data driven analysis_of_southern_grid_of_india

Data Driven Analysis of Southern Regional Grid of India

by

Sarasij Das

.

Based on Thesis “Power System Data Compression For Archiving”

http://etd.ncsi.iisc.ernet.in/handle/2005/572

Aim

Understanding the parameter interrelations of

Southern Regional Grid of India (using SCADA

data)

Highlight the challenges of data driven analysis of

a power grid

Introduction

• Power system instrumentation - transforming from analog to digital• Huge data being generated• Data : an asset • SCADA not designed to handle/analyze huge system data• Data analysis: new tool to understand power system better

• Indian power system going through major changes

• SRLDC - apex body responsible for integrated operation of power system in Southern Regional Grid of India

• 320 RTUs involved in system monitoring across Southern region.

• Huge system information generated

• Presently most of the recorded data remains un-used

• Recorded system data can be analyzed for better system understanding, decision making and optimizing system performance.

Context and Scope

Collected Data Description

• In SRLDC, data stored in compact discs Excel files• Collected data duration year 2005-06• Data logging interval 1 minute – steady state data• Data of four system parameters are used for study

1. Voltage - 26 buses of 400 KV – 1 KV precision

2. MW generation – 60 units – 1 MW precision

3. MVAr generation - 88 units – 1 MVAr precision

4. Frequency - 0.01216 Hz precision

Studies Performed

• System frequency Vs. average system voltage• System frequency Vs. total system demand• Total system demand Vs. average system voltage• Total system demand Vs. Total MVAr generation• Total MVAr vs. average system voltage• MVAr injection averaged at generation bus voltage• System demand averaged at frequency

Average system voltage

Sys

tem

fr

eque

ncy

On 06/06/2006

System frequency

Tot

al S

yste

m D

eman

d On 16/04/2006

Average system voltage

Tot

al S

yste

m D

eman

d On 19/01/2006

Total MVAr generation

Tot

al S

yste

m D

eman

d On 19/01/2006

Average system voltage (kV)

Tot

al M

VA

r ge

nera

tion

On 19/01/2006

Bus voltage (kV)

MV

Ar

gen

erat

ion

On 06/06/2006 - Scatter plot

Bus voltage (kV)

MV

Ar

gen

erat

ion

aver

aged

at e

ach

volt

age

On 06/06/2006

Bus voltage (kV)

MV

Ar

gen

erat

ion

aver

aged

at e

ach

volt

age

On 13/03/2006

Bus voltage (kV)

MV

Ar

gen

erat

ion

aver

aged

at e

ach

volt

age

March 2006

Sys

tem

Dem

and

aver

aged

ov

er e

ach

freq

uenc

y

System frequency

Month - December 2005

Sys

tem

Dem

and

aver

aged

ov

er e

ach

freq

uenc

y

System frequency

Month - April 2006

Change in frequency

Cha

nge

in T

otal

Sys

tem

Dem

and

in c

onse

cut i

ve in

stan

tMonth - December 2005

Change in frequency

Cha

nge

in T

otal

Sys

tem

Dem

and

in c

onse

cut i

ve in

stan

tMonth - March 2006

Challenges

• Presence of outliers

• Incomplete data• Inaccurate time stamping of data

RCHR bus voltage on 02/01/2006 for the period 12:59 to 13:29

Conclusion Data analysis can reveal interesting system characteristics

Outliers, incomplete data pose challenges towards successful mining

Utility data archiving system should be designed keeping data analysis requirement as a criteria