Role of Big Data Analytics in Power System Application

1. Asst. Professor.,
Dept. of Electrical and Electronics Engineering
Ravi V Angadi M.Tech, AMIE, LMISTE, MIEEE
Prepared by
Role of Big Data Analytics in Power System
Application

2. 2
1. Introduction.
2. Big Data Analytics in Various Sectors.
3. Big Data Role in Power System.
4. Sources of Big Data in Power system.
5. Big Data Characteristics in Power system.
6. Important applications of Big Data in Power System Sector.
7. Analytics Techniques for Power System.
8. Machine Learning & Big Data Analytics for Power System application.
9. Application of Big Data Analytics in Power Systems flow diagram.
10. Conclusion
Contents:

3. 3
1. Introduction:
 Big Data does exist everywhere.
 Curiosity - Big Data – Soaring - Past few years.
 Some mind boggling Facts, as per the Forbes report that every minute;
i. 4.15 Million – Watch – You Tube Videos.
ii. 4, 56, 000 – Sends Tweets – Tweeters.
iii. 46, 700 – Photos – Instagram.
iv. 5,10,000 & 2,93,000 – Comments & Status updates- Facebook.
 Huge chunk of Data is produced by such activities.
 The constant creation of data using;
i. Social Media.
ii. Business Application.
iii. Telecom & Various other domains. Leads to the formation of Big Data.
 Evaluation of Big Data.
Ex: Floppy & CD’s.

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Cont.,
 The common Myth associated with Big Data – Size or Volume of Data - But it
is not.
 Then What is Big data …?
 Big Data refers to Large amount of Data which Pouring in form of Various
Data Sources & has different formats.
 Big Data – Varied in nature – Traditional Data Base – Incapable of handling
this data.
The growing market revenue of Big Data in
billion U.S.

5. 2. Big Data Analytics in Various Sectors

6. Cont.,
Big Data in Power & Energy System

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3. Big Data Role in Power System
The power system - various challenges - technological innovations.
 Power demand is increasing and assets are Ageing.
 It should provides Clean, Secure and uninterrupted power
 Smart Grid should be able to Handle large data intelligently.

8. 8
Cont.,
 Outage management based on meter
information instead of consumer call -
Customer Satisfaction.
 Electricity cost can be implemented similar to
stock market in smart grid.

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4 Sources of Big Data in Power System

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5. Big Data Characteristics in Power system.

11. 4.1. Volume:
SL. No Data Class Data Source Volume
1
Utility
Measurements
Phasor Measurement Unit (PMU) 30GB per day
Smart Meter (SM) 120 GB /day
Intelligent Condition Monitor (ICM) 5GB per day
Digital Fault Recorder (DFR) 10MB per fault
2 Weather Data
Radar 612MB/day per radar Scan
Satellite At least 10GB per Day
Automated Surface Observing System (ASOS) 10MB/day per station
National Lightning Detection Network (NLDN) 40 MB/day
Weather Forecast Model (WFM) 5-10 GB/day per Model
3
Vegetation and
Topography
Ecological Mapping System of Texas 2.7 GB per day
Texas National Resources Information System 300 GB for Texas
Light Detection and Ranging 7 GB for Harris Co.

12. 4.2 Velocity:
SL. No Data Class Data Source Volume
1 Utility Measurements
Phasor Measurement Unit (PMU) 240 samples/sec
Smart Meter (SM) Every 5- 15 minutes
Intelligent Condition Monitor (ICM) 250 samples/sec
Digital Fault Recorder (DFR) 1600 samples/sec
2 Weather Data
Radar Every 4-10 min
Satellite Every 1-15 min
Automated Surface Observing System
(ASOS)
Every 1 min
National Lightning Detection Network
(NLDN)
During lightning
Weather Forecast Model (WFM) 15min-12 hours
3 Vegetation and Topography
Ecological Mapping System of Texas Static
Texas National Resources Information
System
Static
Light Detection and Ranging Static

13. 4.3. Variety :
Big Data is generally categorized into three different varieties Structured Data, Semi-
Structured Data and Unstructured Data.
SL. No Data Class Data Source
1 Utility Measurements
Phasor Measurement Unit (PMU)
Smart Meter (SM)
Intelligent Condition Monitor (ICM)
Digital Fault Recorder (DFR)
2 Weather Data
Radar
Satellite
Automated Surface Observing System (ASOS)
National Lightning Detection Network (NLDN)
Weather Forecast Model (WFM)
3 Vegetation and Topography
Ecological Mapping System of Texas
Texas National Resources Information System
Light Detection and Ranging

14. 4.4. Veracity:
SL. No Data Class Data Source
Veracity
(Accuracy)
1
Utility
Measurements
Phasor Measurement Unit (PMU) Error < 1%
Smart Meter (SM) Error < 2.5 %
Intelligent Condition Monitor (ICM) Error < 1%
Digital Fault Recorder (DFR) Error < 0.2 %
2 Weather Data
Radar 1.2 dB ms-1
Satellite VIS<2% ; IR <1-2K
Automated Surface Observing System (ASOS)
T-1.8oF , P<1%, Wind
speed5%, RR- 4%
Precipitation
Weather Forecast Model (WFM) Varies by parameter
4.5. Value:
The amount of data produced in the system represents the volume in the Big Data.
Examine how the data adds device value to work effectively.
The valuable data helps in predicting future generation and demand.

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6. Important applications of Big Data in Power System Sector
 Performance analysis of generation.
 Management loads with response to requests.
 Consumption of energy and measurement performance.
 Analysis of Consumer behavior.
 Economic constraints and social effect assessment and review.
 Scientific reasoning development of decision making.
 Tariff analytics and incentives implementation analysis.
 Infrastructure optimization of grid analysis.
 Service quality analysis.
 Generation and demand forecast under high uncertainties.

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7. Big Data Analytics for Power System
a. Electrical power integration and control in large data
network:
Data Fusion &
Data Integration
Relational
Database & Non
Relational
Database
Data Extract-
Transform-Load
Data warehouse
Integration & Management Technologies of
Electric Power in Big Data
• These are commonly used in areas such as
i. Power System network, ii. Electric
power load prediction and etc.
Correlation
Analysis
• They are commonly used in various power
system areas; i. Condtioning and
monitoring of facilities. ii . Assessment of
system stability and so forth.
Machine
Learning
• Which are mainly used in power systems
to predict or view.
Data Mining
Data analysis techniques for electric power system
b. Electric power network data analyzes
technique.
c. Electric power large data processing technology. Distributed
Computing
Large Scale
Distributed
storage & data
Memory
Computing
Efficent Data
reading &
Processing
Stream
Processing
Real time arrival,
Uncontrolled
speed & Size data
d. Big data visualization technology in the
electrical power system
Data processing techniques of electric power in big data

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8. Machine Learning & Big Data Analytics tools Power System application
 The machine learning tools used for the treatment of huge amount of
distributed data available at source in power system.
 The top most popular and accessible tools for big data analytics based on
libraries such as;
 Apache Spark.
 Hadoop.
 Cassandra.
 Storm.
 Rapid Miner.
 Mongo DB.
 High Performance
Computing Cluster (HPCC).
 Weka
 Python
 R Computing Tool.
 Neo4j.
 Datawrapper.
 Lumify
 Talend.
 Rapid miner and many
more.

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19. 9. Supervised Learning & Classification Methods.
 In supervised learning, models are trained using labelled dataset, where the model learns
about each type of data.
 Once the training process is completed.
 The model is tested on the basis of test data (a subset of the training set), and then it
predicts the output.
Classification:
 Classification algorithms are used when the output variable is categorical.
 Which means there are two classes such as Yes-No, Critical- Non Critical, High-Low, etc.
Classification Methods:
i. Random Forest.
ii. Decision Trees.
iii. Logistic Regression.
iv. Support vector Machines

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10. Application of Big Data Analytics in Power Systems flow diagram

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11. Conclusion
 Potential applications of Big Data in power system.
 Various sources of Big Data.
 Big Data Analytical can improve;
Awareness and Response times both for device operators.
 It help power system sector to recognize and respond proactively to achieve
the sustainable operation and maintenance at various stages to keep power
system in secured condition and help to avoid blackouts.

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