part in the risk analysis process.
The risk identification process includes two main aspects:
Firstly, find the sources of risk; secondly, identify the
conditions of risk factors transformed to the accid e nt.
In this paper, a risk identification method based on
HSMM for the smart distribution network is presented.
Firstly we study the mechanism of smart distribution
network and divides the risk types for finding the sources
of risk, then we establish the stochastic process model of
the risk state and the operating characteristics, which
identifies the accident transformed conditions from the
risk factors to the risk accidents, finally, the risk identifi-
cation model based on HSMM for the smart distribution
is proposed, which helps to predict and identify risk ef-
fectively.
2. Literature Research
2.1. Risk Assessment
The aim of operating risk assessment in distribution net-
work is to evaluate the exposure level of disturbance,
which includes both the likelihood and severity of dis-

F. Y. CHANG ET AL. 955
turbance events. The research on the risk alert field
abroad mainly focuses on the safety assessment, reliabil-
ity evaluation and risk assessment. In the risk assessment
of power system, the current research results can be di-
vided into system-level and component-level. The risk
assessment on trolly wires [2] and transformers [3] are in
the component-level, while the system-level case in-
cludes risk assessment on the transient stability analysis
[4], voltage stability analysis [5] and security range [6].
Literature[7] proposed a series functions to represent the
severity of power flow overloading, generatrix low volt-
age, voltage instability, cascading overload, which is
applied in the risk assessment of transmission system,
has not in the field of distribution network.
2.2. Markov Process
In probability theory and statistics, a Markov process, is
a stochastic process satisfying a certain property [8].A
hidden semi-Markov model (HSMM) is a statistical
model with the same structure as a hidden Markov model
except that the unobservable process is semi-Markov
rather than Markov. This means that the probability of
there being a change in the hidden state depends on the
amount of time that has elapsed since entry into the cur-
rent state. This is in contrast to hidden Markov models
where there is a constant probability of changing state
given survival in the state up to that time [9].
This feature of the Markov process is usually applied
to the analysis of state probability and system reliability
after a self-healing power system’s restoration. However,
the basic conditions for applying the Markov process to
get the reliability index are: the lifecycle distribution of
the components of the system and the repair time distri-
bution after the failure occur, and the other relevant dis-
tribution is exponentially distributed, and all the random
variables are independent of each other.
At present, lots of resear ch has been done base on this
assumption. Literature [10] proposed a component out-
age model based on the Markov process, which combines
the duration of the frequency to perform the reliability
analysis for small system. Literature [11] presented a
transformer reliability assessment model based on Mar-
kov process. However, in practice, such as the com- po-
nent reliability assessment, if the lifecycle and repair
time distribution are not exponential distribution, the
process is far from the Markov process, and will reduce
the accuracy of the simulation results. The Semi-Markov
process does not require the exponential distribution as-
sumption on the transfer function and widely used to the
power plant reliability assessment [12], the uninterrupti-
ble power supply (USP) reliability assessment [13] and
the protective relay reliability assessment [14]. In par-
ticular, literature [15] proposed equipment failure predic-
tion method based on HSMM to predict the probability
of each state according to the partial observation predi-
lection system.
3. Risk Identification based on HSMM
If the time is ordered by 12 in data set ... n tt t


12
,,...,n ttt , the distribution function of ()
nn
X
tx

is
as follows:
21nX NNN
XXXXX X
FF


1
(1)
while ()
ii
X
tx

, 1, 2,,1in


This random process of this nature is defined a Mar-
kov process which follows the principles: the random
process at the time of 0 is already known, the proc-
ess at 0 is only relevant to the process at while irrev-
erent to the process before 0, which is also called non-
memory property or no-follow-up effect, in simple, the
future development of given state process is independent
of probability rules in the history.
tt
t
t
This paper presented a Hidden Semi-Markov Model
(HSMM) for the risk prediction in smart distribution
system. The HSMM described two stochastic processes;
one is the semi-Markov process with randomness to de-
scribe the transferring relationship between the states,
while another is to describe the stochastic relationship of
states and observation values.
Assuming the state data set is

12,
,
L
qq q, and the
corresponding observation data set is
11
11
(,), (,)
L
ll L
oo oo





 ,
the state t observation data set is 11
L
lL
,
and the state duration is 1tt
th
t q(,oo )
ll


. The Figure 1 shows the
process of the hidden semi-Markov model.
4. Scheme of Risk Identification based on
HSMM
Aiming at the limitation of current research of risk in the
distribution network, this paper propose the concept of
risk identification, reveals the cause of risk which is the
1
o
2
o
1
l
o
...
1
q
11
1l
o


12
1l
o

 1
l
o ...
3
q
11l
o

1
2l
o

2
l
o ...
2
q
...
Figure 1. The process of the hidden semi-Markov model.

F. Y. CHANG ET AL.
956
relationship of the operating characteristics/indexes be-
fore and after the risk’s occurrence, on the other hand,
the state transferring principles of component / system is
presented to provide reference basis for risk prevention
and control. The specific research scheme is shown in
Figure 2 and the steps ar e as follows:
 Step 1: study the risk diversity in smart distribution
network according to the actual situation, analysis the
possible risks in smart distribution network;
 Step 2: According to the type of risk, identify the
state of component / system, establish the state space.
The state space is defined as normal state, overload state,
overvoltage state, low-voltage state and failure state
shown in Figure 3:
 Step 3: Select a real distribution network, monitor
the frequency of cases occurs within a certain period, and
the abnormal information /operating characteristics
/index before the incidents;
 Step 4: obtain the state transition probability matrix
by means of neural network training method;
 Step 5: According to the results of Step 2 and Step 4,
establish the state transition model based on the Markov
proce ss risk;
 Step 6: eliminate the abnormal information / oper-
ating characteristics /index irrelevant to the incidents
using the theoretical analysis, mathematical statistics
method, and then apply the multi-source information
fusion to get the relevancy of risk and operating charac-
teristics/indexes observed ;
 Step 7: According to the results of step 6, establish
the stochastic process model of risk state and values ob-
rved;
 Step 8: combine the results of Step 5 and Step 7,
establish the risk prediction model based on HSMM of
smart distribution network;
 Step 9: verify the model above by the dynamic si-
mulation test, and validate the model according to the
test results.
The risk identification method introduced is different
from the traditional research in the operation indexes,
according to the steps above, with the data mining and
multi-dimensional method, the large number of history/
real-time monitoring/simulation information in the smart
distribution network are applied to analyze the relation-
ship between the risks and operation characteristics of
the distribution network, propose the risk identification
model base on HSMM and predict th e possible risks that
the system may face according to the current component/
system states.
5. Future Works and Conclusions
In this paper, on the base of the establishment of the risk
state transition probability mo del and th e random process
model of the risk status and monitoring values, the
mechanism of distribution network risk is revealed
theoretically, the smart distribution network risk
identification model is proposed as well, providing the
new ideas for risk warning from the nature and
mechanism of risk. The research achievement can be
Figure 2. The specific research scheme on the risk identification based on HSMM.

F. Y. CHANG ET AL. 957
Figure 3. The proposed state transfer model.
directly used to the theoretical basis of smart distribution
network risk warning. For the practical application, the
risk prediction analysis and causes mining before the
incident provides technical support for smart distribution
network from passive defense to active defense, which
promotes the security of electricity supply, improve the
reliability, and reduces the impact of the grid accident
and hazards, and has great practical significance on
operation, planning and designing of the power system
and the development of society as well.
6. Acknowledgements
This work is supported by Active defense Technology
based on Multi-source Information Fusion of Smart Dis-
tribution Network of State Gird Cooperation of China
(SGCC) and Risk Alert Technology based on Multi-
source Information Fusion in Smart Distribution Net-
work (Project No. 51177152) of National Science Foun-
dation of China.
REFERENCES
[1] R. Billinton and P. Wang, “Reliability Network Equiva-
lent Approach to Distribution System Reliability Evalua-
tion,” IEEE Proceedings: Generation, Transmission and
Distribution, Vol. 145, No. 2, 1998, pp. 149-153.
[2] R. X. Liu, J. H. Zhang and D. Wu, “Research on Static
Security Index of Distribution Network Based on Risk
Theory,” Power system Protection and Control, Vol.39,
No.15, 2011, pp. 89-95.
[3] H. Wan, J. D. Mccalley and V. Vittal, “Increasing Ther-
mal Rating by Risk Analysis,” IEEE Trans. on Power
Systems, Vol. 14, No. 3, 1999, pp. 815-828.
doi:10.1109/59.780891
[4] W. H. Fu, J. D. Mccalley and V. Vittal, “Risk Assessment
for Transformer Loading,” IEEE Transactions on Power
Systems, Vol. 16, No. 3, 2001, pp. 346-353.
doi:10.1109/59.932267
[5] M. Ni, J. D. Mccalley and V. Vittal, “Software Imple-
mentation of Online Risk-Based Security Assessment,”
IEEE Trans on Power Systems, Vol. 18, No. 3, 2003, pp.
1165-11728. doi:10.1109/TPWRS.2003.814909
[6] H. Wan, J. D. Mccalley and V. Vittal, “Risk based Volt-
age Security Assessment,” IEEE Transactions on Power
Systems, Vol. 15, No. 4, 2000, pp. 1247-1254.
doi:10.1109/59.898097
[7] J. D. Mccalley, A. A. Fouad and V. Vittal, “A Risk-Based
Security Index for Determining Operating Limits in Sta-
bility-Limited Electric Power Systems,” IEEE Transac-
tions on Power Systems, Vol. 12, No. 3, 1997, pp.
1210-1219.doi:10.1109/59.630463
[8] Wikipedia, “Markov Process,” 2013.
http://en.wikipedia.org/wiki/Markov_process
[9] S. Z. Yu, “Hidden Semi-Markov Models,” Artificial In-
telligence, Vol. 174, No. 2, 2009, pp. 215-243.
doi:10.1016/j.artint.2009.11.011
[10] W. Y. Li, “Risk Assessment of Power Systems: Models,
Methods, and Applications,” 1st Edition, John Wiley &
Sons Ltd., Chichester, 2004. doi:10.1002/0471707724
[11] R. Jin, Z. N. Xiao and J. Gong, “Markov Model for Reli-
ability Assessment of Power Transformers,” High Volt-
age Engineering, Vol. 36, No. 2, 2010, pp. 322-328.
[12] M. Perman, A. Senegacnik and M. Tuma, “Semi-Markov
models with an application to power-plant reliability
analysis,” IEEE Transactions on Power Reliability, Vol.
46, No. 4, 1997, pp. 526-532. doi:10.1109/24.693787
[13] Y. Liang, R. M. Fricks and K. S. Trivedi, “Application of
semi-Markov process and CTMC to evaluation of UPS
system availability,” Proceedings of 2002 Annual Reli-
ability and Maintainability Symposium, Seattle, 28-31
January 2002, pp. 584-591.
doi:10.1109/RAMS.2002.981706
[14] L. J. Wang, G. Wang and B. Li, “Reliability Modeling of
a Protection System Based on the Semi-Markov Process,”
Automation of Electric Power Systems, Vol. 34, No. 18,
2010, pp. 6-10.
[15] X. Jun and X. Y. Xiao, “A Method of Reliability Cost
Assessment in Distribution System Based on
Semi-Markov Process,” Relay, Vol. 34, No. 12, 2006, pp.
57-62.