Advanced solutions for solar plants

January 14, 2018 | Author: Anonymous | Category: Science, Health Science, Pediatrics
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Sergio Asenjo, Head of Solar Center of Competence, June 10th 2010

Advanced solutions for solar plants

© ABB PP&PS FES Italia April 9, 2015 | Slide 1

Photovoltaic plant automation Architecture The system will manage, among traditional automation functions/features:

© ABB Solar COC Spain April 9, 2015 | Slide 2



Solar tracking system, when available, for production maximization



Performance calculation of the different stages



ABB patented Switching System for optimizing inverter efficiency



Troubleshooting management of strings



Integration of plant security and surveillance system



Production automatic reporting system

Solar standard solution Technology highlights

© ABB Solar COC Spain April 9, 2015 | Slide 3



High precision shadowing control algorithm for solar tracking



Extensible and scalable solution for any plant size



Switching system for optimizing inverter efficiency



Performance/efficiency oriented supervision system

Solar standard solution Technology highlights High precision shadowing control algorithm for solar tracking

© ABB Solar COC Spain April 9, 2015 | Slide 4



Shadowing prevention according to tracker dimensions and plant layout



Other systems use “backtracking correction”, thus preventing unnecessary movements and efficiency losses

Solar standard solution Technology highlights



High precision shadowing control algorithm for solar tracking 

© ABB Solar COC Spain April 9, 2015 | Slide 5

ABB algorithm calculates the optimal position modeling panels and tracker structure geometry

Photovoltaic plant automation Architecture LAN 2 Local Automation LAN 1 Solar Tracker OPERATOR WORKPLACE DCS Inverters

MV an LV Swicthgears

Internet eMail

Remote Access Transformers

Remote Office © ABB Solar COC Spain April 9, 2015 | Slide 6

Photovoltaic plant automation Function allocation 



At the DCS level is controlled 

Solar plant power electronics device controls



Optimization - switching



Neural networks - intelligent forecast and approximation



Alarms and events handling

At local automation is performed 



© ABB Solar COC Spain April 9, 2015 | Slide 7

Trackers 

Accurate solar tracking algorithm



One and two axis movement control implementation

Power connection box 

Power connection box management



Current per line current control to detect strings failures

Photovoltaic plant automation Local automation architecture Supervision & control systems

8PLC3

9PLC3

9PLC5

9PLC1

8PLC2

7PLC4

7PLC3

6PLC5

6PLC2

4PLC4

6PLC4

6PLC1

4PLC3

6PLC3

5PLC4

7PLC1

5PLC3

5PLC2

4PLC2

3PLC3

2PLC5

4PLC1

3PLC2

2PLC4

3PLC4

3PLC1

2PLC3

1PLC4

2PLC2

1PLC3

Cable Cat5+

9PLC4

9PLC2

9PLC4

8PLC1

7PLC2

5PLC1

2PLC1

1PLC2

1PLC1

Cable interior armari o RS20-0400

Fibra óptica Multimodo Master 2

RS20-0800

RS20-0800

ADSL SAI ON-LINE

© ABB Solar COC Spain April 9, 2015 | Slide 8

Master 1

Spider 5Tx

Photovoltaic plant automation Operator mimics

© ABB Solar COC Spain April 9, 2015 | Slide 9

Photovoltaic plant automation Operator mimics

© ABB Solar COC Spain April 9, 2015 | Slide 10

Solar standard solution Technology highlights Switching System for optimizing inverter efficiency

© ABB Solar COC Spain April 9, 2015 | Slide 11



Input power distribution for optimizing inverter efficiency



Switching principles: 

Inverter low performance at low loads



Inverter high performance at medium-high loads



One inverter working at medium load, better than two inverters working at low load



Load balancing among inverters

Solar standard solution Technology highlights

Switching System for optimizing inverter efficiency



© ABB Solar COC Spain April 9, 2015 | Slide 12

Low performance



High performance

Photovoltaic plant automation Advanced optimization 

© ABB Solar COC Spain April 9, 2015 | Slide 13

DCS advanced control functions 

Operation of the switch over cabinet



Optimization based theoretical calculations



Neural networks analysis

Photovoltaic plant automation Advanced optimization

Over the Maximum Power Point Tracking algorithm (MPPT) to increase performance in operational points like low sun conditions it has been developed a set of algorithms based on Artificial Neural Networks (ANN) and designed to adapt themselves to the particular conditions of every PV plant

© ABB Solar COC Spain April 9, 2015 | Slide 14

Solar standard solution Technology highlights Switching system for optimizing inverter efficiency

© ABB Solar COC Spain April 9, 2015 | Slide 15



Neuronal Network is an adaptive approximation method to achieve a more accurate calculation of output power in case of switching



Working Principle: 

Two inverters: PI1=I1*V1 ; PI2=I2*V2



Switching all strings to Inverter 1



One inverter; PI=PI1+PI2 (Ideal)



One inverter; PI’=PI1’+PI2’ (real)

Solar standard solution Technology highlights Switching System for optimizing inverter efficiency PI nv 1

PI nv 1

PI nv 2

PI nv 3

PI nv 3



PI nv1 PI nv 2

PInv 3

© ABB Solar COC Spain April 9, 2015 | Slide 16



PI nv1

PI nv 3

The difference is in the PV turbine equivalent I-V curve (affected by panel degradation, dirtiness, etc..) Neuronal network learns from real values to get progressively a better PI’

Solar standard solution Technology highlights Performance/efficiency oriented supervision system 

© ABB Solar COC Spain April 9, 2015 | Slide 17

Real time plant performance ratio calculation based on: 

Irradiation



Panels strings



Inverters



Transformers

New advanced features Oriented to performance 

© ABB Solar COC Spain April 9, 2015 | Slide 18

Efficiency calculation: 

For individual elements (strings, trackers, inverters…)



For stages



For the whole plant



To allocate malfunctions in the shortest time



Alarms for deviation in real time (alarms)



Reports

Stages for performance Calculations Tracking Perfect Optimal distribution

Modules Characteristics

DC cable Design charactericits

Transformers characteristics

Inverter characteristics Swicthing scheme

Trafo

Strings

Inverters output

Inverters

Irradiation Real Position

Temperature

V

V

A

String

Modules Efficiency

© ABB Solar COC Spain April 9, 2015 | Slide 19

Tracker

Tracking Efficiency

A

DC field

Cabling efficiency

Inverters

Inverters and Swicthing Efficiency

Counter V

V

A

A

Transformer

Trasnformers efficiency

Real performance Devices for measuring 

Measurements devices: 

© ABB Solar COC Spain April 9, 2015 | Slide 20

Weather station 

Pyranometers



Reference cells



Inclinometers



Strings measurements



Inverters measurement 

Input DC



Output ac



Transformers



Electrical metering

Theoretical performance Calculation methods 



Equipment characteristics 

Modules behavior



Tracking models Perfect



Optimal



Cabling design



Switching, inverter curves



Transformers performance curves

Control system strategy and features 

© ABB Solar COC Spain April 9, 2015 | Slide 21



PLCs, SCADA, Databases

Energy balance reports 18/12/2009

Modules

Plant

Líne

String

Radiation

Output Measured

Output Calculated

P1

P1-L1

P1-L1-S1

8 KWh

1,2 KWh

1,22 KWh

14%

14,5%

96,6%

P1-L1-S2

8 KWh

1,2 KWh

1,22 KWh

14%

14,5%

96,6 %

P1-L1-S3

8 KWh

1,2 KWh

1,22 KWh

14%

14,5%

96,6 %

P1-L1

24 KWh

3,6 KWh

3,66 Kwh

14%

14,5%

96,6 %

P1-L2-S1

8 KWh

1,2 KWh

1,22 KWh

14%

14,5%

96,6 %

P1-L2-S2

8 KWh

0,9 KWh

1,22 KWh

11,25%

14,5%

77,58%

P1-L2-S3

8 KWh

1,2 KWh

1,22 KWh

14%

14,5%

96,6%

P1-L2

24 KWh

3,3 KWh

3,66 Kwh

12,5%

14,5%

90,26%

P1

--

48 KWh

6,9 KWh

7,32 Kwh

13,78%

14,5%

93,52%

P2-L1

P2-L1-S1

8 KWh

1,2 KWh

1,22 KWh

14%

14,5%

96,6 %

P2-L1-S2

8 KWh

1,2 KWh

1,22 KWh

14%

14,5%

96,6 %

P2-L1-S3

8 KWh

1,1 KWh

1,22 KWh

13%

14,5%

90,11 %

P2-L1

24 KWh

3,5 KWh

3,66 Kwh

13,64%

14,5%

94,35%

P2

--

24 KWh

3,5 KWh

3,66 Kwh

13,64%

14,5%

94,35%

--

--

72 KWh

10,4 KWh

10,98 Kwh

13,71%

14,5%

93,80%

P1-L2

P2

Summary © ABB Solar COC Spain April 9, 2015 | Slide 22

Eff. Measured Eff. Calculated

Ratio

ABB system optimization Automatic Switching system during hail and high wind  Production increase.

Wind position.  Production in normal conditions  Production during high wind

Disminución de irradiancia debido a la posición horizontal

Irradiancia (W/m2)

1200,00 1000,00 800,00 600,00

Nubosidad

400,00 200,00 0,00 7:59

10:23

12:47

15:11

Hora

Irradiancia día 14 de septiem bre que llega a los seguidores

1200

1000

Irradiancia

800

600

Sin granizo Con granizo

400

200 0 4:48

7:12

9:36

12:00

14:24

16:48

-200 Hora

Hail Position  Production in normal conditions © ABB Solar COC Spain April 9, 2015 | Slide 23

 Production during hail situation.

19:12

ABB system optimization Automatic Switching system in dawn, nightfall and clouds

Red color area  production increase

Cloudiness

Dawn - nightfallr

Dawn

© ABB Solar COC Spain April 9, 2015 | Slide 24

Solar standard solution Technology improvements 

PV Plant 1



PV Plant 3



PV Plant 2

100 Kwh 90 Kwh 80 Kwh 70 Kwh 60 Kwh 50 Kwh

          

40 Kwh 30 Kwh 20 Kwh 10 Kwh 0 Kwh

Performance/efficiency increased by 0,8% to 2,5%

Production increased during the whole day, starting earlier and shutting off later.

© ABB Solar COC Spain April 9, 2015 | Slide 25

Photovoltaical power plant (PV) Reference plant

© ABB Solar COC Spain April 9, 2015 | Slide 26

© ABB Solar COC Spain April 9, 2015 | Slide 27

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