Data Modelling & Analytics. Patrick Bonk

Transcription

1 Data Modelling & Analytics Patrick Bonk

2 Introduction VicWater & Intelligent Water Networks Program A Workshop outlining current realities The SCADA Situation Sampling SCADA Data SCADAWatch: Web-Based Watchdog Case Study: Pipe Burst and an Emptying Tank Built-in Querying/Analytics Creating Alerts from Multiple Data Streams The Mechanism behind the Watch Dog Integration with Hydraulic Models Mass Balance and Non-Revenue Water Relating SCADAWatch to Current Realities

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5 VicWater Intelligent Water Networks Program The Intelligent Water Networks Program is a partnership between VicWater 17 Victorian water corporations Department of Environment, Land, Water and Planning The IWN investigates new technologies and innovations to meet common challenges such as population growth, ageing infrastructure and climate variability in a more efficient manner. Victorian Water Industry Flagship Program Participation Hosted a session at Ozwater to explore issues.

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14 SCADA Situation Existing SCADA is a process bottleneck Too many steps to obtain/report on data Low confidence level on data Resource intensive > manual data analytics Costly to customize SCADA tells you what is happening but not why

15 SCADA Situation Agency staff outside Operators don t have access to realtime SCADA data Operators only have access to preconfigured data Many people would benefit from open access (read only) to SCADA information Time-sensitive data gets stale quickly

16 Emergence of BIG Data

17 Big Data has Big Challenges I ve got some data from a flow meter data you might want = 31.6 million data points I wonder what that looks like 1 sensor 1 second reads 1 year

18 30 Excel Spreadsheets!

19 A solution to Big Data: Data Sampling Data sampling is a statistical analysis technique used to select, manipulate and analyze a representative subset of data points in order to identify patterns and trends in the larger data set being examined. This needs to be: Dynamic and easily customized Able to deal with null or void readings

20 Sampling SCADA Data - Locally hosted Data SCADA Historian (any OLEDB Database) SCADAWatch Database (Microsoft SQL Server)

21 3 8 hours

22 6 3 hours

23 3 15 minutes

24 21 5 minutes

25 20 1 second

26 Making Sense of Sampled Data

27 Introducing SCADAWatch: - Web Based Watch Dog - Historical Trending, Built-in Analytics and Querying Abilities - Continuously up-to-date Hydraulic, Water Quality and Energy Modelling Potential - Utilize Existing IT Infrastructure and Skillsets within an organization. - Distributed to any device PC or Smart Device.

28 Case Study: Watch Dog Scenario A real incident Pipe burst went unnoticed for 4-hours Happened on a Saturday Detected by public $1,000 potentially saved if response time cut to 2-hours. PR risk waste of water, inefficient operations What was seen by operations Tank level dramatically dropped but unnoticed while within normal operating bands. When tank level was flagged cause was thought to be potentially due to a faulty pump.

29 Raw Spreadsheet Data Flow Data: Feb 4-8, 2015, Approx. 4 min Readings Tank Level Data: Oct 19, April 10, 2015 Approx. 2 hour Readings

30 Geospatially Located Sensors A platform where sensors are geospatially located Web-browser

31 Advanced Analytics on the fly Normal Operations for 5 days of Flow Data Provided. Variance does not exceed 15 Normal determined by review of available data A large historical data study was not required.

32 Advanced Analytics drive alerts During the burst event (Var,5) spikes to 600 The burst created a significant increase in Variance.

33 Creating Alerts from Multiple Data Streams Raw Flow Data 4 Minute Readings Live Data Push 1 data point every 30s Live Data Feed 8x Faster Inflow 1 Inflow 2 Outflow

34 Built-in Querying/Analytics Regression Slope Triangular Moving Average Moving Average Variance Standard Deviation Rate of change. Derived Feed

35 The Watch Dog Mechanism Detected burst in 8 Minutes

36 Dashboards and Data Distribution Quick Sharing of Pipe Burst & Tank Emptying Scenarios

37 Dashboard: Pump Station Summary

38 Dashboard: Range of Visual Data

39 Adjusting the Dashboards

40 Pre-configured Graphing Functionality

41 Spreadsheet Average Values Candlestick Plot O/H/L/C/Ave Sampled Hourly, Daily, Weekly, Monthly Intervals

42 Integration with Hydraulic Model

43 SCADAWatch Connect

44 Mass Balance Scenario Sample System - 4 Pressure Zones - Bound by PRVs - Single Water Treatment Plant

45 Mass Balance & NRW Schematic Exported from InfoWater

46 California IWN Drivers Yorba Linda W.D. Pop. 80, sq. km. Santa Ana Orange County Seat Pop. 330,000; US 57 th largest 71.2 sq. km. Rancho California W.D. Pop. 148, sq. km.

47 California IWN Drivers Berkeley County Pop. 194, sq. km.

48 Significance of Web Watch Dog An existing SCADA vendor and Implementation Consultant. Richer Data in the hands of Operations, Engineers & key decision makers. Utilizes existing IT infrastructure, systems and skillsets. Web-based, built-in querying/analytical functionality for live/historical time series analysis. Connects to existing hydraulic model for Continuous Calibration and Event Response Management.

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50 Information in the hands of decision makers

51 Thank You