Wireless sensor networks and data analysis in Smart …ldos.fe.uni-lj.si/slo/01_Members/43_Mitja_Kolenc/Wireless sensor... · Wireless sensor networks and data analysis in Smart Grids

University of Ljubljana

Faculty of electrical engineering

Wireless sensor networks and data analysis

in Smart Grids

Mitja Kolenc, Matej Zajc

7th IEEE International Symposium on Information and Communication

Technologies - INTSIKT 2011 – ’’Smart City’’, Tuzla, June 06-07

University of Ljubljana ..: Faculty of electrical engineering:..

[LDOS] ..: Laboratory for digital signal, image and video processing:..

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Agenda

Motivation

ICT technologies in Smart Grids

Applications of wireless sensor networks in Smart Grids

Sensors data processing and visualization

Conclusions



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Motivation (1)

European Union countries are bound:• Directive 2009/72/EC, 2009/28/EC

• Directive ’’European Renewable Energy Directive’’

also called ’’20-20-20 objectives’’

- reduce greenhouse gas emissions for 20 %

- achieve 20% growth and energy efficiency

- increase use of renewable energy for 20%

• Document: Smart Grids: European Technology

Platform

• Document: Smart Metering for Europe: Smart

metering systems are essential

’’ESMIG (European Smart Metering Industry Group) believes that a 20% increase in energy efficiency, a 20% share of intermittent renewable energy in the system and a reduction of CO2 emissions by 2020 will not be possible without the introduction of AMM. Only through the widespread deployment of Smart Metering, will the European Union be

able to meet its 20-20-20 goals.’’



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Motivation (2)

Climate change

Reduction of natural reserves of fossil fuels

Modernize the existing power grid

Using the new standardized technologies

(e.g. PLC/BPL → IEEE P 1901, IEC 61334, ITU-T G.hn)

Providing a higher quality of electricity

Providing efficient use of energy

Ensuring a balance between production and consumption of electricity

Integration of distributed alternative energy sources into electricity power

grid

Smart Grids are in addition to nanotechnology and renewable

energy sources, three key topics that IEEE highlighted as top

priority in 2011.



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ICT technologies in Smart Grids (1)

Smart grid present an upgrade of existing electricity networks with

advanced ICT technologies, intelligent devices and systems

Smart Grids provide

• More effective delivery of electricity to users

• Providing a higher quality of electrical energy

• Enable integration and interoperability of alternative energy sources in electricity grid

• Enable so called ’’Self-healing’’ in case of disturbances, natural disasters or security

threats

• Provides consumers information about electricity consumption in real time

Smart Grids consist of:

• Energy providers

• Transmission

• Distributions

• Consumers

The existing power grid are becoming intelligent power networks



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ICT technologies in Smart Grids (2)

The Barriers to Entry of Smart Grid Deployment, and How to Overcome Them,

http://lightingcontrolpros.com/the-barriers-to-entry-of-smart-grid-deployment-and-how-to-overcome-them




























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ICT technologies in Smart Grid (3)

Information and communications technologies (ICT) are

playing a key role in Smart Grid

ICT in Smart Grid enabling:• Two-way communication

• Interaction between users and the electricity market

• Monitoring power network in real time

• Flexibility to changing situations

• Optimal use of resources and equipment

• Management and prediction of electrical energy consumption

• Integration, monitoring, control, security, maintenance, EMS, DMS,

• Security against attacks and threats

• Etc.

Findings by the High-Level Advisory Group on ICT for Smart Electricity Distribution Networks,

ICT for a Low Carbon Economy Smart Electricity Distribution Networks, July 2009



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ICT technologies in Smart Grid (4) - standards

C. Develder, Smart Grids & The role of ICT, Smart Grid Com 2010



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Wireless sensor networks

Applications of wireless sensor networks

A wireless sensor network (WSN) consists of spatially

distributed autonomous sensors to cooperatively monitor

physical or environmental conditions

Wireless sensor networks are now widely used in various fields

Transport and

logistics

Industrial

applications

Biology systems

monitoring

Environmental

monitoring

Geographical area

monitoring

Entertainment

and sports devices

Security and

control

Medicin applications

and diagnostic

Smart home and

automationSmart Grid and

energy control

systems



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Wireless sensor networks characteristics

Energy efficiency

• Low energy consumption → higher efficiency

• Battery powered → mobility

Wireless communication

• The advantage of large networks → easy installation, no wiring

’’Multi-hop’’ communications

• Each sensor node can not communicate with the central node, hence

the use of communication through several successive nodes, while

each of these is counted as one jump

Low price

• Allowed building a large number of sensor nodes

Distributed data processing

• Local data processing (filtering, aggregation of data) in each node can

relieve the main node



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Generic architecture of sensor nodes

Sensor node is the fundamental building block for sensor networks

Sensor network → consists of large number of nodes

Basic architecture of sensor nodes

Processorunit

Power supply

Communication unit

Storage

Sensorunit

• Power supply

(battery)

• Processor unit

(fast processor cores with low

power consumption)

• Storage unit

(installed, additional external memory)

• Sensor unit

(analog and digital sensors)

• Communication unit WSN node Mica Z, http://logicalneighbor.sourceforge.net/images/mica2.jpg

http://logicalneighbor.sourceforge.net/images/mica2.jpg



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Wireless sensor networks in Smart Grids

Smart Grids and

energy control

systems

Energy

providers

Transmission Distributions

Consumers

• Control of

diffuse sources

• Monitoring status

of accumulation of

electricity

• Balance between

production and

consumption of

electricity

• Control of

transmission lines

• Power monitoring

• Alarm

• Control of

transformer stations

• Energy consumption

• Alarm

• Smart mereting

• Advanced Metering

Infrastructure

• Control and

managment



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Wireless sensor networks in Smart Grids -

limitations

Harsh environmental conditions :

• The impact of electro energetic environment on topology and wireless network

connectivity - loss of connection

• The impact of RF interference, corrosive environment, high humidity,

vibration, dust particles on the sensor nodes - often replacement of sensor

nodes in needed

Reliability and delay requirements:

• Sensor networks in Smart grids will have a different quality of service (QoS),

reliability and delay

• Providing real-time data

Packet error and varying channel capacity

Resource limitations:

• Power supply, processing power, storage

• Limited battery power - communication protocols are designed accordingly, an

appropriate choice of electronic components



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Sensors and heterogeneity of data

B. Lu, W. Song, Research on heterogeneus data integration for Smart Grids,

IEEEexplore, September 2010

Energy

providers

Transmission Distributions

Consumers

• Control of power

plants and battery

power

• Control of

alternative energy

sources

• Control of

transmission lines

and transportation

way

Electronic Power Research Institute Sensor

technologies for a smart transsmision system, An

EPRI White paper, 2009 – sensor types

• Control of

transformation

stations

Data heterogeneity

in Smart Grid

• Smart metering

•AMR/AMI



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Sensor data analysis – data visualization

Data visualization is a graphical representation of data in a format that allows a

qualitative understanding of the information provided

Data visualization

• Scientific visualization

• Information visualization

Motive - Why use data visualization?

• Increasing number of smart meters, smart devices

→ data types

• Data heterogeneity

• The amount of data in Smart grid is increasing

→ estimate: for 3000x

• Distributors, costumers:

- New tools

- Architecture

- SystemsData management in Smart Grids

Amount of data

Amount of installed

smart technologies

Amount of data

will increase for 3000 x



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Sensor data analysis – data visualization

in Smart Grid

Data

Smart meters, sensors,

devices, mobile terminals - at

all levels of active networks

Communications network,

transport and data collection

Integrated data architecture for

data management CIM -

Computer-Integrated

Manufacturing)

Data analysis and

visualization

Data

generation

Transport

Persistence

Visualization

J. Taft, Smart Grids data mining



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Sensor data analysis – data visualization methods

and algorithms

Data types

Methods and

algorithms

Use of data visualization

in Smart Grid

Standard 2D/3D

plots

Geometrically

transformed

plots

Using icons

Pixel plots

Dimensional

stacking

One dimensional

data(1D)

Two dimensional

data (2D)

Multidimensional

data (3D > )

Central monitoring

systems

web portals for

monitoring

parameters in real

time

Mobile devices

Data visualization



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Implementation of algorithms in the processing

systems – Matlab Simulink

Requirements for communications and data transfer in real time

How frequently ? How quickly?

Constant Real time (< 1s requirement to respond) → NEED

Hourly Near real time (< 10 s feedback)

Daily Not problematic (response to the request within a reasonable time)

Designing fast and efficient communications systems for data analysis in real

time

• Using high-speed dedicated processor

DSP technology

• FPGA + Matlab Simulink

• Design of components to operate

in real time

HDL Coder™

Simulink → Verilog, VHDL



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Project activities: EUREKA - IMPONET

General goals

High-performance communication networks, based on

existing power networks (i.e. PLC)

Remote access and control, developing an appropriate

platform for smart metering

Research and development of new services capable of

monitoring the quality of electrical signals in future electricity

networks.

Investigation of how different elements of the electricity

network and information systems may interact and share

information between them in real-time.

Basic info

Project name: IMPONET

Project type: european project

Project duration: 26 months

Project resources: 13.950.428 EUR

Project partners

Capvidia Tecnológico, Fundación Deusto, Faculty of Electrical Engineering, Josip Juraj Strossmayer, ESI -European Software Institute Tecnalia, HEP Operator prijenosnog sustava , INDRA Sistemas S.A., INDRA, Software Labs, Kapion D.O.O., Mnemo, Evolution and Integration Services, Prodevelop, Universidad polytécnicade Madrid, University of Girona, University of Ljubljana Faculty of EE, LDOS, Vrije Universiteit Brussel, AnswareTECH S.L., Engineering Office for Integrated Projects, Innova, LNL Elektrik Elektronik Bilisim ve Danismanlik, Ltd. Sti., Nile University, SABA Electric, Union Fenosa Distribucion, Kema, Orange Labs Cairo, Wooam Inc.



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Project activities: KC - SURE

General goals

KC SURE – Competence Center – Advanced systems

of efficient use of electrical energy

Establishing the concept of Smart Girds in Slovenia

Development of solutions for active electricity

network (ICT infrastructure, services, ect.)

Development of power system components

Adaptive, enery efficient devices and systems for

home use

Research on energy-efficient power converters

Basic info

Project name: KC - SURE

Project type: home project

Project duration: 26 months

Project resources: 10.585.130 EUR

Project partners

TECES, Bartec Varnost d.o.o., BSH hišni aparati d.o.o., ELES, Elektro Slovenija, d.o.o., Gorenje, d.d., Hidria, Rotomatika, d.o.o., INEA d.o.o., Kolektor Group, d.o.o., Metrel, d.o.o., Petrol, d.d., Sipronika, d.o.o., Smart-COM, d.o.o., Domel, d.d., Univerza v Ljubljani, Fakulteta za elektrotehniko, Fakulteta za strojništvo, Univerza v Mariboru, Fakulteta za elektrotehniko, računalništvo in informatiko

KC - SURE



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Conclusions

Wireless sensor networks are used in many different Smart Grid

fields – they help to provide data from a field

The implementation of algorithms in the processing systems

(for purposes of: capture, processing and data analysis in real

time)

Data visualization in smart grids: the use of visualization

algorithms and methods for the analysis of heterogeneous data

Heterogeneity data, multi-sensor data fusion, the search for new

relationships between heterogeneous data



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Acknowledgements

This work was funded and conducted under the auspices of the

European project EUREKA: IMPONET

University of Ljubljana

Faculty of electrical engineering

Thank you for your attention !

Mitja Kolenc

[email protected]

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Wireless sensor networks and data analysis in Smart …ldos.fe.uni-lj.si/slo/01_Members/43_Mitja_Kolenc/Wireless sensor... · Wireless sensor networks and data analysis in Smart Grids