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CHAPTER 4

DISTRIBUTION SYSTEM RELIABILITY

EVALUATION SERVICES

4.1 INTRODUCTION

Reliability assessment is the most important factor in designing and

planning of distribution systems that should operate in an economic manner

with minimal interruption of customer loads. Over the past decades

distribution systems have received considerably less attention regarding

reliability modelling and evaluation than devoted to generating systems. The

main reasons are that the generating systems are very capital intensive and

generation inadequacy will have greater impact on both the society and its

environment. A distribution system is relatively cheap and outages have a

much localized effect. On the other hand, the analysis of customer failure

statistics shows that distribution systems make the greatest individual

contribution to the unavailability of customer supply.

The goal of a power system is to supply electricity to its customers

in an economical and reliable manner. It is important to plan and maintain

reliable power systems because cost of interruptions and power outages can

have severe economic impact on the utility and its customers. In the

distribution systems, most of the outages or failures would result in direct

impact on the customers. A customer connected to an unreliable distribution

system could receive poor energy supply even though the generation and

transmission systems are highly reliable. This fact clearly illustrates the

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importance and necessity of conducting reliability evaluation in the area of

distribution systems. Attention should be focused to enhance the distribution

system reliability on par with the generation and transmission systems to

achieve greater customer satisfaction without additional operational cost. The

power utilities require a variety of networked interconnections and

telecommunication technologies to monitor and control power system

operations especially distribution system operations. Reliability assessment

of a distribution system is concerned with the performance at the customer

load points.

4.2 ADEQUACY INDICES IN DISTRIBUTION SYSTEM

RELIABILITY EVALUATION

In the classical concept of reliability, the basic parameters used to

evaluate the reliability of a distribution system can be categorized as load

point indices and system reliability indices. The basic load point indices are

the load point failure rate (ë), the average outage time (r) and average annual

unavailability or outage (U). The set of system reliability indices includes

interruption indices and energy oriented indices. As most power system

interruptions are due to breakdowns in distribution systems, it is essential for

a distribution company to have proper planning tools to assess and improve its

reliability and performance.

The load point and system reliability indices are normally

determined on annual basis. Because of the stochastic nature of a power

system, the indices for any particular year are random values and are

functions of the component failure rates, repair times and restoration times

within the year. A complete representation of these indices involves

knowledge of the underlying probability distributions. It is relatively easy to

compute the average values as the associated analytical techniques are highly

developed for both radial and meshed distribution systems.

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4.2.1 System Reliability Indices

The three primary load point indices introduced above are very

important from a customer standpoint. The system performance can also be

assessed on an overall distribution system including system reliability indices.

These indices reflect the adequacy of overall system supply and indicate the

system behaviour and response. The system basic reliability indices are

defined as follows:

System Average Interruption Frequency Index

The index represents the average number of sustained interruptions

experienced by a customer in a unit time (generally 1 year). The definition of

service area is flexible in the sense that the number of customers and the

interruptions experienced by them changes with the definition of the enclosed

area. For instance, a feeder SAIFI indicates the average number of

interruptions a customer serviced by the particular feeder would experience in

a year. Similarly SAIFI reported for a substation or a distribution system

encloses the total customers in the service area. The system average

interruption frequency index is given in Equation (4.1)

ServedCustomersofNumberTotal

onsInterruptiCustomerofNumberTotalSAIFI (4.1)

In order to calculate the index, data of individual sustained

interruptions in a year are required. For each of these interruptions the number

of customers affected comprises the customer interruptions for the particular

outage. The denominator is the total number of customers in the service area

under consideration. Thus, the SAIFI is represented by the Equation (4.2):

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i

T

NSAIFI

N (4.2)

where Ni is the number of interrupted customers for each interruption event

during the reporting period and NT is the total number of customers served in

the area. The method to improve on the SAIFI levels of a system is by

reducing the number of sustained interruptions that occur. This can be

achieved through a proper maintenance cycle for each of the component in the

system and through the use of automation and improved protective equipment

that sense faults and attempt to clear the same before it turns into a permanent

outage.

System Average Interruption Duration Index

The index indicates the average time a customer has an interruption

during a time cycle (1 year). It is usually specified in customer minutes or

customer hours of interruption / year. SAIDI (system average interruption

duration index) is the average interruption duration per customer served as

given in Equation (4.3). It is determined by dividing the sum of all customer

interruption durations during a year by the number of customers served.

Customer Interruption DurationsSAIDI

Total Number of Customers Served (4.3)

SAIDI can be improved by reducing the number of interruptions or

the duration of the interruptions. For rural areas and long distance feeders,

the time taken to reach the outage spot is comparatively larger than the actual

time of repair. In such cases, SAIDI can be reduced by using optimal crew

dispatch techniques like having a decentralized scheme to crew movement

wherein crew are available at multiple locations throughout the system and

the ones nearest the fault fix the problem. Use of automation is another

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method to achieve improvements in SAIDI. For a given service area, system

average interruption duration index SAIDI is represented as given in

Equation (4.4).

T

ii

N

NrSAIDI (4.4)

where Ni is the number of interrupted customers for each interruption event

during the reporting period, NT is the total number of customers served in the

area and ri is restoration time for each interruption event. The number of

customers affected and the time it took for the restoration for each

interruption event are the parameters required to estimate the system average

interruption duration index. The restoration time includes, the time taken to

notice an outage, the time taken to locate and reach the location and the time

to repair the fault.

Customer Average Interruption Duration Index

Customer Average Interruption Duration Index (CAIDI) is the

average interruption duration for those customers interrupted during a year. It

is determined by dividing the sum of all customer interruption durations by

the number of customers experiencing one or more interruptions over a one

year period.

The index is the ratio of SAIDI to SAIFI as given in Equations (4.5)

and (4.6) respectively. It represents the average time taken to restore service

to the customers when a sustained interruption occurs.

Customer Interruption DurationsCAIDI

Total Number of Customer Interruptions (4.5)

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CAIDI can be improved by reducing the length of interruptions by

faster crew response time and repair times. The value of CAIDI for a given

service area is given as:

i i

i

r N SAIDICAIDI

N SAIFI (4.6)

where Ni is the number of interrupted customers for each interruption event

during the reporting period and ri is restoration time for each interruption

event.

The performance indices such as SAIFI, SAIDI and CAIDI express

interruption statistics in terms of system customers. A customer here can be

an individual, firm, or organization who purchases electric services at one

location under one rate classification, contract or schedule. If service is

supplied to a customer at more than one location, each location shall be

counted as a separate customer.

Customer Total Average Interruption Duration Index

The customer total average interruption duration Index (CTAIDI)

represents the average time the customers facing interruptions spent without

power. Unlike CAIDI, only the customers that actually had interruptions are

included in the computation of this index as defined in Equation (4.7).

Customer Interruption DurationsCTAIDI

Total Number of Customers Interrupted (4.7)

The difference arises in the way an interrupted customer is

accounted. For the computation of CTAIDI, every customer facing an

interruption is counted only once, irrespective of the number of interruptions

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seen for the reporting period. The CAIDI uses “total number of customer

interruptions” in its denominator while the CTAIDI has “total number of

customers interrupted”. Thus, CTAIDI can be represented as given in

Equation (4.8).

N

ii

C

NrCTAIDI (4.8)

where, CN is the total number of customers facing an interruption during the

reporting period.

Customer Average Interruption Frequency Index

Customer average interruption frequency index (CAIFI) gives the

average frequency of sustained interruptions for those customers experiencing

interruptions as given in Equation (4.9).

N

i

C

N

dInterrupteCustomersofNumberTotal

onsInterruptiCustomerofNumberTotalCAIFI (4.9)

SAIFI is the average frequency of interruptions experienced by a

customer and includes even the customers that haven’t experienced an outage,

while CAIFI’s computation involves only the inclusion of customers that have

experienced atleast one interruption. Also, the customer is counted once no

matter how many times they have been interrupted.

Average Service Availability Index

The average service availability index (ASAI) gives the fraction of

time the customer has power during the reporting time. Higher ASAI values

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reflect higher levels of reliability. Equation (4.10) is used to calculate the

value of ASAI for a given service area:

8760N

Nr8760N

demandservicehoursCustomers

tyavailabiliservicehoursCustomerASAI

T

iiT

(4.10)

Average System Interruption Frequency Index

Unlike SAIFI, ASIFI is an index that uses the load interrupted

rather than the number of customers interrupted. Thus, it is a measure of the

expected number of times load is interrupted during the specified interval of

time. Thus ASIFI for a system is be computed as given in Equation (4.11).

T

i

L

L

ServedKVAConnectedTotal

dInterrupteLoadofKVAconnectedTotalASIFI

(4.11)

where, Li is the load interrupted due to each outage while LT is the total load

connected to the system under consideration. ASIFI becomes equal to SAIFI

when the load distributed to each customer is equal.

Average System Interruption Duration Index

Similar to ASIFI, ASIDI is load based and computes the average

duration for which load is interrupted when a sustained outage occurs. Thus

ASIDI is computed as given in Equation (4.12).

T

ii

L

Lr

ServedKVAConnectedTotal

dInterrupteLoadofDurationKVAConnectedASIDI

(4.12)

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ASIDI becomes equal to SAIDI when the load is homogeneously

distributed amongst the customers in a system. The units of ASIDI are hours

or minutes for which load is interrupted.

4.2.2 Energy Oriented Indices

The most important parameters required in the evaluation of load

and energy oriented indices is the average load at each load point busbar. The

average load La is given in Equations (4.13) and (4.14).

La = Lp f (4.13)

where Lp = peak load demand

f = load factor

The average load La is also defined as follows:

t

E

interestofperiod

interestofperiodindemandedenergytotalL d

a (4.14)

Energy not Supplied Index

Total energy not supplied by the system is estimated using

Equation (4.15)

ia(i) ULENS (4.15)

where, La(i) and Ui respectively are the average connected load and the

average annual outage time at load point i.

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Average Energy not Supplied Index

The average energy not supplied by the system is estimated using

Equation (4.16).

i

ia(i)

N

UL

servedcustomersofnumbertotal

suppliednotenergytotalAENS (4.16)

where, La(i) and Ui respectively are the average connected load and the

average annual outage time at load point i and Ni is the number of customers

at load point i.

The system wide indices are used to describe the average

performance of the system. For customers requiring high levels of reliability,

using the system wide indices to generalize the quality of service may not be

appropriate. In such cases the use of customer level indices are used to

compute the reliability of the service. Generally, three indices used for the

same are:

Customer interruption frequency which is the frequency of

interruptions seen by the customer during the year

Customer outage duration which is the average time the

customer spends in the interrupted state

Customer service availability is the fraction of the year the

customer has power supply.

Apart from the indices defined so far certain utilities also define

metrics for prioritizing customers based on importance. These indices can be

used not only to assess the past performance of a distribution system but also

to predict the future system performance.

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4.3 RELIABILITY EVALUATION OF RADIAL DISTRIBUTION

FEEDER TEST SYSTEM

A service oriented model is proposed for predictive reliability

assessment of radial distribution feeder system using Failure Modes and

Effects Analysis (FMEA). The radial feeder can be represented as an

interconnection of components whose failure characteristics can be used to

predict the system behaviour. The predictive methods are commonly used to

predict the distribution system reliability. The test system, which is shown

in Figure 4.1, consists of a switching station and a typical radial distribution

feeder arrangement (Yeddanapudi et al 2005). The test feeder consists of five

laterals L1, L2, L3, L4, and L5 with various number of customers at load

points C1, C2, C3 and C4 with capacities as shown in Figure 4.1. Fuses and

substation breakers are the protective devices and are shown as F1, F2, F3 and

M1, M2, M3 respectively. It is assumed that the switches are 100% reliable

and the average switching time of each of the switches (S1, S2, and S3) is 0.5

hours. The failure rate information by assuming a single mode of failure of

each segment in the radial feeder test system is given in Table 4.1. It is

assumed that the time taken to repair the fault in the circuit breaker M1 is 4

hours and hence customers at the load points C1, C2, C3 and C4 experience

an outage of 4 hours.

In reliability analysis, huge amount of data are being exchanged

among interconnected systems. The exchange of power system data using

XML offers trouble-free integration with the Web and Intranet / Internet

applications. In order to compute the power system reliability indices,

utilities often use large databases where outage histories are maintained often

termed as Outage Management Systems (OMS). These databases include

details of the location, date / time of the failure event, the component involved

and the number of customers interrupted due to each outage. Also recorded

are the entities like the time taken to restore service to the affected customers,

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the time to repair / replace the failed component and more importantly the

cause of the interruption. Events where service was restored in stages due to

switching actions or reconfiguration of the system are also recorded. Put

together, the outage database provides information on each and every event

that happens on a distribution system. The history of outages in power system

is needed for calculating the distribution system reliability indices. The

outage data consist of number of customers, components failure data, repair

data and forced outage data. SOAP communication with attachment model is

proposed to predict the analytical reliability indices of the radial feeder

distribution test system.

Figure 4.1 Radial Distribution Feeder System

F2

C1

C4

C2

C3

900 Customers1800 KVA

550 Customers1100 KVA

125 Customers

300 KVA

450 Customers825 KVA

NC - Normally ClosedBRK - Breaker

L3

M3

S1 (NC)

L1

L4

L5

F3

L2

S2 (NC)

M2

M1

Substation

BRK

F1

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Table 4.1 Failure Rate Information of Radial Feeder Test System

Failure

Component

Failure Rate ( )

Frequency/yr

MTTR

(hours)

FOR

hrs/yr

M1 0.10 4 0.40

M2 0.25 4 0.12

M3 0.30 4 0.15

L1 0.20 3 0.60

L2 0.40 3 0.00

L3 0.10 1 0.05

L4 0.10 2 0.05

L5 0.00 0 0.00

The XML data representation for distribution system reliability

analysis is as follows:

<distribution_system_reliablity_data>

<general>

<no_of_customers>Total No. of Customers at Loading Point

</no_of_customers >

<clr type="KVA"> Customer Load Rating </clr>

<id>Customer System Data </id>

</general>

<loadpoint_data>

<loadpoint name="C1">

<failure_device_name device="M1">

<failure_rate>Failure Rate of the Device</failure_rate>

<mttr type="hour">Mean Time To Repair</mttr>

<for>Forced Outage Rate</for>

</failure_device_name>

</loadpoint >

</loadpoint_data>

</distribution_system_reliability_data>

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The reliability data pertaining to the Radial Feeder Test System is

represented using XML annotations and the corresponding XML document

and its XML schema are generated. The data required for FMEA analysis

are represented in a class ‘LoadPointData’, which encapsulates the load point,

protective device identification, failure rate of each component, mean time to

repair and forced outage rate. The class ‘LoadPointData’ is described as

follows:

In order to represent each member of the above class as an XML

element in an XML document, the class ‘LoadPointData’ has to be annotated

with @XmlRootElement annotation. As it is aimed to estimate the

distribution system reliability indices, an instance to the ‘LoadPointData’

class is encapsulated with other members such as number of customers and

class LoadPointData {

String loadPoint;

String failureModeDevice;

double failure_rate;

int mttr;

double forcedOutageRate;

public LoadPointData(String lp, String fmd, double fr,

int mr, double outage_rate)

{

loadPoint=lp;

failureModeDevice=fmd;

failure_rate=fr;

mttr=mr;

forcedOutageRate=outage_rate;

}

}

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customer load rating in a class named as ‘DistributionSystemReliability’, thus

making the members of the ‘LoadPointData’ class as child elements in the

XML document. The @XmlRootElement annotation associated with the

‘DistributionSystemReliability’ class has two elements, name and namespace

and described as follows:

The code segment to generate the formatted XML document using

the above annotation is as follows:

@XmlRootElement(name ="reliability",

namespace ="http://www.reliability.com/radialfeeder")

public class DistributionSystemReliability {

int noOfCustomers;

int customerLoadRating;

LoadPointData loadPointData;

DistributionSystemReliability(int noOfCustomers,

int customerLoadRating,

LoadPointData lpd)

{

this.noOfCustomers=noOfCustomers;

this.customerLoadRating=customerLoadRating;

loadPointData = new LoadPointData();

loadPointData.loadPoint= lpd.loadPoint;

loadPointData.failureModeDevice= lpd.failureModeDevice;

loadPointData.failure_rate= lpd.failure_rate;

loadPointData.mttr= lpd.mttr;

loadPointData.forcedOutageRate= lpd.forcedOutageRate;

}

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The formatted XML document stored in the ‘dsr.xml’ file, which

represents the distribution system reliability estimation data for the radial

feeder test system, is given below.

The necessary XML schema documents are generated using

‘schemagen’ utility for the ‘DistributionSystemReliability’ class. The XML

schema definition to map the ‘DistributionSystemReliability’ class is as

follows:

<?xml version="1.0" encoding="UTF-8"?>

<ns2:reliability xmlns:ns2="http://www.reliability.com/radialfeeder">

<noOfCustomers>900</noOfCustomers>

<customerLoadRating>1800</customerLoadRating>

<loadPointData>

<loadPoint>C1</loadPoint>

<failureModeDevice>M1</failureModeDevice>

<failure_rate>0.1</failure_rate>

<forcedOutageRate>0.4</forcedOutageRate>

<mttr>4</mttr>

</loadPointData>

</ns2:reliability>

JAXBContext context =

JAXBContext.newInstance(DistributionSystemReliability.class);

Marshaller marshaller = context.createMarshaller();

marshaller.setProperty(Marshaller.JAXB_FORMATTED_OUTPUT, true);

OutputStream os=new FileOutputStream("dsr.xml");

marshaller.marshal(new DistributionSystemReliability(900, 1800, new

LoadPointData("C1", "M1", 0.10, 4, 0.40)), os);

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Subsequently, the above schema is used to generate the XML

document to represent the reliability data for the radial feeder distribution

system and can be added directly to the SOAP envelope as request payload,

while a request is initiated to invoke the distribution system reliability

evaluation services.

<?xml version="1.0" encoding="UTF-8"?>

<xs:schema version="1.0"

xmlns:xs="http://www.w3.org/2001/XMLSchema">

<xs:complexType name="reliability">

<xs:sequence>

<xs:element name="loadPointData" type="LoadPointData"/>

<xs:element name="noOfCustomers" type="xs:int"/>

<xs:element name="customerLoadRating" type="xs:int"/>

</xs:sequence>

</xs:complexType>

<xs:complexType name="loadPointData">

<xs:sequence>

<xs:element name="customerId" type="xs:string"/>

<xs:element name="failureModeDevice" type="xs:string"/>

<xs:element name="failure_rate" type="xs:double"/>

<xs:element name="forcedOutageRate" type="xs:double"/>

<xs:element name="mttr" type="xs:int"/>

</xs:sequence>

</xs:complexType>

</xs:schema>

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4.4 SOAP COMMUNICATION MODEL FOR RELIABILITY

ESTIMATION OF ELECTRIC POWER DISTRIBUTION

SYSTEMS

The major components of the proposed SOAP communication

model are Distribution System Reliability (DSR) Service Provider and the

Service Requester. The main aim is to represent the estimation of distribution

system reliability indices as a service and to exchange the required data using

XML over SOAP. The SOAP communication model for distribution system

reliability analysis is shown in Figure 4.2.

Figure 4.2 SOAP Communication Model for Distribution System

Reliability Analysis

The DSR services are categorized into estimation of Interruption

Indices such as System Average Interruption Frequency Index (SAIFI), the

System Average Interruption Duration Index (SAIDI), the Customer Average

Interruption Frequency Index (CAIFI), the Customer Average Interruption

Duration Index (CAIDI), Customer Total Average Interruption Duration

DSR Service Provider

Service Requesters

Reliability service invocation

System Reliability Indices

Interfaces

(SAIFI service) (CTAIDI service)

(SAIDI service) (ASAI service)

(CAIDI service) (ASIFI service)

(CAIFI service) (ASIDI service)

Distribution System Reliability

service description using WSDL

Power System

Clients

SOAP with Attachment

API

Run Time Environment

Exchange of reliability data

Using XML over SOAP

Transport protocol (HTTP)

Binding Binding

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Index (CTAIDI), the Average Service Availability Index (ASAI), the

Average System Interruption Frequency Index (ASIFI), Average System

Interruption Duration Index (ASIDI) and estimation of energy indices such as

the Energy Not Supplied (ENS) and the Average Energy Not Supplied

(AENS).

The DSR Service provider offers the above services and describes

the services to its clients using Web Services Description Language (WSDL).

Since the described services are Distribution System Reliability Services, the

WSDL file is named as DSRS-WSDL, which is in XML format. A service is

a reusable function that can be invoked by another component through a well

defined interface. Services are loosely coupled, that is, they hide their

implementation details and only expose their interfaces. In this manner, the

power system client need not be aware of any underlying technology or the

programming paradigm which the service is using. The loose coupling

between services allows for a quicker response to changes than the existing

conventional applications for power system operations. This results in a

much faster adoption to the need of power system industries.

4.4.1 Implementation of the Proposed Model

A radial feeder test system is considered for reliability evaluation

using the proposed model. Synchronous SOAP request-response model is

proposed in which the request payload encapsulates service invocation details

and reliability data in XML format and the SOAP response messages

represent system reliability indices, which are also in document style format.

The various stages involved in the implementation of the proposed SOAP

communication model for distribution system reliability analysis are XML

data representation, reliability service interface, service implementation,

service configuration, description, service mapping, service binding and

service invocation.

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In the proposed interoperable SOAP communication model for

power distribution systems reliability evaluation, the reliability data is

included as attachment within the SOAP message. The power system

reliability services are designed to enhance the interoperability based on

standards such as HTTP, XML and SOAP. The calling sequence of

reliability evaluation services along with required data is configured as a

SOAP message. The proposed SOAP communication model in the form of

document style will provide a common computational environment for

interaction between various power system clients and reliability service

providers. Analytical techniques for distribution system reliability assessment

are effectively used to evaluate the wide range of system reliability indices.

4.4.2 Reliability Service Interfaces

The service interface provides the contract between the power

system client and reliability service provider. The service interface is

responsible for all of the implementation details needed to perform the

communication between the clients and service provider. In this proposed

model, eight interfaces are created for the representation of various reliability

indices estimation services such as System Average Interruption Frequency

Index estimation, System Average Interruption Duration Index estimation,

Customer Average Interruption Duration Index estimation etc. Decoupling

the service interface from the service implementation enables the system to

deploy two codebases on separate tiers, potentially increasing the deployment

flexibility. The service interface for computing System Average Interruption

Frequency Index is as follows:

package reliability;

public interface saifi extends Remote {

public String estimateSAIFI() throws RemoteException;

}

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The service interface encapsulates all aspects of the network

protocol used for communication between clients and service provider. All

other interfaces are described similarly. The implementation of the service is

modified without affecting the user who consumes that service. The services

related to computing of power distribution system reliability indices are

configured as follows:

The above XML document contains the information and details

about the deployed distribution system reliability services (SAIFI, SAIDI

etc.,) and metadata such as their service name (ReliabilityService) and

namespace (Reliability).

4.4.3 Reliability Service Descriptors

A service description is document-based that defines or references

the information needed to use, deploy, manage and control the reliability

services. The service descriptor includes the information and behaviour

models associated with a service to define the service interface. The purpose

of service descriptor is to facilitate interaction and visibility, particularly

<service name="ReliabilityService"

targetNamespace="urn:Reliability"

typeNamespace="urn:Reliability"

packageName="reliability">

<interface name="reliability.saifi"/>

<interface name="reliability.saidi"/>

<interface name="reliability.caifi"/>

<interface name="reliability.caidi"/>

..........................

<interface name="reliability.asidi"/>

</service>

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when the providers and participants are in different ownership domains. The

RSDs describe how the service provider and client communicate with each

other. It also provides the information about the data type, binding and

address information for invoking the services from the service provider. The

SAIFI service is described as follows:

<definitions name=”ReliabilityService”

targetNamespace="urn:Reliability">

<types>

<schema targetNamespace="urn:Reliability">

<doubleType name="estimateSAIFI">

</doubleType>

<element name="estimateSAIFIResponse" type ="string"/>

</types>

<message name="SAIFI_estimateSAIFI">

<portType name="saifi">

<operation name="estimateSAIFI">

<input message="tns: SAIFI_estimateSAIFI"/>

<output message="tns:

saifi_estimateSAIFIResponse"/></operation>

</portType>

<binding name="saifiBinding" type="tns: saifi">

<soap:binding transport="http://schemas.xmlsoap.org/soap/http"

style="document"/><operation name="estimateSAIFI">

</binding>

<service name="ReliabilityService">

<port name="saifiPort" binding="tns: saifiBinding">

<soap:address location=" http://loacalhost:8080/reliability"/>

</service>

</definitions>

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The name of the service is defined as ‘ReliabilityService’ and

declares the namespace as ‘Reliability’. The Java based data types used in the

interfaces are converted into JAX-RPC based data types that would be used to

describe the distribution system reliability data within the Web service. The

abstract definition of the operation “estimateSAIFI” that uses a concrete

protocol SOAP to represent messages is described in the Web service

descriptor file. The SOAP message represents a logical definition of the data

being transmitted between the client and the service provider. The service is

bound to an endpoint address named as “saifiPort” through which the clients

can invoke the reliability estimation services. In this proposed SOAP

communication model, all the reliability service objects are translated and

mapped into XML for interoperability in a heterogeneous environment. The

mapping file describes how the Java objects like package, type, port, method,

and endpoint are mapped into XML and vice versa. While invoking the

reliability service, the method call and its parameters are mapped into XML

and sent through SOAP communication protocol. When received at the client

or server end, the request / response parameters must be mapped from XML

to their proper types or objects to make interoperability inherently.

4.4.4 SOAP Handlers for Reliability Estimation Service Messages

SOAP Handlers are effectively utilized in order to maintain power

system operations in a pre-defined sequence. In the proposed SOAP

communication model, the SOAP request sent by the client is received by the

service provider as a SOAP MessageContext. A handler is introduced to

extract the request payload from the MessageContext and to parse it to extract

the actual data, which is required for reliability estimation services. A handler

chain is used to control the sequence of operations to obtain the desired

results. Separate handlers are introduced to carry out the intended task in

sequence to obtain the desired reliability index. In order to estimate the

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customer average interruption duration index (CAIDI), the proposed model

has to invoke the XMLised power system reliability data generation Web

service, the service associated with the estimation of system average

interruption frequency index (SAIFI) and followed by invoking the service

associated with system average interruption duration index (SAIDI). The

sequence of operations is effectively controlled using <handler> elements

within a handler chain XML document. The handler chain used in the

proposed model to estimate CAIDI is given as follows:

<?xml version="1.0" encoding="UTF-8"?>

<handler-chains xmlns="http://java.sun.com/xml/ns/javaee">

<handler-chain>

<handler>

<handler-name>XMLised Data Generation</handler-name>

<handler-class>power.XMLisePSDataImpl</hander-class>

</handler>

<handler>

<handler-name>Estimation of SAIFI</handler-name>

<handler-class>reliability.SaifiImpl</handler-class>

</handler>

…………………

<handler>

<handler-name>Estimation of CAIDI</handler-name>

<handler-class>reliability.CaidiImpl</handler-class>

</handler>

</handler-chain>

</handler-chains>

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The handler chain has been associated with the CAIDI estimation

service using @HandlerChain annotation with the “file” element initialized

with “handler-chain.xml”. The CAIDI estimation service calls

handleMessage() method, which will receive the request payload as SOAP

MessageContext. All service handlers listed in sequence within the “handler-

chain.xml” file are effectively managed by the handleMessage() method and

the SOAP response for the CAIDI estimation service is sent to the requested

client. Each handler is associated with handleFault() method to report the

errors and exceptions to the client while executing the services in sequence.

This model provides an automation of sequencing the services and it is of

great importance while handling complex power system processes where

many operations with variable response times run simultaneously in which the

response of one operation will become the request for subsequent operations.

4.4.5 SOAP Request and Response for Reliability Services

The reliability service descriptor represents information about the

interface and semantics of how to invoke a reliability service. The power

system client has to establish the SOAP connection using

SOAPConnectionFactory for invoking the desired services. The SOAP

message is created using MessageFactory reference to an endpoint interface.

The method call and its parameters are mapped into XML and sent through

SOAP communication protocol. When received at the client or server end,

the request / response parameters must be mapped from XML to their proper

types or objects. The following code segment delineates how the SAIFI

service is being invoked by the power system clients.

130

The SOAP request with the attachment of power distribution

system reliability data for invoking the reliability estimation service is shown

below:

String destination ="http:/192.168.1.1:1078/powersystem/ReliabilityService";

SOAPConnectionFactory soapConnFactory =

SOAPConnectionFactory.newInstance();

SOAPConnection connection = soapConnFactory.createConnection();

SOAPMessage reply = connection.call(message, destination);

<SOAP-ENV:Envelope

…………….. <!-- method invocation request -->

</SOAP-ENV:Body></SOAP-ENV:Envelope>

<SOAP:Attachment>

<distribution_system_reliability_data>

<general>

<cust>900</cust>

<clr type="kva">1800</clr>

</general>

<loadpoint_data>

<loadpoint_customer one="c1">

<failuremode_device_name brk="m1">

<frd>0.10</frd>

<mttr type="hour">4</mttr>

<for>0.40</for>

</failuremode_device_name>

<failuremode_device_name brk="m2">

<frd>0.25</frd>

<mttr type="hour">0.50</mttr>

<for>0.125</for>

</failuremode_device_name>

</loadpoint_customer>

</loadpoint_data>

</distribution_system_reliability_data>

</SOAP:Attachment>

131

The SOAP response for SAIFI and SAIDI services obtained using the

proposed model is shown below:

In the proposed SOAP communication model, the various

reliability services can be invoked by the clients without any limitations. It

has been proven that the proposed model is capable of representing the power

system problems in heterogeneous environments. The data required for

distribution system reliability evaluation are attached in the SOAP body. The

provider sends an entire document rather than sending a set of parameters to

the clients. SOAP message handlers are used to intercept both SOAP request

and response messages and for sequencing the operations. The proposed

SOAP communication model for reliability analysis makes the distribution

<env:envelope xmlns:env="http://schemas.xmlsoap.org/soap/envelope/"

xmlns:enc="http://schemas.xmlsoap.org/soap/encoding/"

xmlns:ns0="urn:reliability"

xmlns:xsd="http://www.w3.org/2001/xmlschema"

xmlns:xsi="http://www.w3.org/2001/xmlschema-instance">

<env:body>

<ns:computeresponse>

<result>

<distribution_system_evaluation>

<dsrindices>

<saifi>1.04999</saifi>

<saidi>2.02257</saidi>

………………………..

</dsrindices>

</distribution _system _evaluation>

</result>

</ns:computeresponse>

</env:body>

</env:envelope>

132

system reliability service provider and its clients to exist in a loosely coupled

environment.

4.5 SECURE AND RELIABLE SYNCHRONIZED EVENT

NOTIFICATION SYSTEM

Over decades, computer network and distributed information

processing technologies have seen profound growth. In the meantime,

deregulation is starting and will be spreading in the electric power industries.

The deregulation of power sectors is bringing about major changes in the

utilities, with new players involved in the network and increased quality and

profitability requirements. Consequently, customers request low cost power

supply and high quality services from the power suppliers. Corresponding to

these requirements caused by deregulation, Seki et al (2002) have developed a

prototype power systems information delivering system using Internet

technologies supplying useful power systems information such as power

failures. The purpose of using Internet technologies is to reduce the cost and

time to develop applications and integrate them with existing systems. The

prototype system consists of an application layer based on a Web browser, an

information model layer which reflects the power systems behaviour and a

distributed object management layer using CORBA and OS / Network layer.

Due to deregulation policies, an electric power company needs to compete

with other power companies and hence more efficient operations are required

and various services are to be provided to the customers. There will be stiff

competition on cost and services associated with supplying power. It is

necessary to provide timely power systems operational information to the

customer.

Originally the software was designed and built in the EMS /

SCADA systems, which was dependent on the hardware since the data had

been received from the sensors. Later the EMS / SCADA for power systems

133

have been developed using Internet Protocol (IP) for the Wide Area Network

(WAN) connected between the control centres and substations. This system

provides information on power failures to customers through the Internet, in

addition to the operators of the electric power sector. Moreover, the system is

able to supervise the status and failure of both the power transmission systems

and the power distribution systems simultaneously. This system detects

power failures using the operation status and activated status of the switch and the

relay. The power systems information delivering system has the following

features:

Power failure monitoring using Web browser

Reproducing at any time the power systems status in the past

The integration of the information on power systems

configuration and status for the power transmission and the

power distribution network

The integration of power systems information and geographical

map information

The provision of statistical or historical information for offline

use, such as calculated blackout time or number of customers

affected

Kivikko et al (2003) presented a Web application for viewing

customer-specific interruption data. The application is integrated with

commercial Distribution Management System (DMS) and it uses the same

database as DMS system. The authors have clearly described about the

content of interruption database and the construction of interruption sector.

They have proposed a Web application for customer interruption monitoring.

The application presents the existing outage data measured and saved by the

SCADA and DMS systems on the browser. They have also explored the

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possibilities of new technologies, which make the development of Web and

mobile applications. Microsoft Visual Studio .Net programming tool and C#

programming language are used in the implementation of the Web

application. They have described a logic how the interruption data is gathered

to the DMS database and how the number and durations of interruptions can

be searched for a certain customer and illustrated a methodology for

construction of interruption sector. The major aim is to develop a Web

application, which uses the existing interruption data gathered by other

systems and can be integrated with existing commercial data systems.

Yves Chollot and Martine Pauletto (2003) of Schneider Electric

Industries have proposed Web based solutions which provide an efficient and

reliable sharing of the network information between all the clients and which

increase the quality of their services. They have developed a general

architecture that describes the system that is Web enabled and designed for

medium voltage / low voltage substation monitoring and maintenance. The

developed system utilizes Web, GSM and GPRS technologies, which are

becoming lesser and lesser expensive and makes it possible to:

inform the right persons when there is an alarm in a substation

via e-mail

inform through SMS

display on the Web through a PC with a Web browser

provide real time information from the substation and also

provide historical information

Even though Web enabled power system applications provide faster

and timely information and collaborate efficiently with other network

135

technologies, they are still facing challenges due to heterogeneous nature and

the way they are communicating with each other.

Kannammal et al (2006) have proposed a secure model based on

the concept of shared objects and mobile agents to secure the business

database present in the e-business environment. The models have been

implemented that would control the direct access to the business database

while maintaining synchronized data transfer. This model faces security

challenges because of the introduction of mobile agents. The author uses

public key cryptographic algorithm to secure communication using mobile

agents, which causes additional overhead to the overall performance of the

system.

Wang Xin Fang et al (2003) had applied XML and SOAP in

transformer substation management information system for communication

between client and server and for providing data exchange with other electric

utilities. They have made possible that transformer substation can exchange

data with other power utilities through firewalls. Cao Hou-ji et al (2007) have

described how transmission substations exchange large quantity of outage

data among the heterogeneous platforms by using SOAP message in a

predefined format with strong firewall passing ability and without the need

for additional installation of special software to receive them.

Pasteur et al (2007) have proposed to utilize SOAP based Industrial

Messaging Specification (SIMS) to exchange power plant process data with

increased real time performance. The proposed SIMS messaging is based on

two components: a client and a server. The server component is a data

provider, which provides read, write and subscribe Web services. The client

component is the data consumer and it aims to invoke services and provides

call-back functions. These call-back functions are also Web services used by

the server to notify the client that new data are available. They have designed

136

the messaging server to have an access to any type of data sources and

capable of providing these data if its Web services are invoked.

The Messaging server provides two Web services read() and write()

that can be called by the client to write or read current values. Also client can

subscribe to some data in order to be notified if any of the attributes of this

data changed. If data changes are made, a notification is created and stored in

a buffer and the client will be intimated using ‘notify’ message that new

values are available for the data to which it has subscribed. The authors have

demonstrated this SIMS messaging based on Web services to exchange data

between a hydraulic power plant and a telecontrol center.

The SIMS messaging was first used in SCADA application to

telecontrol 15,000 MW hydro power plants from four dispatching centres

(Laurent Bacon and Cedric Bellec 2006). Three levels tele-control

architecture was defined to ensure the robustness of the control systems. First

level concerns about the power unit control, the second level called as plant

local control deals with power distribution among all power units and the

third level called as dispatching center deals with control, modification and

supervision of generation schedules. SIMS is used as the communication

protocol between a dispatching center and hundreds of local control plants.

Open connectivity unified architectures are evolving based on service

oriented architecture to have effective message based communication.

Standard specifications are emerging to define a base set of generic services

to browse and query namespaces, read / write data and publish / subscribe

events and data changes.

Jeongje Park et al (2010) have developed Web based on-line real

time reliability integrated information system for monitoring reliability of

electrical energy supply including wind turbine generator. In order to supply

137

information about the quality, reliability and security of the electric service,

several techniques and methodologies have been adapted that include

traditional operation planning, real time control functions and a redesign of

control system hardware and software architectures. As the utilization of

renewable resources has been receiving considerable attention in recent years,

the information system requirement is increased. The reliability information

system is more important for implementing the smart grid. The proposed

integrated information system not only supplies the information about the

reliability indices of the power system but also the estimation of CO2

emission.

The system developed by them is used to evaluate probabilistic

production energy with reduction in production cost and to obtain CO2

emission by inserting renewable energy to the power systems. Probabilistic

reliability indices have been used extensively for generation expansion

planning. The basic reliability indices, namely the loss of load expectation

(LOLE), the expected energy not supplied (EENS) and the energy index of

reliability (EIR) can be calculated using effective load duration curve. From

these reliability indices, probabilistic production energy, production cost,

capacity factor and CO2 emission can be obtained. The proposed on-line

reliability information system is successfully established and applied to Jeju

Island Power System in South Korea. The functioning of this system can be

viewed in the Website, http://worris.gsnu.ac.kr/PraWin. The users of this

system are the system operators, decision makes and information seekers and

they will access the system with browser via the Web. Jaeseok Choi et al

(2010) have extended the above work for grid constrained probabilistic

reliability evaluation of power systems including wind turbine generators.

They have developed a multi-state model for composite power system

reliability evaluation based on the composite power system effective load

138

model in order to consider wind turbine generators. The proposed work is

integrated with the information system developed by Jeongje Park (2010).

In order to enhance the security of operational data of different

power sectors in the deregulated environment and to protect the history of

outage information, a new SOAP based communication model is proposed

using remotely accessible shared objects between the messaging server and

client. The proposed model ensures synchronized communication between

the operations monitoring service providers like SCADA applications and the

power system operators to enhance reliable notification of failure events and

other operational data required for decision making to keep the capacity

reserve at the required levels to meet the demand and to avoid catastrophic

events.

The proposed model uses remote sharable bean components and

Web services to update the power system operators automatically with new

information due to data changes with respect to the operational status of the

power systems. The service that resides in the database server is informed

about the new information by triggering a function. Then the Web service

updates the bean with the recent outage information by invoking a remote

method. This sharable component is accessed by another Web service on the

client side, which sends the information to the decision makers and

information seekers. This approach improves security, as clients are not

aware of the location of the outage history database and makes power system

events notification application more scalable by deploying Web services.

4.5.1 Securing Outage History Database for Power System

Applications

While integrating power system applications with the Internet

offers potentially unlimited opportunities for increasing efficiency and

139

reducing cost, it also offers potentially unlimited risk. In the automation of

power system operations, the system parameters are maintained consistently

in a database. The outage history thus logged in the database throughout the

operation of power systems reflects the quality of power system maintenance.

In the deregulated environment, the power utilities should keep their

operational data secure especially the outage history table. Critical decisions

have been made based on the operational data and based on the outage history

table. A knowledgeable and malicious client or even the system operator can

execute unauthorized procedures or SQL queries inside the outage history

database.

An unauthenticated user with browser access to a Web server

hosting the power system application can exploit the database. This activity

will lead to unwanted decisions, which will pose a false hope to the planners

as well as consumers and create a negative impact on the quality of service of

the system. If an unauthorized user voluntarily records a false outage data to

the outage history table even when the power system operation is in line with

the normal and stable operating conditions, it will cast an illusion of poor

maintenance of power system. The risk of exposure to power system

operational data as well as outage history database is high, as any client with

browser access and specialized knowledge can exploit these vulnerabilities.

At the same time, the occurrence of the critical events should be notified to

the system operators in a synchronized manner without any information loss

or omission in order to take timely decisions.

A Web service model has been proposed for event notification

system to improve scalability and a concept of remotely accessible sharable

bean component is introduced to improve the security of Web applications.

This sharable bean component named as shared object is used not only to

improve the security, but also to enable synchronized and reliable

140

communication between server side applications. Using the recently stored

contents of the sharable bean component, the Web service updates the

operators and decision makers automatically with new information. The Web

service associated with the database server is informed about the new

information by triggering a function. This information is copied onto the

static sharable bean component, which is accessed by another Web service,

which will forward the same to the system operators and to the clients. The

block diagram representation of the architectural design of synchronized

event notification system is shown in Figure 4.3.

Figure 4.3 Block Diagram Representation of the Event Notification

System

Power system utilities maintain data about the various outages

occurring in the system, especially in distribution systems. The outage data

includes failure histories which comprise details of fault occurrence times,

fault restoration times, feeder information and interruption type. A radial

system with 2 feeders (F1 and F2) having an outage history as shown in

Table 4.2 has been considered (IEEE Guide, 2003) to illustrate the event

notification system. Assuming Feeder 1 has a total of 900 customers along

with a load of 1800 kVA and Feeder 2 has 1850 customers with a load of

3700 kVA, the total number of customers in the system is: 900+1800=2700

while the total load connected to the system is: 1900+1125= 4025 kVA. An

interruption is loss of power supply to the customer and its effect is variable,

which is classified as sustained and momentary based on the duration of

failures. The interruption types ‘S’ and ‘M’ stand for sustained and

momentary respectively.

Power

System

Clients

Remote

Sharable Bean

Component

Outage

History

Table

Web

Service

Web

Service

Trigger

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Table 4.2 Historical Outage Data for a Radial Feeder system

DateTime of

fault

Time of

restorationFeeder

No. of

Customers

Load

kVA

Interruption

type

23 March 12:02:20 12:20:30 F1 900 1800 S

15 April 16:13:56 16:14:26 F1 550 1100 M

5 May 00:23:10 01:34:29 F1 450 825 S

12 June 23:17:00 23:47:14 F2 400 800 S

6 July 09:30:10 09:31:10 F2 1850 3700 M

20 August 15:45:39 20:12:50 F1 450 825 S

31 August 08:20:00 10:20:00 F2 900 1800 S

3 September 17:10:00 17:20:00 F2 950 1900 S

2 October 10:15:00 10:55:00 F2 1850 3700 S

31 October 01:47:25 03:35:15 F2 900 2600 S

23 November 15:00:05 15:20:00 F1 550 1100 S

13 December 09:05:10 09:06:15 F2 1850 3700 M

The notification system includes one or more system operators and

service consumers to whom the failure events are to be informed in time to

take appropriate actions. Figure 4.4 depicts the architecture of the proposed

secure, reliable and synchronized event notification system.

The power system operators and decision makers have to

continuously enquire the database server by sending requests to the client side

Web service. The database server has to send responses to all the requestors

only during occurrence of new events in the system.

142

Figure 4.4 Secure and Reliable Synchronized Event Notification System

Web services have been deployed to act as intermediaries between

the database server and the remotely accessible sharable bean and between the

sharable bean and the power system clients respectively. The sharable bean

component contains fields to hold the information present in the outage

history database due to insertion of new failure event information or due to

update of existing outage data. And also it has a flag to indicate whether the

recent contents are consumed by the power system clients or not. The Web

services access the sharable bean using a middleware component, which is

common to both forwarding and notification services. The middleware

application in turn uses the RMI framework to access the sharable bean

component. The sharable bean component has been declared as static within

the remote object to enable sharing of data between services.

SCADA

System

(custom)

FAX/

Printer

B1

OutageHistoryTable

Power system

Client side

Web service

Java Bean

Sharable

Component

Create

Store

Invoke

Web service

Browser

E-mail

SMS

DS 1

JAX-RPC

based

Web service

Trigger

143

The outage history information as shown in Table 4.2 is stored in a

relational database using a table name “power”. The table has been associated

with the following trigger, which has to be initiated while inserting new

outage record or updating the contents of the existing record.

/*An insert or update trigger, which invokes in turn a Web Service

to forward the outage information*/ create or replace trigger message_send

after insert or update on power for each row

declare

fault_occurrence_time date;

restoration_time date;

feederno varchcar2(10);

interruption_type varchar2(2);

newdata varchar2(36);

mobile_no varchar2(15);

email_id varchar2(30);

begin

fault_occurrence_time := :new.intime;

restoration_time := :new.restorationtime;

feederno := :new.feedernumber;

interruption_type := :new.interruptiontype;

mobile_no := :new.mobileno;

email_id := :new.emailid;

newdata:= 'Outage Data ' || to_char(fault_occurrence_time,'hh:mi:ssam') ||

to_char(restoration_time,'hh:mi:ssam') || feederno ||

interruption_type;

communicationService(newdata, mobile_no, email_id);

end;

144

The trigger has to invoke the SQL procedure, “communicationService”

to forward the outage data or restoration data to the Web service associated

with the database server. The Web service in turn invokes a remote method,

“storeData()” to copy the outage information in the static sharable bean

component and sets the flag in the bean as ‘true’. The procedure for

“communicationService” is given in Appendix 4. The “communicationService”

will access the JAX-RPC based Web service using the endpoint reference,

“http://localhost:8080/power/ForwardingService?WSDL”

When there is an information update, the database server sends the

recent outage information to the Web service logically named as

“ForwardingService” through the “message_send” trigger. The

“ForwardingService” checks the flag attribute of sharable component to find

out whether the information provided by previous ‘update’ or ‘insert’ has

been consumed or not by the Web service on the client side, logically named

as “NotificationService”. If the flag is set to ‘false’ by the notification

service, it means that the previous information is consumed, and then the

forwarding service updates the outage information attributes of the sharable

bean by invoking the remote method “storeData()”. Also, the flag attribute of

the bean is set to ‘true’ to indicate that new information is available for the

notification service to consume. The “NotificationService” has been

implemented using SOAP communication in AXIS2 platform.

The notification service on the client side continuously monitors the

remotely accessible sharable bean for the availability of new outage

information. When the flag attribute is ‘true’ then it means that the new

information is available, and hence notification service retrieves the

information and sets the flag to ‘false’, to indicate that the information is

consumed. The flag field is updated accordingly in order to ensure

synchronized and reliable data transfer between the database server and the

145

clients. The power system clients have to contact the notification service

continuously by sending request messages for any information update. The

clients need not contact the database server for the information. Also, the

location of sharable bean and the database server are hidden from the clients.

By this approach, the outage history database is made secure by avoiding

direct access to the database server. Since the forwarding and notification

services are deployed as Web services, they are inherently scalable and will

respond to a large number of clients who are sending request messages for

updated information.

As the system is designed to notify the fault events during power

systems operations, it is not sufficient to have only scalable and synchronized

services but also reliable services are required to notify the system events to

the power system operators at appropriate time to enable them to take

effective decisions. The reporting facility is extended to various devices to

enhance the reliable communication. The notification service has been

developed to report the consumed information from the sharable bean

component to the power system operators using various modes of

communication such as PC, Web browser, printer or fax, SMS using a mobile

device or sending the outage information via e-mail.

This model enhances the security of operational data of different

power sectors in the deregulated environment and protects the history of

outage information as the clients are completely detached from the location of

the operational database. A common middleware component connects the

forwarding service and the notification service, which in turn uses an RMI

framework to access the static sharable bean component. The reliability of

notification system is enhanced by providing the information to various

devices simultaneously.

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4.6 CONCLUSION

The predictive reliability assessment of radial distribution feeder

system using Failure Modes and Effects Analysis has been presented and

analyzed in the distributed environment. The basic distribution system

reliability indices used in practice are illustrated. The reliability data

pertaining to the radial feeder test system is represented using XML

annotations. An effective SOAP communication with attachment model has

been developed to evaluate the reliability of power distribution systems.

SOAP handlers are used to intercept the SOAP messages and for

manipulating the request sent by the client. A handler chain is introduced in

the distribution system reliability estimation model in order to control the

sequence of invocation of services starting from data generation to sending

the SOAP response to the requested clients. Each handler is associated with

the error handling procedure to report the exceptions to the clients while

executing the services in sequence. Analytical techniques for distribution

system reliability assessment are effectively used to evaluate the wide range

of system reliability indices. Other power system services can also be

plugged into this model and the services are made available anytime and

anywhere for the power system planning and operations.

A secure and scalable Web application framework has been

developed using SOAP communication for continuous monitoring of power

distribution systems and maintenance of outage history information which is

required for planning while further expansion. The model is designed to

report the status of the power distribution system corresponding to outage and

restoration state in a synchronized way. This model enhances the security of

operational data of different power sectors in the deregulated environment and

protects the history of outage information as the clients are not aware of the

location of the operational database. The SOAP communication model uses

147

sharable bean components and Web services to update the power system

operators automatically with outage or restoration information due to data

changes with respect to the operational status of the power systems. The

service that resides in the database server is informed about the new

information by triggering a function. The reporting facility is extended to

various devices such as PC, Fax and other electronic gadgets to enhance the

reliable communication to avoid abnormalities. The developed notification

model is scalable as the forwarding and notification services are designed as

Web services and the critical events are notified to the operators as well as to

the decision makers instantly in a synchronized way, which enables them to

take appropriate actions in time to improve the quality of service and

maintenance.