Risk Identification based on Hidden Semi-Markov Model in Smart Distribution Network

doi:10.4236/epe.2013.54B183

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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

Email: [email protected]

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





,,...,n

ttt , the distribution function of ()



as follows:

21nX NNN

XXXXX X





1

(1)

while ()



, 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

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,

qq q, and the

corresponding observation data set is

(,), (,)

ll L

oo oo











 ,

the state t observation data set is 11

,

and the state duration is 1tt

t q(,oo

)



. 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

...









...

11l



...

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.

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