A DATA-DRIVEN APPROACH TO ANALYZE

THE SPATIAL AND TEMPORAL VARIATIONS

IN THE POWER DISTRIBUTION GRID

QIWEI ZHENG, PENG XIAO

UNIVERSITY OF CONNECTICUT

A Data-Driven Approach to Analyze

the Spatial and Temporal Variations

in the POWER DISTRIBUTION GRID

From Wikipedia

A Data-Driven Approach to Analyze

the Spatial and Temporal Variations

in the POWER DISTRIBUTION GRID

A Data-Driven Approach to Analyze

the Spatial and Temporal Variations

in the POWER DISTRIBUTION GRID

* **

1. Motivation of The Concept

2. Feasibility

3. Ultimate Goals

4. Data Processing

5. Future Application on Visualization

Figure 1

OVERVIEW� The project aims at providing a stable and

healthy distribution grid by optimizing its

configuration through the analysis of past

sampling data.

� A feeder line with minimum fluctuation is considered as steady and robust.

� Also, an entire distribution grid needs to have

small difference between peak and valley as

possible.

MOTIVATION

1. Seasonal change (summer and

winter)

2. Different patterns on

holiday/weekends

3. Power source in distribution grid

(new energy/renewable energy

source)

From Wikipedia

• Adapt the change

• More stable and healthy power network

DAS220kV

10kV

10kV

Substation

A

Substation B

RMU

#1

RMU

#2

RMU

#3

RMU

#4

RMU

#5GA01

GB01

G101

G203

G105

G103

G301 G302 G505

G202G201

G303

G401

G406

G501

G403

G506

LD 1 LD 3 LD 5

LD 2 LD 4

Figure 1

Figure 2

PREDICTION

Last Year ���� Seasonal Prediction

Weekdays / Weekends

Recently data Adapt Changes

Spring Mar. 21 ~ June 22

Summer June 23 ~ Sept. 23

Fall Sept. 24 ~ Dec. 22

Winter Dec. 23 ~ Mar. 20

DATA

Figure 1

DATA

• Every 15mins

• 96 points a day

• Auto-regressive Moving Average Module

• Probability distribution

ϕi=

Ni

n i = 0,1,2...10

0

5

10

15

20

25

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Spring weekdays Load Graph For Unit 1

0

5

10

15

20

25

30

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Spring weekdays Load Graph For Unit 2

220kV

10kV

10kV

Substation A

Substation B

RMU #1

RMU #2

RMU #3

RMU #4

RMU #5

GA01

GB01

G101

G203

G105

G103

G301 G302 G505

G202G201

G303

G401

G406

G501

G403

G506

LD 1 LD 3 LD 5

LD 2 LD 4

Figure 2

SIMPLEST APPROACH

220kV

10kV

10kV

Substation A

Substation B

RMU #1

RMU #2

RMU #3

RMU #4

RMU #5

GA01

GB01

G101

G203

G105

G103

G301 G302 G505

G202G201

G303

G401

G406

G501

G403

G506

LD 1 LD 3 LD 5

LD 2 LD 4

AUTO CORRELATION

220kV

10kV

10kV

Substation A

Substation B

RMU #1

RMU #2

RMU #3

RMU #4

RMU #5

GA01

GB01

G101

G203

G105

G103

G301 G302 G505

G202G201

G303

G401

G406

G501

G403

G506

LD 1 LD 3 LD 5

LD 2 LD 4

CORRELATION (CONT.)

GIS APPROACH

Figure 5

GIS APPROACH

DDACTS

Data-Driven Approaches to Crime and Traffic Safety

NHTSA National Highway Traffic Safety Administration

and CTSRC Connecticut Transportation Safety Research Center

Figure 6

Source: Nampa Police Department

THANK YOU