SAMBA WP7 report - Statnett · 5.2.4 Veracity: the trustworthiness of the data..... 22 6 Recommendations for further work in Statnett ... 23 7 References ... in Statnett, as well

Innovations and recommendations SAMBA WP7 report

Report Dokumentet sendes til:

Arne Smisethjell / UPX

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Maren Istad/SINTEF Energy Research

Eivind Solvang/SINTEF Energy Research

Arne Smisethjell/Statnett

Maria Catrinu-Renström/Statnett

Jørn Johnsen/Statnett

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Til orientering:

Fornavn Etternavn / UPX

Ansvarlig/Adm. enhet:

Fornavn Etternavn / UPX

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Dokument ID: [000000]

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Innovations and recommendations - SAMBA WP7 report

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Executive summary This report gives an overview of innovations and recommendations from the research project Smarter Asset Management with Big Data (SAMBA). The project, led by Statnett, has had 40 user-oriented disseminations and 13 public oriented disseminations from 2016 to early 2019. The project has succeeded in spreading results to a wide audience mainly within the energy domain. The project has involved many Statnett employees in workshops and discussion to identify, describe and test use cases for asset management purposes. In addition, employees from different departments have been involved in describing the future risk monitoring function and ICT architecture. SINTEF Energy Research, IBM, ABB and GE Grid Solutions have participated in discussions, tested models and presented results for Statnett. The project results are synthesized into 12 main innovations and these are presented in this report:

1. Use case identification 2. Circuit breaker failure model 3. Risk monitoring function 4. Prediction of transformer winding ageing 5. Multivariate analysis of transformer failure gasses 6. Overview of historical data availability 7. Cable and transformer temperature prediction 8. Asset reinvestment analysis 9. Reactor breaker reignition identification 10. Health index for transformers 11. Line connector condition assessments 12. ICT asset management architecture investigation

Detailed recommendations are available for each use case that has been tested. This report provides generalized recommendations for data management, asset management and analysis. The report also contains recommendations for further work in Statnett to follow up the results from the SAMBA project. Most importantly, the establishment of the asset management digitalization program called FRIDA ensures that the results from the SAMBA project are taken further in Statnett.


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Contents

1 Introduction .......................................................................................................................... 7 1.1 Report overview ..................................................................................................................... 7 1.2 Big data ................................................................................................................................... 7 1.3 Smart Grid Reference Architecture ........................................................................................ 7

2 Project overview .................................................................................................................... 9 2.1 WP1 User needs and possibilities ........................................................................................... 9 2.2 WP2 Methods and use cases and WP3 Data models and integration ................................. 10 2.3 WP4 Architecture and business rules ................................................................................... 12 2.4 WP5 Testing and evaluating of use cases ............................................................................. 12 2.5 WP6 Risk monitoring centre ................................................................................................. 13 2.6 WP7 Innovations and recommendations ............................................................................. 14 2.7 WP8 Project management and dissemination ..................................................................... 14

3 Dissemination ...................................................................................................................... 15

4 Innovations.......................................................................................................................... 17

5 Recommendations from SAMBA .......................................................................................... 20 5.1 Recommendations for data-driven asset management ....................................................... 20 5.2 Recommendations for data management ........................................................................... 20

5.2.1 Volume: the amount of data ................................................................................... 21 5.2.2 Velocity: the speed at which the data is created, stored, analyzed and visualized . 21 5.2.3 Variety: the many sources and types of data. ......................................................... 21 5.2.4 Veracity: the trustworthiness of the data ................................................................ 22

6 Recommendations for further work in Statnett .................................................................... 23

7 References ........................................................................................................................... 24

Appendices:

V1 List of disseminations ........................................................................................................... 25

V2 Agenda for the final conference ........................................................................................... 27


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1 Introduction

1.1 Report overview

This is the final report from the project Smarter Asset Management with Big Data (SAMBA). The aim of this report is to give an overview of innovations and recommendations in order to aid the transition from research phase to implementation at Statnett.

The report contains:

• An overview of the project (chapter 2) • An overview of the disseminations from the project (chapter 3) • An overview of the innovations from SAMBA (chapter 4) • Recommendations from the projects (chapter 5), primarily based on:

o Gap analysis o Use case identification/description and data matrices o Work on ICT architecture o Use case testing o Defined scope for a risk monitoring function

• Further work in Statnett (chapter 6)

1.2 Big data

The amount of data Statnett has today for asset management purposes might not be that large compared to other industries. However, it is foreseen that in the future there is a large potential for data creation and gathering - a significant increase in the amount of data that will be made available for analysis, with an important impact on asset management processes.

The hypothesis of the SAMBA project was that the Big Data concept as an underlying technology can support Statnett's need for complex analysis, advanced data processing and storage, and the future need for high volume, high variety and near real-time analysis. The project has tested the parts of the Big Data concept that will be useful for Statnett's future asset management.

1.3 Smart Grid Reference Architecture

The SGAM framework [1] has been used in the SAMBA-project to explain the project's main R&D challenges and scientific methods, see [2] for more information. The framework is shown in Figure 1-1. All the layers of the SGAM framework must be interoperable, as information from components in the component layer must be available for analysis in the function layer to support the business objectives of optimized lifetime of the components, high reliability etc.


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Figure 1-1 SGAM framework [1]

In SGAM's function layer, depicted in Figure 1-1, functions and services are represented as use cases independent of the physical realisation in systems and components. This level ensures that the right information enters the right process and the right actor. Therefore, the main research work in SAMBA consists of the description and testing of use cases with focus on data (information layer) and the description of the future ICT architecture. However, the implementation of use cases, data models and architecture at Statnett is outside the scope of SAMBA.


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2 Project overview The SAMBA-project has been a three year long (2016-2018) innovation project, partly supported by the Norwegian Research Council's EnergiX-program1. Statnett has been the project owner. SINTEF Energy Research, IBM, GE Grid Solutions and ABB have been project partners.

The work in SAMBA has been organized in 8 work packages (WPs). Figure 2-1 shows the timeline and connection between the WPs. As illustrated in the figure all WPs, directly or indirectly, gives input to WP7, some WPs more than others.

Figure 2-1 SAMBA WPs timeline and connection

2.1 WP1 User needs and possibilities

The main activities in WP1 User needs and possibilities were three workshops involving all partners in the project. The aim of these workshops was to get an overview of both the status and future needs concerning asset management in Statnett and of the research, methods, tools, systems and experiences within asset management from GE Grid Solutions, ABB, IBM and SINTEF Energy Research. The results of this WP are documented in the report Status and further work - Results from WP1 in the SAMBA-project [2]. The results are also provided in a summary that can be found in [3]. Important elements of asset management, including ICT requirements, are shown in Figure 2-2. A well-designed and well-functioning ICT-architecture should be able to tie all the elements together and is a premise for the asset management platforms of the future. Data must be collected, exchanged, aggregated, presented, visualized and stored to fulfil the needs of the users. The ICT capacities necessary for asset management were identified in WP4.

1 https://www.forskningsradet.no/prosjektbanken/#/project/NFR/256358

https://www.forskningsradet.no/prosjektbanken/#/project/NFR/256358


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Figure 2-2 Elements in asset management, including ICT requirements

2.2 WP2 Methods and use cases and WP3 Data models and integration

The work in WP2 Methods and use cases and WP3 Data models and integration is documented in the report Use case collection – SAMBA WP2 and WP3 report [4]. In these two WPs use cases, see Figure 2-3, were identified through a series of workshops with Statnett's employees. Subsequently, the use cases where described, 95 in the form of a short user story, while 16 were described using the IEC standard 62559-2 use case template. In addition, data matrices for the required input data were made to document the availability and where the data can be found in Statnett today. In addition, the industry partners, ABB and GE Grid Solution made textual descriptions of their use cases.


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Figure 2-3 Overview of use cases in SAMBA


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2.3 WP4 Architecture and business rules

The results from WP4 Architecture and business rules are documented in a report called Future asset management architecture – SAMBA WP4 report [5], which defines and describes the existing and proposed future architecture for asset management in Statnett, see Figure 2-4. The Open Group Architecture Framework (TOGAF)2 methodology was used for assessing, analyzing and documenting the architecture. WP4 has identified several relevant ICT capabilities required for future asset management by analyzing needs from the SAMBA project, as well as other relevant initiatives in Statnett. The report builds on the results and conclusions from among others the Finbeck3 and Fia4 projects as well as the AutoDig5 2.0 project.

Figure 2-4 Statnett reference architecture

2.4 WP5 Testing and evaluating of use cases

In WP5 Testing and evaluating of use cases 19 use cases were tested by Statnett, SINTEF Energy Research, ABB and GE Grid Solutions. The results from the use case testing and the recommendations derived from the testing are documented in the report Use case testing – SAMBA WP5 report [6]. The selection of use cases for testing was done in WP2 based on input from Statnett's experts, available data at Statnett today, and available models/methods from industry and research partners. The testing consists of developing algorithms/codes to carry out the functions described in the use cases, and then applying this to real data from Statnett, if available. In some use cases fictitious data was used due to lack of appropriate real data. For some of the use cases the models that were tested were readily available, i.e. asset health indexes for transformers. The results from the testing highlight possibilities and benefits of implementing the use cases in Statnett, as well as identified limitations and problems. Assessment of input data (availability and quality) was central in all use cases. In SAMBA, equally important was to shed light on the areas with good data and areas where data was missing or was of poor quality.

2 TOGAF is a framework for enterprise architecture that provides an approach for designing, planning, implementing, and governing an enterprise information technology architecture. 3 Finbeck was an internal Statnett project. The project focused on ICT system architecture (data storage, system, integration and functionality) and infrastructure (servers, network, gateways, routers and security barriers). 4 Fia FIA is an abbreviation for common information architecture in Norwegian. The FIA-project aim at ensuring that Statnett has a common data model, handling of master data, data flow and life cycle. 5 Autodig is the system that is currently used to perform failure analysis, including data gathering and analysis from several data sources.


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The SAMBA-project has supported the project and master thesis of Martine Ukkelberg, NTNU, and summer jobs for three students, Martine Ukkelberg, Markus Heggås and Øyvind Auestad. Figure 2-5 shows pictures from the blogs about the summer project students.

Figure 2-5 Left: Jørn Foros, Anders Holvik og Martine Ukkelberg Right: Øyvind Auestad, Arne Smisethjell and Markus Heggås

2.5 WP6 Risk monitoring centre

WP6 Risk monitoring centre is a feasibility study of what risk monitoring can be in Statnett. The study is documented in the report Risk monitoring function in Statnett – SAMBA WP6 report [7]. The report describes the scope for risk monitoring in Statnett, with focus on asset-related risks, i.e. the risks emerging from the degradation of the assets' condition. The risk monitoring function was described in relation to the functions asset management, system operation/planning, analytics services and 1st line operations, see Figure 2-6. The risk monitoring function in Statnett will have an active role in alerting throughout decision levels, i.e. system operations or asset management decision-makers, to take appropriate actions when risk is changing/increasing. The risk monitoring function will contribute to improving data quality through discovering and investigating deviations and diverging observations in data sets and models.

The risk monitoring function will also have an important role as the "eyes" of the organisation in the field using cameras and drones. Security issues, like trespassing, vandalism and safety issues, i.e. ensuring that it is safe to enter a station after a failure.


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Figure 2-6 Risk monitoring function in relation to other functions in Statnett

2.6 WP7 Innovations and recommendations

In WP7 Innovations and recommendations the work performed in SAMBA-project was summarized through descriptions of innovations and the main recommendations from the project. The work of WP7 has been to review all reports and publications from the project, gather the most important findings and make this report. In addition, the work in WP7 has contributed to the final reporting to the Norwegian Research Council.

2.7 WP8 Project management and dissemination

In WP8 Project management and dissemination meetings where arranged every other week for the core SAMBA-group to keep track of project status. Steering committee meetings were arranged twice a year and reports for the Research Council were made three times a year. Dissemination in the project is reported in chapter 3 of this report.


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3 Dissemination The SAMBA-project has made it a priority to make the results from the project available outside the project. An overview of the dissemination in the project is shown in Figure 3-1. The categories in the figure are according to those used by the Norwegian Research Council. Most of the dissemination activities have been user-oriented reports, notes, articles and lectures for the target groups interested in asset management, in total 40. In addition, there have been 13 public-oriented dissemination measures, including 36 public blogs. Total of 53, including 2 presentations that will be held in April and May, after the formal closure of the project.

Figure 3-1 Dissemination from the project

The user-oriented measures include a paper and poster to CIGRÉ 2018 in Paris and a presentation at the 2018 Euromaintenance 4.0 conference in Antwerp, Belgium. The project was also presented at the seminar "Moderne vedlikehold" in 2018 in Trondheim, Norway. The project had two reference visits. The first visit was to San Antonio, USA, where SAMBA representatives discussed and presented the project to two utilities American Electrical Power (AEP) and First Energy and participated at the Distributech 2018 conference. The second reference visit was to Qatar, to discuss and share experience on asset management topics with Katharama, the DSO and TSO of Qatar. The complete list of disseminations can be found in Appendix 1 (in Norwegian). The project is also mentioned in a video about asset management in Statnett, see https://bcove.video/2DmNozB

On 23. January 2019 the SAMBA-project invited to a final conference7 in Oslo. Almost 90 participants from SAMBA partners, distribution network operators, asset management vendors and Nordic transmission system operators found their way to the conference to learn about the results from the SAMBA-project. The agenda for the final conference can be found in Appendix 2. Two photos from the final conference are seen in Figure 3-2.

6https://blogg.sintef.no/sintefenergy-nb/elkraftkomponenter/sommerforskere-pa-tur/ https://blogg.sintef.no/sintefenergy-nb/sommerstudenter-samba/ https://blogg.sintef.no/sintefenergy-nb/statnett-samba-prosjektet/ 7https://www.statnett.no/om-statnett/nyheter-og-pressemeldinger/nyhetsarkiv-2019/anleggsforvaltning-med-kunstig-intelligens/

https://bcove.video/2DmNozB

https://blogg.sintef.no/sintefenergy-nb/elkraftkomponenter/sommerforskere-pa-tur/

https://blogg.sintef.no/sintefenergy-nb/sommerstudenter-samba/

https://blogg.sintef.no/sintefenergy-nb/statnett-samba-prosjektet/

https://blogg.sintef.no/sintefenergy-nb/statnett-samba-prosjektet/

https://www.statnett.no/om-statnett/nyheter-og-pressemeldinger/nyhetsarkiv-2019/anleggsforvaltning-med-kunstig-intelligens/



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Figure 3-2 Steven Hagner, Industry Solution Executive ABB (left) and Jørn Foros, Research Scientist at SINTEF Energy Research (right), presents SAMBA results at the final conference


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4 Innovations The term innovation can signify both an activity and the outcome of an activity and is defined by The Organisation for Economic Co-operation and Development8 (OECD):

An innovation is a new or improved product (or combination thereof) that differs significantly from the unit's previous products and processes and that has been made available to potential users (product) or brought into use by the unit (process).

The definition uses the generic term "unit" to describe the actor responsible for innovations. This definition is also referred to by the Norwegian Research Council.

Statnett had data for use case testing, but this data was not easily available and not always at the required quality. It requires a stepwise innovation, to make data available first for testing and then for implementation into daily operation in Statnett. Figure 4-1 shows the 12 most important innovations from SAMBA.

Figure 4-1 SAMBA innovations

The innovations for Figure 4-1 are briefly explained in Table 4-1. Use case identification, description and testing have given the project an overview of available data and the quality of the data, which is an important starting point for the work to be done after SAMBA, as outlined in chapter 5.

8 http://www.oecd.org/sti/oslo-manual-2018-9789264304604-en.htm

http://www.oecd.org/sti/oslo-manual-2018-9789264304604-en.htm


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Table 4-1 Description of SAMBA innovations

Innovation Research activity Innovation description

Numerous use cases identified through workshops with Statnett employees, see Figure 1-3.

19 use cases described in detail and tested by Statnett, SINTEF Energy Research, GE Grid Solutions and ABB.

Failure model established for circuit breaker that enables estimation of risk of failure based on condition level and external/operational stresses.

Failure models can be established for any component. Circuit breakers were used to illustrate the value of such modelling and as input to technical-economic analysis. The value of failure models is aiding the transition from condition data to decision support.

Feasibility study describing a risk monitoring function including a description of dashboard functionality.

Options for Statnett concerning the risk monitoring function, including description of a stepwise process for implementation.

Prediction of hot spot temperature using supervised machine learning.

Predicted temperatures used for calculation of remaining life (DP-value).

Tested on Statnett data.

Confirming that it is possible to predict hot spot temperatures and calculate remaining life based on Statnett data. Issues concerning data quality identified.

Performed multivariate analysis on transformer gasses using Statnett data.

Identification of transformers with deviating gas values.

Multivariate analysis can be used to identify deviating gas values.

Further research, based on the results, will enable detection of transformers in the danger zone.

Data matrices made for use cases including where data can be found today, and which data is missing.

The available data can be used for analysis today with some limitations.

Prediction of cable and transformer temperatures using machine learning algorithms.

Tested on Statnett data.

Possible to predict temperature in cables and temperature based on Statnett data.

Further research, based on the results, will enable utilized in operation and anomaly detection.

Statnett use case identification

Circuit breaker failure model

Risk monitoring function

Prediction of transformer winding ageing

Multivariate analysis of transformer gasses

Overview of historical data availability

Cable and transformer temperature prediction


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Innovation Research activity Innovation description

Cost-benefit analysis performed using circuit breaker condition data.

Illustrated how deterioration level can be used in cost-benefit analysis for decision support purposes.

Algorithm to detect reignition has been developed and successfully tested on historical data from fault recorders.

Possible to detect reactor breaker reignition.

Health index schemes from SINTEF Energy Research, GE Grid Solution and ABB have been tested with data from Statnett.

Different data sets for ABB, GE and SINTEF Energy Research.

Possible to use health indices with data from Statnett today. Missing data and poor data quality limits the quality of the health indices.

80 line connectors tested in laboratory using the pulse current method. All were in good condition.

The pulse current method is applicable for sample testing to eliminate age-based replacements.

Mapping of SAMBA use cases and capabilities of the ICT architecture.

Overall Statnett reference architecture model for Big Data & Analytics described.

Foundation for further work on asset management ICT architecture.

Asset reinvestment analysis

Reactor breaker reignition identification

Health index transformers

Line connector condition assessment

ICT asset management architecture investigation


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5 Recommendations from SAMBA This chapter presents generalized recommendations from the SAMBA-project concerning data-driven asset management and data management. The latter is provided in relation to the four Vs of big data, see Figure 5-1. Specific recommendations from use case testing and for the risk monitoring function in Statnett can be found in [6] and [7], respectively.

One important recommendation from the SAMBA-project is to improve data availability and quality

5.1 Recommendations for data-driven asset management

Recommendations in this section is related to data-driven asset management.

• Clarify and restructure the decision processes for asset management to enable utilization of data as Statnett aspires to be a data-driven organization.

• Transform of the company culture to enable exploitation of big data in daily operation. • Create an analytic community for asset management. • Create or invest in better tools for asset management to improve the performance of Statnett`s

assets in terms of increasing reliability, reducing operational risks and reducing costs. • Employ new methods for analysis, including machine learning. • Perform systematic cost-benefit analysis of projects. Create and/or invest in tools that facilitate such

analysis. These tools should further allow for technical-economic analysis of new maintenance strategies and reinvestment projects.

• Consider using sample testing prior to replacement to check the condition of the components. • Continue the work on load/temperature prediction (more research is needed) • Dissect components taken out of service to learn more about the deterioration process and

investigate whether the condition was correctly estimated.

5.2 Recommendations for data management

Recommendations in this section is related to data management. Data management is defined in [8] as an administrative process by which the required data is acquired, validated, stored, protected, and processed, and by which its accessibility, reliability, and timeliness is ensured to satisfy the needs of the data users. The recommendations in this section is classified in accordance with the four Vs of big data.

Figure 5-1 Four Vs of big data


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5.2.1 Volume: the amount of data

The SAMBA project has identified, for selected use cases, which data is currently missing and not collected at Statnett. This assessment is done, of course, from the perspective of the methods chosen and applied in the different use cases. The recommendation is that in order to take into use more data-driven decision support tools to model for instance assets' condition, Statnett must collect more data in the future. This implies both storing data for more components as well as storing data with higher resolution than it is done today. For example, for transformers, higher resolution temperature data will probably be needed for future analysis as the thermal models are becoming more detailed. Ultimately this would also imply the installation of more accurate sensors on both 'old' and 'new' assets or for instance using drones and robots to collect data from inspections.

When collecting and storing more data, the following aspects must be thoroughly thought through as a part of the company's digitalization strategy:

• What would be the correct sampling rate for data harvesting? • Which sensors and transmitters are best for measuring the important parameters for critical assets? • How to ensure enough data storage capacity to receive new data from measurement? • Which data would be valuable to store from power system events, like lightening, short circuits,

galloping and so on? Such information can be important for residual lifetime estimation, even though the events did not cause an immediate failure of the components.

• What data might tomorrow's analysis demand? Such information can be found when participating in research projects and cooperation with best practice companies in the energy domain or outside.

5.2.2 Velocity: the speed at which the data is created, stored, analyzed and visualized

The scope for a risk monitoring function in Statnett is described in WP6 [7]. A prerequisite for implementing a risk monitoring function is to develop an analytic platform with more automatized analysis of asset failures and risks. This implies unprecedented data analysis at asset and system levels and thus increased data velocity. Risk monitoring implies that in its digitalization strategy, Statnett should thoroughly assess the following aspects:

• Which capabilities are necessary to perform online analyses and visualization? • How to ensure enough capacity for monitoring fast continuous data streams?

5.2.3 Variety: the many sources and types of data.

The SAMBA project has identified, for selected use cases, which data (of interest for the use case) is not collected at Statnett today. More data, and more diverse data, must be collected to take into use more data-driven decision support tools Statnett. This includes more external data sources.

Another aspect highlighted in the project is that data can be structured or unstructured and thus difficult to combine in models and algorithms. Today text or images are unstructured data and challenging to use in large volumes.

One option would be to create more structured instead of unstructured data. This would mean to find a way to interpret unstructured data and 'quantify' it for further modelling. SAMBA discussed for instance implementing condition grades from visual inspections to be further used in health index models or other analyses. Information collected during inspections can be used by experts to assess assets' condition. Information that is today collected at Statnett as plain text (free text) cannot be used without manual intervention and interpretation. The establishment of templates and instructions for determining the condition grades will improve the speed of assessment and thus that this information will regularly be included as a basis for decisions.


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However, the differences between unstructured and structured data might not be as important for analysis in the future, with the advances in machine learning and artificial intelligence.

5.2.4 Veracity: the trustworthiness of the data

Data quality and trustworthiness are crucial if more data should be used to reduce the uncertainty in decision making in Statnett and in all aspects studied in the SAMBA project. The following recommendations concern general features to be included in the future data management strategy:

• Perform systematic data cleaning and quality checks. • Establish calibration routines according to international standards for sensors, transmitters/

measuring loop/tools. • Highlight measurement traceability9. • Evaluate if "missing" data identified in the SAMBA use cases should be collected in the future. • Evaluate the use of transmitters instead of switches to identify variations in process values.

o Example from monitoring of SF6 pressure for gas insulated switchgear (GIS): Today, a pressure switch gives an alarm at low pressure, hence will only give information that the pressure is too low and no historical information about pressure. With a pressure transmitter (PT) installed, the exact pressure at any time will be monitored and any deviation from desired pressure level will be identified. Alarm level for Low (L) and Low Low (LL) pressure will be set according to recommendations and experiences.

• Investigate the need for using more resources on retrieving historical data. o For old assets there can be little data, easily available, on technical condition. However, in

some cases more historical (unstructured) information can possibly be found in paper documentation or reports (typically pdfs). This information can be retrieved, but the value versus the cost must be investigated prior to such efforts.

9 Measurement traceability is a property of a measurement result whereby the result can be related to a reference through a documented unbroken chain of calibrations, each contributing to the measurement uncertainty from https://www.bipm.org/utils/common/documents/jcgm/JCGM_200_2012.pdf

https://www.bipm.org/utils/common/documents/jcgm/JCGM_200_2012.pdf


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6 Recommendations for further work in Statnett The SAMBA-project has tested Statnett's ability to answer key questions in order to move towards a smarter data-driven asset management and through the use cases aided the choices on what the company should focus on in the future.

In order to make asset management more efficient and utilize the digitalized options available in a cost-efficient manner it is important for Statnett to have the right in-house competence. The road towards predictive maintenance (PdM 4.0) will imply that companies develop competence within reliability engineering and data science, which will be closely embedded in the asset management tasks. At the same time, it is important to cooperate with the best experts both inside and outside the energy business area to learn and exchange experiences and knowledge. Traditionally, the development of technology and analytical methods were often driven by industry and research institutes, which are important partners when seeking for efficient asset management solutions.

In the market there are many models and solutions that can be used, however Statnett's employees must have enough competence to explore the possibilities and implement new ideas and solutions by themselves. Statnett should have enough competence and knowledge about the assets and decide what information and tools/solutions to use as decision support.

An ICT architecture that fulfills asset management's needs must be established. A well-designed and well-functioning ICT-architecture should be able to tie all the elements together and is a premise for the asset management platforms of the future. Data must be collected, exchanged, aggregated, presented, visualized and stored to fulfill the need of the users.

Innovations and recommendations from SAMBA are i.e. included in Statnett's plans for asset management ("Plan for anleggsforvaltning").

Statnett has recently initiated an asset management digitalization program called FRIDA. The aim of the program is to make asset management and grid planning in Statnett more efficient, and to contribute to the digital transformation of Statnett. FRIDA will build on several results from SAMBA.

FRIDA will run during 2019 and 2020 including a R&D cooperation with Cognite10. For the first phase, lasting until autumn 2019, two asset management teams are established:

• Smart maintenance / asset health • Asset Monitoring Centre

Other Statnett initiatives following up SAMBA:

• Implementation of a sensor strategy • Development of work methods towards a digital workplace

10https://www.statnett.no/om-statnett/nyheter-og-pressemeldinger/Nyhetsarkiv-2018/statnett-oker-tempoet-i-den-digitale-transformasjonen/

https://www.statnett.no/om-statnett/nyheter-og-pressemeldinger/Nyhetsarkiv-2018/statnett-oker-tempoet-i-den-digitale-transformasjonen/

https://www.statnett.no/om-statnett/nyheter-og-pressemeldinger/Nyhetsarkiv-2018/statnett-oker-tempoet-i-den-digitale-transformasjonen/


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7 References

[1] "CEN/CENELEC/ETSI, Smart Grids Coordination Group Reference Architecture for the Smart Grid," 2012.

[2] M. K. Istad, E. Solvang, A. Verdal and M. Catrinu-Renström, "Status and further work - results from WP1 in the SAMBA-project," 2017.

[3] "https://www.statnett.no/contentassets/86093e07ebd4439bb6cabb607751fb9a/samba---wp1-report-summary-presentation.pdf," [Online].

[4] M. Istad et al., "Use case collection - SAMBA WP2 and WP3 report," Statnett, 2018.

[5] L. Lopacki, S. Fjogstad and J. Sigurd Aagenæs, "Future asset management archtecture - SAMBA WP4 report," 2018.

[6] M. Istad, J. Foros, E. Tveten, M. Kolstad, E. Solvang, G. Rosenlund, S. Hagner, M. G. Henderson and J.-L. Coullon, "Use case testing - SAMBA WP5 report," 2019.

[7] M. K. Istad, E. Solvang, M. Catrinu-Renström and J. Johnsen, "Risk monotoring function in Statnett - SAMBA WP6 report," 2018.

[8] [Online]. Available: http://www.businessdictionary.com/definition/data-management.html.

[9] IEC 62559-2: Use case methodology - Part 2: Definition of the templates for use cases, actor list and requirements list, 2015.

[10] J. Foros, "Trafotiltak - Analyse av behov for diagnose, vedlikehold eller utskifting av krafttransformatorer," SINTEF Energy Research, 2018.

[11] M. Ukkelberg, "Utilisation of machine learning in power transformer asset management," master thesis, NTNU, 2018.


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V1 List of disseminations Nr. 2016 1 Presentasjon av SAMBA i fellesmøte drift og marked Statnett 01.03.2016. Oslo. Arne 2 SAMBA prosjektet - Hva er det? Blogginnlegg Nettopp Statnett 27.04.2016. Arne 3 Presentasjon av SAMBA for UTT i Statnett, 20.10.2016. Arne 4 Presentasjon av SAMBA og forventninger på IKT samling Statnett Ullevål 20.10.2016 Oslo. Arne 5 Presentasjon av samba for eSmart 25.10.2016. Arne 6 Blogginnlegg Nettopp Statnett om SAMBA 01.12.2016. Arne 7 SAMBA presentasjon Smarte Nett Teknologisamling FoU Oslo 25.10.2016. Ane 8 SAMBA presentasjon for KUBE 25.10.2016. Oslo. Arne 9 Innlegg på Statnett sine hjemmesider om SAMBA 19.06.2016. Arne 10 Presentasjon av SAMBA på Sintef sine hjemmesider 26.07.2016. Maren

Nr. 2017 11 Presentasjon av SAMBA for Bane Nor 16.02.2017 Oslo.. Arne 12 Presentasjon av SAMBA for Plan og Analyse i Statnett 14.02.2017. Oslo. Arne 13 Presentasjon av SAMBA for Sunndal Næringsforening Sunndalsøra. 02.05.2017. Arne 14 Presentasjon av SAMBA for anskaffelser Statnett, 25.10.2017. Oslo. Arne 15 Presentasjon for KUBE 21.06.2017. Oslo. Arne 16 Presentasjon av SAMBA for Cineldi Trondheim 22.06.2017. Arne 17 Presentasjon av SAMBA for DDP i Statnett 17.10.2017. Oslo. Arne 18 Presentasjon av SAMBA for Skagerak Energi 20.11.2017. Oslo. Arne 19 Presentasjon av SAMBA for NordAM 25.10.2017. Oslo. Maren 20 Blogginnlegg Nettopp Statnett om SAMBA, 6.10.2017. Arne 21 SAMBA innlegg FoU konferansen Ullevål Stadion 20.04.2017. Arne 22 Presentasjon av SAMBA hos Hydro Energi 13.12.2017 Oslo. Arne 23 Innlegg på Statnett sine hjemmesider: FoU-prosjekt for bedre anleggsforvaltning. 13.03.2017. Arne 24 Status SAMBA på Sintef sine hjemmesider 21.03.2017. Maren 25 En smart vei til et smartere sentralnett - NVE. SAMBA presenter blant flere. 21.10.2017

Nr. 2018 26 Presentasjon på "moderne vedlikehold" 11. januar 2018. Maren og Jørn 27 Presentasjon av SAMBA prosjektet i paneldebatt: Making Sense of Sensor Data. Distributech San

Antonio (TX, USA) 25.januar 2018. Arne. 28 Presentasjon for den Norske Cigre komiteen i Trondheim tirsdag 24.april 2018. Arne 29 Presentasjon av SAMBA for NordAM, 23.03.2018. Oslo 30 Blogginnlegg ble publisert 26.april for WP2/3 og WP4 på Statnett Nettopp. Arne 31 Artikkel om SAMBA prosjektet i Energiteknikk nr. ¾ - mai 2018. Arne har blitt intervjuet. 32 Poster session på Cigre i Paris onsdag 29.august 2018. Group discussion meeting torsdag 30.08.18. 33 Sintef blogg om sommerstudenter 2018 som har jobbet med SAMBA prosjektet. 34 Sintef presentasjon i Cineldi 4.juni 2018. Tema: Tilstandsanalyse av transformatorer. Helseindeks. 35 Sintef presentasjon på Smartgridkonferansen 12.september 2018, Prediksjon av kabeltemperatur

vha maskinlæring – Erfaring fra SAMBA prosjektet til Statnett. 36 Presentasjon av SAMBA prosjektet for Kahramaa i Doha 11 og 12. september 2018. 37 Sintef blogg om sommerstudenter 2018, resultater fra arbeidet med SAMBA prosjektet.


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38 Statnett blogg om Sintef sommerstudenter 2018, resultater fra arbeidet med SAMBA prosjektet. 39 Euromaintenance 2018 i Antwerpen. 25.september 2018. Presentasjon av SAMBA prosjektet. 40 Presentasjon av SAMBA for ledergruppa i SINTEF Energi, 29.oktober 2918. Maren 41 Presentasjon av SAMBA brukermøtet for Transformatorer Stavanger 14.11.2018 Nr. 2019 42 Sluttkonferanse 23.januar 2019 SAMBA prosjektet. Oslo 43 Artikkel i Energiteknikk 29.januar. "Forvaltning i SAMBA takt"

http://energiteknikk.net/2019/01/forvaltning-i-samba-takt 44 Pressemelding fra Statnett 28.januar: "Anleggsforvaltning med kunstig intelligens".


45 Presentasjon for KL i SINTEF konsernet mandag 11.februar 2019 Trondheim. Maren 46 Foredrag på Tekna konferansen Brannforum 2019 Stavanger, Arne 47 Innlegg NFV, Olavsgård tirsdag 26.februar 2019. Arne 48 Presentasjon for KL mandag 11.mars 2019 Oslo. Arne 49 Maintech konferansen uke 13/2019 i Trondheim. Arne 50 Fagartikkel Smartere Anleggsforvaltning i bladet Energiteknikk nr.2/2019. Maren og Arne 51 Presentasjon SAMBA, allmøte SINTEF Energi, 20.mars 2019. Maren 52 Presentasjon SAMBA, FoU Statnett SF, April 2019.Oslo. Arne 53 Monitor X sluttseminar 9.mai 2019. Stockholm, Arne

http://energiteknikk.net/2019/01/forvaltning-i-samba-takt




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V2 Agenda for the final conference Topic Presenter Affiliation

Introduction, Why SAMBA? Sonja Berlijn Executive director R&D Statnett

Execution of the SAMBA project Arne Smisethjell Project Manager SAMBA, Statnett

Use case presentation for transformers Lars Lundgaard Jørn Foros

Chief Scientist, Sintef ER Research scientist, Sintef ER

Use case presentation for overhead lines Svein M. Hellesø Research scientist, Sintef ER

Use case presentation for cables Gjert Rosenlund Frøydis Oldervoll

Research scientist, Sintef ER Department Manager, Statnett

Use case asset management: Cost history analysis of maintenance and

reinvestments Technical-economic analysis of

maintenance and reinvestment projects Estimate of residual service life,

probability of failure and risk

Maria Renstrøm Eivind Solvang

Senior Analyst, Statnett Senior Scientist, Sintef ER

Use Case ABB Steven Hagner Industry Solution Executive, ABB

Use Case GE Sèbastien Balland Senior Grid Services ITO/ OTR Engineering Leader GE

ICT architecture Henrik Svalheim Enterprise Architect, Statnett

ICT architecture, IBM Per Bjarte Hanstveit Client Executive – IBM Energy Norway

WP6, risk monitoring centre Jørn Johnsen Ivar B. Hanssen

Senior Advisor and Program Manager for R&D, Statnett Section Manager, Asset Monitoring Center, Statnett

WP7, innovations and recommendations Arne Smisethjell Maren Istad

Project Manager SAMBA, Statnett Research scientist, Sintef ER

Summary and further work Kjerstin Bakke Senior Vice President Asset Management, Statnett

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Statnett SF Nydalen allé 33, Oslo PB 4904 Nydalen, 0423 Oslo Telefon: 23 90 30 00 Fax: 23 90 30 01 E-post: [email protected] Nettside: www.statnett.no

Documents

SAMBA WP7 report - Statnett · 5.2.4 Veracity: the trustworthiness of the data..... 22 6 Recommendations for further work in Statnett ... 23 7 References ... in Statnett, as well