Monitoring and Visualising a Neighbourhood Area Sub-Grid

Zoya Pourmirza and John M. Brooke

School of Computer Science,The University of Manchester, Oxford Road, Manchester, U.K.

Keywords: Smart Grid, Communication Network, Neighbourhood Area, Monitoring, Sensor Network, Software

Architecture, Visualisation Tool.

Abstract: In this paper we analyse the architecture and technologies for monitoring the Neighbourhood Area Network

(NAN) of the Smart Grid. We consider the role of sensor networks in providing information about the

environment of the NAN, for example to monitor temperature and movement of vehicles and people, which

can provide useful information about changes in the loading of the NAN. The two main contributions of

this research are as follows. Firstly, we develop a software architecture for an ICT network of the Smart

Grid which could integrate information from sensors from various levels of the grid. Currently no such

architecture has been implemented for collecting data and providing the basis of Decision Support Tools

(DSTs) for the NAN level of the grid. Secondly, we have developed a visualisation interface for the human

operator of the grid, as the basis for such DSTs, which overlays the information from the sensors and the

measurements of the electrical performance of the NAN on a GIS-based view of the NAN. We describe an

actual implementation of this design currently being installed in the sub-Grid supplying the University of

Manchester which is of comparable size and complexity to urban NANs.

1 INTRODUCTION

The intelligent electrical networks called Smart Gids

incorporate communcations and information

technology to service the generation, transmission

and finally the distribution networks of the power

grid. To reflect this structure the ICT network of the

Smart Grid is divided into three networks. These

networks can be considered as the Wide Area

Network (WAN), Neighbourhood Area Network

(NAN) and Home Area Network (HAN). The lack

of research on the monitoring and predictive real-

time system control in the NAN leads us to focus on

this specific level of the Smart Grid.

We have designed and implemented a software

architecture for the ICT in the NAN. It integrates

data from smart meters for controlling building

data, wireless sensors for monitoring the

environment and devices which measure the

electrical behaviour of the power network. Finally,

based on our software architecture, a visualisation

tool has been developed which provides a basis for

Decision Support Tools (DSTs) that can be used to

plan and operate the Grid at the NAN level.

The remainder of this paper is organised as

follows: Section 2 discusses about the usage of the

sensor network in the Smart Grid. Section 3

proposes a software architecture for the ICT section

of the NAN. Section 4 presents a visualization tool

for the NAN. Finally section 5 summarizes our

contributions and presents ideas for future work.

2 THE ROLE OF SENSORNETS

Power Grids have historically been centrally

controlled, with the NAN and HAN levels being

essentially passive. Detailed monitoring at this level

has therefore not been a priority. As the Grid

evolves to a higher degree of localised control, the

types of sensors and computational units should

become more lightweight and widely deployed. We

gather two types of real-time information, firstly

measurements of the electric network itself and

secondly measurements of the environment of the

NAN. The first type of information enables the

detection of abnormal behaviour which can identify

faulty components before their failure leads to more

widespread failures of the system. The second type

enables predictions of future demand to be made

based on environmental variables that influence

local demand.

127

Pourmirza Z. and M. Brooke J..

Monitoring and Visualising a Neighbourhood Area Sub-Grid.

DOI: 10.5220/0004409101270131

In Proceedings of the 2nd International Conference on Smart Grids and Green IT Systems (SMARTGREENS-2013), pages 127-131

ISBN: 978-989-8565-55-6

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

We choose to monitor the NAN by a WSN. We

selected TinyOS (Levis et al., 2005) sensors as a

prototype to evaluate the architectural proposal by

simulation. Accordingly, TOSSIM (Levis and Lee,

2003) the WSN simulator for the TinyOS sensors

has been selected to simulate the network. TOSSIM

has some advantages over the other WSN simulators

which will be discussed later. A range of

applications of the WSN in Distribution Networks

(of which NANs are an example) are identified in

(Pourmirza and Brooke, 2012b). The ones we are

particularly interested in are local weather condition

monitoring and lighting in order to find the relation

between these parameters and electricity

consumption.

3 MONITORING A SUB-GRID

ON A UNIVERSITY CAMPUS

In designing an ICT architecture for the NAN sub-

Grid, we choose to componentize the NAN system

into interacting sub-systems. This architecture has

various advantages such as preventing single point

of failure, dealing with a potential information flood

caused by the centralized system, and applying finer

grained monitoring and control at the level that was

blind previously. Additionally, utilising cluster

based communication and componentizing the ICT

network monitoring and the NAN results in a

scalable architecture that can cope with future

implementations and additions to the system.

This design is currently being implemented on

University of Manchester campus. The ICT

architecture for this project (Figure 1) is based on

the network architecture described in more detail in

(Pourmirza and Brooke, 2013). It contains a server

side and a client side. The server side itself has 3

layers which are infrastructure layer, persistence

layer, and application layer. The infrastructure layer

is itself componentized into three monitoring levels,

each relating to a specific section of the NAN in the

distribution sub-Grid. The monitoring system

implemented at the building level utilises smart

meters, which are used to monitor the Home Area

Network level data. These devices are located in all

the buildings (our HAN level) in our campus test

bed, transmitting data every 30 minutes. They are

already connected to the power network and

communicate by wired connections.

The monitoring system implemented at the street

level is a wireless sensor network (WSN) which is

used to monitor the environmental data such as

temperature, light, and humidity, which are being

logged every second. These environmental data are

important for understanding and controlling the

power grid since they can provide information that

can be used to anticipate demand and improve

control actions

. These sensors run on batteries. The

battery life with a 1% duty cycle would be 6 months

(Kling, 2003). These sensors are able to alert when

they run low on battery power. Since these sensors

are grouped into clusters and have direct

communication to their cluster head the routing will

not be affected while changing the battery.

The final level of the monitoring is the substation

level monitoring. The devices used for this level are

reconfigurable real-time control and acquisition

systems called compact RIOs. 16 cRIOs are located

in each substation in our campus test bed logging

data from the electrical network four times a second.

They are connected to the power network and are

able to transmit data through wired and wireless

communication. Electrical network attributes such

as three-phase voltage, current, active power, power

factor can be monitored at this level which can be

used for fault identification, power quality analysis,

and many more applications.

At the moment the building level and the

substation level metering devices are implemented

in a real test bed, already producing live data. The

street level monitoring devices are not available yet,

thus we have used WSN simulation called TOSSIM

to simulate the data at this level. The advantage of

TOSSIM is that it enables the users to take their

implementation and run it on an actual sensor when

these are available. Thus we can test our prototype

network in the laboratory based environment and

also in a real physical environment. To achieve this,

TinyDB (Madden et al., 2005) which is a WSN

query processing engine, was extended to extract

environmental data from the electrical Grid

(Pourmirza and Brooke, 2012a). The difference

between TinyDB and a traditional DB is that, instead

of passively receiving and archiving data, we can

also receive real-time data in response to our

queries. The three monitoring levels discussed in the

infrastructure level will transmit their data to the

next layer of the architecture called the persistence

layer.

The persistence layer contains a local data base

which stores all the data received from the

infrastructure layer, and a database connectivity

module which use an interface to connect to the next

layer which is the application layer. The backup

strategy embedded in this level will enhance the

preservation of the data. Moreover it will

accommodate the ever-increasing volume of data

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produced by infrastructure layer.

The application layer is a NET application which

is able to connect to the outside world, and send

HTTP request and respond to the client side. It can

use some component in case of emergency to send

alerts to the engineers in the field and grid operator

behind the screen in the control room.

On the client side we have used technologies

such as ASPX, CSS, JavaScript, Ajax, and Google

Maps API to visualise the collected data so it can be

viewed by the grid operator.

According to our knowledge this is the first

software architecture introduced for the NAN in the

distribution sub-Grid which could integrate various

level of monitoring from building level to the street

level and finally the substation level, and apply

monitoring and control over the collected data at

such level.

In the next section we will describe how this

visualisation tool operates.

Figure 1: The developed software architecture.

4 THE VISUALIZATION TOOL

FOR A NAN

While Smart Grid systems provide for automated

control of the electrical networks, human operators

are still essential players for certain monitoring and

control tasks. We have developed a visualisation

tool to enhance human understanding of

performance of the electrical network. This tool

operates at the application layer of our architecture

and provides a basis for the development of

Decision Support Tools for planning and operating

the network.

4.1 Related Work

Visualisation is an eminent method for managing

and displaying the data, which has been employed in

different engineering fields. As an example in water

distribution grids, visualisation has been used to

provide a graphical user interface that enables the

display of dynamic information and prediction of the

future state of the grid. This information can be

displayed via a Google maps based web interface

(Haines et al., 2009, Stoianov et al., 2007). The

Google map visualisation techniques can also be

applied to other networked systems, such smart

electrical grid.

A recent study by (Nga et al., 2012) proposed a

visualisation technique exploiting Google maps and

other techniques to display the data of the

distribution network in the Smart Grid. The research

mentioned above has provided a tool for monitoring

the AMI and SCADA devices. These can only

monitor and display the data of the critical areas of

the distribution grid, whereas our proposed

visualisation tool goes beyond that and can visualise

the data from all over the distribution grid.

Furthermore, our tool is able to visualise both the

electrical grid data, and also environmental data

from WSNs at street and building level. Thus all the

data collected from the infrastructure layer can be

visualised by our developed visualisation tool.

4.2 A GIS-enhanced Visualisation Tool

Our visualisation tool overlays the electrical grid and

environmental information on a map using Google

map API (Application Programming Interface). This

research is being implemented on the medium

voltage power network of the University of

Manchester campus test bed, enabling the

visualisation tool to be developed using real

equipment, real data, and the feedback from

professionals in the field. Figure 2 illustrates the

additional functionalities added to the tool as first

described in (Pourmirza and Brooke, 2012a) such as

security authentication, fault detection and alarming

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system. Moreover, we have added visualisation of

the data from the smart meters, deployed in our test

bed, to the data collected from the cRIO devices in

the substations. The figure below displays data over

a period of one week. The x-axis presents the time,

here the days of a week, and y-axis represents the

power consumed by the selected smart meter. The

peak (week days) and off-peak (weekend) are

illustrated.

Figure 2: The developed visualisation tool.

4.3 Data Flow Diagram

The Figure 3 illustrates the level 1 Data Flow

Diagram (DFD) which illustrates the flow of data

and highlights the main functions carried out by the

system.

When the application is started, the Google map

GIS information will be loaded and the program will

show the map view of our test bed. Then the user

can follow three main actions. The main action is to

select the desired metering device and send request

for login to the database. After entering the correct

the database and select the data table of interest for

the desired period of time and then view the data in a

new window. It can also plot the data graph.

Showing the data on a data graph enhances better

comprehension of the behaviour of the data. After

visualising the data, it will determine if any of these

data are over the defined threshold. If the threshold

is exceeded then the tool will send an alert to any

operators who have subscribed to it. For sending the

alert it will follow two main actions. Firstly in order

to send the passive alert it will highlight the over

threshold data for the operator behind the screen.

Secondly in order to send the active alert it will send

text messages to the engineers in the field by using

IntelliSMS component. It will inform them about the

actual fault and time and location of it.

In addition there are two other flows that the user

can follow. S/he can check the schematic view of the

test bed and locate the desired metering devices on

schematic map. The final flow is to start the

TinyDB, in order to generate the environmental data

and then send these data to the WSN simulator.

Finally the user can select the WSN simulator on the

visualisation tool and visualise the environmental

data collected from the street level. All these

collected data can then be used as an input for

control algorithms of the grid.

5 SUMMARY AND FUTURE

WORK

This research was undertaken to design an ICT

architecture for the neighbourhood area of the Smart

Grid (NAN), to analyse the various communication

technology, and to find the most appropriate choices

for this specific section of the grid. The present

study justifies the need for having a sensor network

alongside the power grid, and determines its possible

applications in the future grid.

This paper described how we have designed and

implemented a software architecture which is

partially applied to the real test bed in the electrical

network sub-Grids. We explain how we collect real

data and how we use simulation to tune the

implementation of the architecture on a real test bed.

The main concern of this research is that it will

enable information to be gathered from the working

of a real system operating a sub-Grid level where

monitoring has not previously been deployed.

The key contribution of our work is that it can

deliver the information from the building levels,

environmental conditions, and finally from the

power grid below 33 kV which remains unknown

until now. According to our knowledge there is no

previous architecture that could integrate all the

information as a whole system for this level of the

electrical Grid and present them via visualisation

tool. This tool acts an essential component in

operation and planning of the system.

In future, more work needs to be done to add

Web Services to this architecture and to add more

functionality to the visualisation tool. For example,

automatically detecting the failure of nodes and

alerting the field engineers. Finally, a further stage

of this research is to investigate control strategies,

for the NAN level of the Grid, to further the

decentralised control of the whole Grid.

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Figure 3: The DFD diagram.

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