Big Data Analysis. ipcgrid 30 March 16. Gordon Matthews, P.E. Bonneville Power Administration Technology Innovation

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1 Big Data Analysis ipcgrid 30 March 16 Gordon Matthews, P.E. Bonneville Power Administration Technology Innovation

BPA Overview Bonneville Power Administration (BPA) is a Federal Power Marketing Agency in Pacific Northwest BPA markets power from 31 Federal dams and the Columbia Generating Station Nuclear Plant

2 BPA Overview Bonneville Power Administration (BPA) is a Federal Power Marketing Agency in Pacific Northwest BPA markets power from 31 Federal dams and the Columbia Generating Station Nuclear Plant BPA operates more than 15,000 miles of transmission, including 4,735 miles of 500-kV lines BPA operates several large paths in the Western Interconnection California Oregon AC Intertie (4,800 MW), Pacific HVDC Intertie (3,100 MW), Northern Intertie (3,100 MW), and Montana Intertie (2,200 MW) 2

3 Problem statement PMUs generate a lot of Data But Information is the Point Data Rich Information Poor, need BPA statistics on data volumes, also need to mention data availability and quality issues Processing +18,000 measurements/sec in real time: looking for abnormal power system behavior Saving over 200,000 PMU measurements/second Generating Gb PI archive file every 30 minutes Saving Terrabytes: ~3 years measurements and calculations 3 months of data for dispatchers Event data saved indefinitely 3

4 BPA Big Vision Identifying anomalies & responding quickly Robust study environment Integrating disparate data for insightful information Current Big Data Projects: Data validation and correction using linear state estimation BPA as test lab for WECC effort Data mining for past events, archive walker Long-term phase angle base-lining Data anomaly detection and machine learning techniques Linear State Estimator (LSE): Lab Setup PMU Breaker Status via ICCP PMU PMU Lab PDC EPG DataNXT DataNXT replaces measured values with state estimates Estimates stored in temporary LSE Data Archive Estimate compared to raw signal for reasonability LSE Data Archive Raw PMU Data Archive Long Term Comparison 4

5 BPA Investment in PMU-driven/Linear SE Technology TIP050: Inter-Area Oscillation Damping Controls. Assesses oscillation damping risks in the Western Interconnection TIP051: Response-Based Voltage Stability Controls. Assesses voltage stability risks and researches methods to improve the voltage stability and controls in the Pacific Northwest power system. TIP299: Synchrophasor Linear State Estimator and PMU Data Validation and Calibration Explore development and implementation of data mining and validation tools for the incoming synchrophasor data. TIP305: Data Integrity and Situational Awareness Tools (DISAT) Focus on handling the substantial amounts of data provided from PMUs and will employ algorithms to clean this data TIP319: Multidimensional Learning on PMU Data for Event Detection, Characterization and Prediction Examines historical records of BPA s synchrophasor (PMU) data to perform event detection, classification, clustering, and outcome prediction using machine learning techniques. TIP324: Faster Than Real Time State Estimation with Forecast for Multiple Contingency Analysis Objective is to develop enhanced state estimation and contingency analysis functions to include forecasting information and execute faster than today s tool. TIP325: Real-Time System Operating Limits (SOL) Computation and Visualization for BPA Demonstration study to (1) compute the RT SOL (2) benchmark /validate computation results with off-line analyses, (3) develop interfaces with State Estimator TIP328: Real-Time Load Composition Estimation Develop, test, and validate a real-time load composition estimation methodology. TIP332: Open Source Platform for Accelerating Synchrophasor Analysis The flood of information arriving with the advent of PMUs is causing challenges and opportunities. This project is about leveraging open source and big data tools to enable greater researcher and practitioner productivity. This is a classic issue of Big 5

adapting algorithms to clean this data Jeff Dagle DISAT allows for near real time analysis of PMU data Developed in R with the User

6 TIP305 DISAT: Data Integrity Situational Awareness Tool Anomaly Detection Pacific Northwest National Laboratory (PNNL): Focus: Handling unprecedented volume of PMU data Brett Amidan Processes streaming PMU data (>50 PMUs) in near real-time Jim Follum Developing and adapting algorithms to clean this data Jeff Dagle DISAT allows for near real time analysis of PMU data Developed in R with the User Interface developed in Shiny. Uses data-driven, multivariate algorithms to discover Spectral coherence issues, Typical patterns, and Atypical events 6

7 Frequency B O N N E V I L L E P O W E R A D M I N I S T R A T I O N DISAT Steps Read raw PMU data Develop and then use data quality filters to clean poor quality data Remove bad data 1 day of 60 Hz PMU data (54 PMUs) = 26 GB b.amidan@pnnl.gov 7 March 31, 2016

Multivariate Statistical Algorithms Used in a Data Driven Approach to Discover

pattern Listing of the most atypical moments with the rationale behind what is

is over a threshold, indicating an atypicality Drill down plots showing which

8 Multivariate Statistical Algorithms Used in a Data Driven Approach to Discover atypical events Find patterns (group similar entities) Information about each pattern Listing of the most atypical moments with the rationale behind what is causing the atypicality Atypicality score display over time, showing when score is over a threshold, indicating an atypicality Drill down plots showing which variables were possibly responsible for the atypicality b.amidan@pnnl.gov 8 March 31, 2016

9 DISAT Viewer Atypicality Exploration Select atypical time period Produces a rationale explaining what was atypical during this time period March 31, 2016 b.amidan@pnnl.gov 9

10 DISAT Viewer Performance Envelopes Performance of the atypical variable +/- 15 minutes of the atypical time Underlying gray image plot represents typical behavior of the variable during that specific time period for that month Zoom in of the atypical variable +/- 2 minutes of the atypical time March 31, 2016 b.amidan@pnnl.gov 10

11 DISAT Viewer Raw Data Viewer Select Date/Hr/Min/Sec and length to view Select variable type and variable to plot View plot with data quality filter applied or not March 31,

12 DISAT Viewer Data Quality Display Select date and PMU View the data quality for each signal during the day (black and grays indicate data quality issues) March 31,

13 DISAT Viewer Spectral Coherence Select atypical time period View a spatial animation of the magnitudes of the oscillations View how atypicality score changes during the animation March 31,

14 Continued Development Tune the algorithm to find meaningful events Use statistical techniques to build models to predict different types of atypical events & look for precursors of atypical events Investigate sources of signals causing spectral coherence atypicalities Expand the Spectral Coherence algorithm to include multi-channel and multi-window Future Big Data Projects Parallel computing cluster for big data analysis Short term: MATLAB distributed computing server, central storage architecture Long term: Apache Spark, distributed storage architecture Integration of multiple data sources PMU, SCADA, weather, etc. Improved event detection and data mining algorithms Automated post-event analysis and report generation March 31,

15 Challenges beyond Technology: BPA led the deployment of PMU s in the West TI-Projects Significant Enabler Collaboration of Utilities, National Laboratories, Universities, Vendors PMU Data is now considered Critical Electric Infrastructure Information (CEII), and subject to: Federal Information Security Management Act (FISMA) Peak Reliability Coordinator (PeakRC) Universal Data Sharing Agreement (UDSA) Provisions of the FAST Act (H.R.22) Sharing PMU data now problematic for BPA Alternative? Academic PMU Network Each University that has PMUs Uploads the data to a common repository Create independent PMU Network for research Equivalent to regional utility PMU network Ultimately: More Objective Risk Assessment 15

Synchrophasor-based Solutions to Strengthen Grid Resilience and Reliability

Synchrophasor-based Solutions to Strengthen Grid Resilience and Reliability PAVEL ETINGOV Pacific Northwest National Laboratory 16th Annual i-pcgrid Workshop, San Francisco, March 28-30, 2018 PNNL SA 133255