Big data technology is used to analyze large and complex datasets from sources in electrical power systems. This data comes from phasor measurement units, smart meters, and other intelligent electronic devices. The data has characteristics of volume, variety, and velocity. It is analyzed to extract useful information for applications like faster decision making, fraud and fault detection, load forecasting, and power generation management. Some disadvantages include potential hacking or cybersecurity issues. Overall, big data analysis provides benefits for managing the smart grid but also faces security challenges.

1. Applications of Big Data in Electrical
Energy System

2. Contents
• Introduction
• Big data technology
• Characteristics
• Analyzation of big data
• Sources of big data
• Role of big data in power systems
• Advantages and disadvantages
• Applications
• Conclusion

3. Introduction
Big data

4. Big data technology
• Big data technology is an emerging technology
which applies to data sets where data size is so
large and common data-related technology tools
are hard to capture, manage, and operate
• Big data technology provides architectures for
complex power grid, effective data analysis to
aware the unfavorable situation in advance, and
various data processing methods

5. Characteristics of big data

6. Volume:
• Big Data indicates huge ‘volumes’ of data that
is being generated on a daily basis from
various sources like social media platforms,
business processes, machines, networks,
human interactions, etc.

7. Variety:
1.Structured:
Structured data are those which are stored in
an order.
2.Unstructured:
The data has no clear
format.
3.Semi- structured:
The data sometimes
look structured and
or sometimes
unstructured.

8. Velocity:
Velocity refers to the increasing speed at which big
data created
and the increasing
speed at which data
is needs to be stored
and analysed.
Veracity:
Data veracity, in general, is how accurate or truthful
a data set may be.

9. Analysing big data

10. Data generation:
This phase refers to the big data generation
processes and the sources of big data with
various types, characteristics, and origins.
Data acquisition:
This phase concerns big data aggregation to
obtain and classify the resulted information for
further phases. Data collection, transmission,
and preprocessing are the most significant
aspects of this phase.

11. Data storage:
This phase aims to store and manage big data
for further processes and applications.
Data analysis:
This phase facilitates the analytic approaches
to analyze the gathered data by using
inspection and modeling methods to prepare
classified and extracted information from the
collected raw data. This phase is the most
important stage in big data system aiming at
useful information extraction for further
decision-making processes.

12. Sources of data in power system
The data can be generated through diverse
measurements acquired by Intelligent Electronic
Devices (IEDs) in the smart grid:
1. Data from Phasor Measurement Units (PMUs)
for situation awareness.
2. Data from energy consumption measured by
the widespread smart meters.
3. Data from management, control and
maintenance of device and equipment in the
electric power generation, transmission and
distribution in the grid.

13. Intelligent electronic devices
Smart meters: Smart meters
typically record energy
hourly or more frequently,
and report at least daily.
PMU: It is a device used to
estimate the magnitude
and phase angle of an
electrical phasor quantity
in the electricity grid.

14. SCADA (supervisory control and data
acquisition)
• A SCADA system is a common industrial process automation
system which is used to collect data from instruments and
sensors located at remote sites and to transmit data at a central
site or monitor or controlling purpose

15. Role of big data
The data obtained from PMU’S, smart meters and other IED
devices is analyzed by the process of “analyzation of big data”
and can help improve the smart grid management to a higher
level.

16. Advantages
1.Faster, better decision making.
2. Fraud Detection and Fault detection.
3.Load forecast.
4.Power Generation Management.
Disadvantages
1.Hacking or cyber-security.

17. Applications

18. Continued…

19. conclusion

20. Any queries