MATHMET

The European Centre for
Mathematics and Statistics in Metrology

EMRP ENG63
“Sensor network metrology for the determination of electrical grid characteristics“

Description

Involved MATHMET members
NPL (UK), PTB (Germany)
Project partners
FFII (Spain), SMU (Slovakia), TUBITAK (Turkey), VSL (Netherlands), CIRCE (Spain), TU Clausthal (Germany), STRAT (UK), TU Eindhoven (Netherlands)
Duration
07/2014 - 06/2017
Website
Background

Methods to observe and control electrical power systems rely on sensor networks and state estimation. The use of these methods to meet the requirements of distribution networks is an active field of research and many challenges remain as networks evolve. Much of this research is carried out only in simulation and often assumes that the topology and impedances of the analysed networks are fixed and known. Further, network conditions that change with time affect the ability of algorithms to function effectively.
In order to address changing network conditions and limited forecast data this project aims to improve dynamic network models and to develop a procedure for on-line generation of high quality forecasts based on additional information, such as weather data. The combination of dynamic estimation for incomplete measurement infrastructure, improved forecasting based on additional external information and thorough uncertainty analysis goes beyond the state of the art and will lead to more complete and accurate information on grid state.
The topology and impedances of distribution networks are often poorly documented due to inaccurate and missing feeder diagrams. This project will therefore develop an approach to employ state estimation methods to address missing topology details, such as line impedances and uncertain connections, where network circuit diagrams are incomplete.
Optimal sensor placement algorithms will also be developed, aimed at reducing the amount of instrumentation required for effective grid control. This will include investigations into the use of Smart Meter data and Phasor Measurement Units (PMUs), potentially allowing further reductions in additional measurement infrastructure.
Reliable uncertainties will be assigned to real network parameters and the developed algorithms will be fully validated on demonstration Smart Grids, the Alliander LiveLab and Strathclyde University’s Power Network Demonstration Centre (PNDC), before being applied to real working distribution grids, going beyond the current research being done in simulation.

Need for the project

As electricity networks evolve to accept more distributed renewable generation and reduce reliance on fossil fuels, it is vital that sufficient information exists for their management and control. Such control is required for grid stability and to reduce the possibility of blackouts. Further, the precise structure and topology of distribution grids is often not known and is required if retrofitted distributed renewable generation is to be installed and used reliably, whilst maintaining a stable and controllable network.
With the large number of nodal points in distribution networks it is impractical and uneconomical to measure at every node and branch. A compromise must be reached between the cost of purchasing and placing a large number of sensors and the accuracy of the knowledge about the state of the grid. This leads to a need for sensor networks that are optimised to provide the necessary information to effectively control the Smart Grid at the distribution level, while minimising the cost of the required sensors. New techniques to monitor these grids and enable their effective control must expand on traditional metrology, which is focussed on individual measurements, and address the additional challenges when multiple simultaneous measurements are required.
It is also essential that the data flow between all components of sensor networks is secure and reliable. It is impossible to have a measurement system without a secure means of communication. Electrical grids are at risk from cyber terrorism, which could result in damage to equipment and widespread long term blackouts if security concerns are not addressed. New standards are emerging to ensure these issues are addressed as grids evolve. Smart Grid measurement systems must fulfil the requirements of these standards and the implications of such security measures on the uncertainty of sensor networks and their dynamic behaviour must be addressed.
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Background

Methods to observe and control electrical power systems rely on sensor networks and state estimation. The use of these methods to meet the requirements of distribution networks is an active field of research and many challenges remain as networks evolve. Much of this research is carried out only in simulation and often assumes that the topology and impedances of the analysed networks are fixed and known. Further, network conditions that change with time affect the ability of algorithms to function effectively.
In order to address changing network conditions and limited forecast data this project aims to improve dynamic network models and to develop a procedure for on-line generation of high quality forecasts based on additional information, such as weather data. The combination of dynamic estimation for incomplete measurement infrastructure, improved forecasting based on additional external information and thorough uncertainty analysis goes beyond the state of the art and will lead to more complete and accurate information on grid state.
The topology and impedances of distribution networks are often poorly documented due to inaccurate and missing feeder diagrams. This project will therefore develop an approach to employ state estimation methods to address missing topology details, such as line impedances and uncertain connections, where network circuit diagrams are incomplete.
Optimal sensor placement algorithms will also be developed, aimed at reducing the amount of instrumentation required for effective grid control. This will include investigations into the use of Smart Meter data and Phasor Measurement Units (PMUs), potentially allowing further reductions in additional measurement infrastructure.
Reliable uncertainties will be assigned to real network parameters and the developed algorithms will be fully validated on demonstration Smart Grids, the Alliander LiveLab and Strathclyde University’s Power Network Demonstration Centre (PNDC), before being applied to real working distribution grids, going beyond the current research being done in simulation.

Need for the project

As electricity networks evolve to accept more distributed renewable generation and reduce reliance on fossil fuels, it is vital that sufficient information exists for their management and control. Such control is required for grid stability and to reduce the possibility of blackouts. Further, the precise structure and topology of distribution grids is often not known and is required if retrofitted distributed renewable generation is to be installed and used reliably, whilst maintaining a stable and controllable network.
With the large number of nodal points in distribution networks it is impractical and uneconomical to measure at every node and branch. A compromise must be reached between the cost of purchasing and placing a large number of sensors and the accuracy of the knowledge about the state of the grid. This leads to a need for sensor networks that are optimised to provide the necessary information to effectively control the Smart Grid at the distribution level, while minimising the cost of the required sensors. New techniques to monitor these grids and enable their effective control must expand on traditional metrology, which is focussed on individual measurements, and address the additional challenges when multiple simultaneous measurements are required.
It is also essential that the data flow between all components of sensor networks is secure and reliable. It is impossible to have a measurement system without a secure means of communication. Electrical grids are at risk from cyber terrorism, which could result in damage to equipment and widespread long term blackouts if security concerns are not addressed. New standards are emerging to ensure these issues are addressed as grids evolve. Smart Grid measurement systems must fulfil the requirements of these standards and the implications of such security measures on the uncertainty of sensor networks and their dynamic behaviour must be addressed.
Impact

This project aims at a substantial extension of the mathematical infrastructure for metrology in Europe. Collaboration between European NMIs with mathematical and statistical expertise is essential to ensure wide take-up of the project outputs and to maintain Europe’s current leading role in mathematics for metrology. The results of this project will strengthen European capabilities for innovation by enabling traceability for modern metrology and measurement techniques. Product testing, safety regulations, medical diagnosis and drug testing will be significantly improved by the procedures for reliable uncertainty evaluation, decision-making and conformity assessment to be developed in this project. Training courses provided by the Creating Impact work package will allow European NMIs and DIs that are not part of the project consortium to develop their capacity in the application of mathematics and statistics to challenging uncertainty evaluation problems. The planned virtual European Centre for Mathematics and Statistics in Metrology will be based in the first instance on the members of the current JRP-Consortium.

Application Partners and Stakeholders

At the international level JCGM Working Group 1 (JCGM/WG1) on the Expression of Uncertainty in Measurement, which represents BIPM, IEC, IFCC, ILAC, ISO, IUPAC, IUPAP and OIML, has identified the need for research in the areas of Monte Carlo methods, regression and inverse problems, conformity assessment and the application of expert and prior knowledge. Many national professional societies of engineers and accreditation bodies have recognised the need for further development of uncertainty evaluation methods. These bodies maintain specialised committees dealing with the topic of uncertainty evaluation that regularly seek advice from NMI experts. European and international associations dealing with best-practice guides to the use of computationally expensive models have started to address questions of uncertainty.

23 stakeholders have expressed their strong interest in this project. The list includes industry, universities, professional societies, regulatory bodies, international organisation and NMIs outside Europe. The stakeholders will form an Application Partner and Stakeholder Committee whose role of will be to ensure that the outputs of the product have clear relevance to NMI experimentalists and to industrial stakeholders and end users. Committee members will also have a key role in testing and establishing the usability of software produced by the technical work packages.
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Publications

Related scientific publications

Authors
Title
Journal
Year
S. Eichstädt, N. Makarava and C. ElsterOn the evaluation of uncertainties for state estimation with the Kalman filterMeas. Sci. Technol. vol. 27(12), 125009, 20162016
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This work is part of the European Metrology Research Programme (EMRP) project ENG63. The EMRP is jointly funded by the EMRP participating countries within EURAMET and the European Union.
More information can be found here.
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