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Computational Frameworks for HiPerDNO

Computational Frameworks for HiPerDNO. Dr David Wallom University of Oxford On behalf of HiPerDNO Workpackage 1, including IBM, GTD, Korona and Oxford. Overview. IT requirements Messaging technologies to collect data Data mining to assess and improve DSE and CM

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Computational Frameworks for HiPerDNO

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  1. Computational Frameworks for HiPerDNO Dr David Wallom University of Oxford On behalf of HiPerDNOWorkpackage 1, including IBM, GTD, Koronaand Oxford HiPerDNO: High Performance Computing for Smart Distribution Network Operation

  2. Overview • IT requirements • Messaging technologies to collect data • Data mining to assess and improve DSE and CM • Platform to provide AMI, DSE and CM HiPerDNO: High Performance Computing for Smart Distribution Network Operation

  3. Requirements for the IT System Efficient messaging layer for data acquisition with real-time quality of service Computational system supporting R/T DSE, on-demand CM and historical analysis Reliable algorithms to extract features within specified time limits with limited misidentification Upgrading DSE software to take new numerical techniques into account Secure infrastructure protecting DNO critical data and ensuring consumer privacy Low power consumption  use innovative technology HiPerDNO: High Performance Computing for Smart Distribution Network Operation

  4. Input Data Collection &transmission to computational services • New High Speed Messaging technology to provide the following benefits when compared to standard communication technologies: • Very high performance • Support for differentiated Quality of Service and near real-time data distribution • Pro-active data stream management, dynamic activation of flow control and data aggregation HiPerDNO: High Performance Computing for Smart Distribution Network Operation

  5. AMI Networking Topology in the domestic domain Utility Control Center B InfoBridge module B Downstream – control data Upstream – mostly metering (conflated) data • Intermediate Layer InfoBridge B B Downstream – control data Upstream – mostly metering (conflated) data B Home Layer InfoBridge HiPerDNO: High Performance Computing for Smart Distribution Network Operation

  6. DSE for Automatic Functions Picture courtesy EDF R&D HiPerDNO: High Performance Computing for Smart Distribution Network Operation

  7. Data Mining Devvelopment • Data mining algorithms will be implemented for the following tasks (and more): • Distributed feature extraction • Classification • Clustering • Anomaly detection • Finally, specific DNO applications will be developed, such as: • Failure detection • Optimized consumption. HiPerDNO: High Performance Computing for Smart Distribution Network Operation

  8. Data Repository Configuration • Platform based on relevant open source solution to support long term scalability e.g. • Hadoop Distributed File System • Inherent design against data loss • Multi-site mirroring of data • Raw captured data is never altered, only annotated with links to new generated products • Metadata labeling to identify source, enabling historical data analysis • Query interface producing data as stream using identical interface to live data streaming HiPerDNO: High Performance Computing for Smart Distribution Network Operation

  9. Computational Platform • Based on a design used for calibration of the global Square Kilometer Array project • Data Distribution and Control Hub DDCH constantly running • AMI, DSE and CM processes contained in application specific pipelines • Automated pipeline launching dependant on input metadata at the DDCH • Pipeline scaling dependant on load • Pipeline closure at end of data stream • Pipeline uses a standard interface to allow wrapping of different types of DSE, AMI or CM software to run HiPerDNO: High Performance Computing for Smart Distribution Network Operation

  10. Modular Computational FrameworkSupporting Real-time Analysis DSE Data Sources Data Distribution and Operation Control Hub DNO System Data Repository HiPerDNO: High Performance Computing for Smart Distribution Network Operation

  11. Modular Computational FrameworkSupporting Real-time Analysis DSE Data Sources Data Distribution and Operation Control Hub DSE1 DSE1 DSE1 DSE2 DSE2 DSE2 DNO System Data Repository DSE3 DSE3 DSE3 HiPerDNO: High Performance Computing for Smart Distribution Network Operation

  12. Modular Computational FrameworkSupporting Real-time Analysis DSE Data Sources Sub Station Data Distribution and Operation Control Hub Sub Station 2 DSE1 DSE1 DSE1 DSE2 Sub Station2 DSE2 DSE2 DNO System Data Repository DSE3 DSE3 DSE3 HiPerDNO: High Performance Computing for Smart Distribution Network Operation

  13. Modular Computational FrameworkSupporting Real-time Analysis DSE Data Sources Sub Station Cable Data Distribution and Operation Control Hub Sub Station 2 DSE1 DSE1 DSE1 Cable1 DSE2 Sub Station2 DSE2 DSE2 DNO System Data Repository DSE3 DSE3 DSE3 HiPerDNO: High Performance Computing for Smart Distribution Network Operation

  14. Modular Computational FrameworkSupporting Real-time Analysis Network failure DSE Data Sources Sub Station Cable Data Distribution and Operation Control Hub Sub Station 2 DSE1 DSE1 DSE1 Cable1 DSE2 Sub Station2 DSE2 DSE2 DNO System Data Repository DSE3 DSE3 DSE3 HiPerDNO: High Performance Computing for Smart Distribution Network Operation

  15. Modular Computational FrameworkSupporting Real-time Analysis Network failure DSE Data Sources Sub Station Data Distribution and Operation Control Hub DSE1 DSE1 DSE1 Sub Station 2 DSE2 DSE2 Sub Station2 DSE2 DNO System Data Repository DSE3 DSE3 DSE3 HiPerDNO: High Performance Computing for Smart Distribution Network Operation

  16. Modular Computational FrameworkSupporting Real-time Analysis Network failure DSE Data Sources Sub Station Data Distribution and Operation Control Hub DSE1 DSE1 DSE1 DSE1 Sub Station 2 DSE1 DSE1 DSE1 DSE2 DSE2 DSE2 DSE2 DSE2 DSE2 Sub Station2 DSE2 DNO System Data Repository DSE3 DSE3 DSE3 DSE3 DSE3 DSE3 DSE3 HiPerDNO: High Performance Computing for Smart Distribution Network Operation

  17. Modular Computational FrameworkSupporting Real-time Analysis DSE Data Sources Sub Station Cable Data Distribution and Operation Control Hub Sub Station 2 DSE1 DSE1 DSE1 Cable1 DSE2 Sub Station2 DSE2 DSE2 DNO System Data Repository DSE3 DSE3 DSE3 HiPerDNO: High Performance Computing for Smart Distribution Network Operation

  18. Modular Computational FrameworkSupporting Real-time and Historical Analysis DSE Data Sources Data Distribution and Operation Control Hub DSE1 DSE1 DSE1 DSE2 DSE2 DSE2 DNO System Data Repository DSE3 DSE3 DSE3 HiPerDNO: High Performance Computing for Smart Distribution Network Operation

  19. Modular Computational FrameworkSupporting Real-time and Historical Analysis DSE Data Sources Sub Station Data Distribution and Operation Control Hub Sub Station 2 DSE1 DSE1 DSE1 DSE2 Sub Station2 DSE2 DSE2 DNO System Data Repository DSE3 DSE3 DSE3 HiPerDNO: High Performance Computing for Smart Distribution Network Operation

  20. Operational Models • DSE • Constantly running stream of network monitoring information • Variable load due to changing network complexity through variation in number of parallel pipelines • Condition Monitoring • Monitoring stream started for system node • Stream passed to data store and concurrently connected to specialized processing pipeline • Each type of network node will have its own application hosted in a specific pipeline HiPerDNO: High Performance Computing for Smart Distribution Network Operation

  21. Conclusion Specialised high performance system for data collection from the DNO Connected to operationally specific computational architecture giving flexible, on-demand processing of priority data sources HiPerDNO: High Performance Computing for Smart Distribution Network Operation

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