Jenny Woodruff Innovation & Low Carbon Networks Engineer Steve Burns Innovation & Low Carbon Networks Engineer LCNF2013 Thursday 14 th November 2013

Transcription

1 NETWORK MONITORING DATA Using and manipulating data to predict network behaviour. Jenny Woodruff Innovation & Low Carbon Networks Engineer Steve Burns Innovation & Low Carbon Networks Engineer

2 Super Conducting Fault Current Limiter EAST MIDLANDS WEST MIDLANDS Isentropic Energy Storage SOUTH WALES SOUTH WEST Carbon Tracing

3 Agenda Our experience of network monitoring What we have learned from the data Data manipulation in the future - Big Data?

4 POWER SUBSTATION MONITOR COMMS MODULE INTERFACE PROCESSOR SENSOR Generally 1 to 8 LV legs

5 LEARNING POINTS SYSTEM DESIGN WPD has tested 10 monitoring variants Good off the shelf products now exist Bespoke Internal Design 2010 Redeploy Alternative Technology 2011 Off-the-shelf

6 LEARNING POINTS - SENSORS Rogowski Coils Sprung Clamp CT Solid State CT Split Core CT Solid State CT Solid State CT

7 Option 1 Split core CT around Busbars Requires Outage Option 2 Sensor around cable termination Can be done live

8 LEARNING POINTS - SENSORS Many types can be installed live No need for shutdown Issues with cable spacing Problematic for rigid sensors 0.5-1% Accuracy Rogowski offered greatest flexibility 2% Accuracy

9 LEARNING POINTS - POWER Existing Test Point Approved Busbar Clamps 1 2 Modified Fuse Carriers 3 X Direct Connection

10 LEARNING BEST PRACTICE Site Survey Comms Cable Runs Case Location Cable Lengths Housing LV Cabinet Type Equipment Push Fit Connections Data Storage IP Rated Case Installation Existing Holes 1-2 Hours Thin Design Plastic Range of Sensors Per phase per leg Power Specific Teams

11 What we have learned Early Learning Project Metering installed at LV cabinets plus single phase monitoring at customers homes. Cable impedance has negligible impact on the voltage. LV planning method predicted higher voltages than were seen. Some diversity in PV output due to orientation & tilt. Orientation differences smaller impact than differences in Individual customer s use. No over-voltages observed due to PV.

12 What we have learned PV s in Suburbia Voltages Harmonics and Distortion Power Export Within limits but at the higher end of the band. Still driven by the primary substation. Current waveform more like saw-tooth than sinusoidal. 3 rd harmonics increased and 5 th harmonics decreased. No reverse power flows seen.

13 What we have learned LV Network Templates Voltage monitoring >+10% 0.35% -6% to -10% % Under -10% % Confirmed expected mirroring of load profiles. Heat pumps have more impact on voltage than PV % readings within UK voltage limits. Majority of all excursions are high volts. Potential to lower voltages. A 2.5% voltage reduction + adopting EU voltage limits would improve voltage compliance to % of EU limits reduce energy consumption ( 315m pa -UK domestic customers) Increase voltage headroom for generation

14 What we have learned LV Network Templates PV monitoring PV units never exceeded 81% of installed capacity. Can predict output of nearby PV units using a monitored unit. Reference unit should be as average in size, orientation, tilt. Hidden demand estimates help optimise spinning reserve. A data link is in place and we are working towards providing a service.

15 What we have learned LV Network Templates Load monitoring Loads on substations can be estimated using a range of templates. Domestic loads are best suited to this approach. There will always be quirks that don t fit the pattern. Validation with other DNO data gives similar results. Confident classification correlates to better quality metrics.

16 Data Handling and Analytics Large data volumes in hard to use packages. Lots of filtering to find nuggets of information. Analytics tools essential.

17 Learning from our Data We are increasing the value and learning obtained from our data by sharing it. Data sharing agreements. Formalisation of agreement, not a barrier. Consistent with DPA approach. De Montfort university Falcon network model Centre for Sustainable Energy Load data Loughborough University Load data Reading University Load and voltage data

18 What about Big Data? Network Monitoring Data likely to increase. Smart Meter Data potentially large volumes. Do we need a Big Data approach?

19 What is Big Data? Volume Petabytes & larger Not just used for social media Banks, Healthcare, Retail, Pharmaceuticals etc. Amazon, Spotify, Ebay, Netflix, Ancestry.com (Petabyte = 1000 TB= Bytes) Velocity Rate of flow of data - exceeding existing capacity. E.g. to support live streaming. Variety Structured & unstructured data types. photos, audio, video, location data, complex simulations, 3d models. Items that are hard to categorise.

20 Big Data Utility Applications Smart Meter Data Mining (CSE, Bristol University, SSE) Rapid analysis of EV charging data economy charging allows DNOs control of charging. EDF have used predictive analytics to improve customer retention. Predictive analytics using smart meters for capacitor bank failure and smart meter failure. Potential for outage management, predictive asset maintenance, load forecasting, losses analysis etc.

21 Do DNOs need to adopt Big Data? Volume Velocity Variety Too early to tell. National analysis maybe. Yes - Real time network & outage management. No - Asset management and Planning. Smart meter and monitoring - structured. Images and video asset condition unstructured. Traditional architectures may cope

22 But Big Data architectures do work with smaller datasets. New tools reduce requirement for Java expertise. Cloud processing - rent not buy services for Big Data Barriers to entry are reducing Vendors are gearing up Potential should not be overlooked

23 Thank you

24 Backup

25 Cluster Architecture Traditional Networked Data Networked Data Networked Data Networked Data Big Data DP DP DP DP DP DP Networked Data Networked Data Networked Data Networked Data Networked Data Networked Data Distributed Processing Central Processing File Management System Google developed MapReduce Allows large datasets to be distilled before traditional analysis is applied.

26 Big Data Tools Apache PIG Hadoop Open Source software framework Implements MapReduce with HDFS Hadoop Data File System A high level programming language for data analysis. Auto-translate into MapReduce programs Can you guess which term is not associated with Big Data? HBase Mahoot Hive Splunk Zookeeper Oozie Amazon Elastic Compute Cloud Chukwa