advanced solutions for solar plants milan infracon, head of solar center of competence, june 10th...

$: Advanced solutions for solar plants Milan Infracon, Head of Solar Center of Competence, June 10th 2012 © MIPL SOLAR PLANT December 14, 2015 | Slide Sergio$
Advanced solutions for solar plantsMilan Infracon, Head of Solar Center of Competence, June 10th 2012

© MIPL SOLAR PLANTApril 21, 2023 | Slide

Sergio Asenjo, Head of Solar Center of Competence, June 10th 2010

© MIPL Solar April 21, 2023 | Slide 2

Photovoltaic plant automationArchitecture

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

Solar tracking system, when available, for production maximization

Performance calculation of the different stages

MIPL patented Switching System for optimizing inverter efficiency

Troubleshooting management of strings

Integration of plant security and surveillance system

Production automatic reporting system


Solar standard solutionTechnology highlights

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




Shadowing prevention according to tracker dimensions and plant layout

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




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


Photovoltaic plant automationArchitecture

LAN 2

Local Automation

Solar Tracker

Inverters

MV an LV Swicthgears

DCS

Transformers

OPERATORWORKPLACE

Remote Office

Internet

Remote Access

LAN 1

eMail


Photovoltaic plant automationFunction 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

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


RS20-0800 RS20-0800

RS20-0400 Spider 5Tx

9PLC5

Fibra ópticaMultimodo

Cable Cat5+

Cable interior armario

9PLC4

9PLC3

9PLC2

9PLC1

9PLC4

8PLC3

8PLC2

8PLC1

7PLC4

7PLC3

7PLC2 7PLC1

6PLC5

6PLC4

6PLC3

6PLC2

6PLC1

5PLC4

5PLC3

4PLC4

4PLC3

5PLC2

5PLC1

4PLC2

4PLC1

3PLC4

3PLC3

3PLC2

3PLC1

2PLC5

2PLC4

2PLC3

1PLC4

2PLC2

2PLC1

1PLC3

1PLC2 1PLC1

Master 2 Master 1

SAION-LINE

ADSL

Supervision & control systems

Photovoltaic plant automationLocal automation architecture


Photovoltaic plant automationOperator mimics



Switching System for optimizing inverter efficiency

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



Switching System for optimizing inverter efficiency

Low performance High performance


Photovoltaic plant automationAdvanced optimization

DCS advanced control functions

Operation of the switch over cabinet

Optimization based theoretical calculations

Neural networks analysis


Photovoltaic plant automationAdvanced 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



Switching system for optimizing inverter efficiency

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)



Switching System for optimizing inverter efficiency 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’

1nvIP

2nvIP

3nvIP

1nvIP

3nvIP

1nvIP

2nvIP

3nvIP

1nvIP

3nvIP



Performance/efficiency oriented supervision system

Real time plant performance ratio calculation based on:

Irradiation

Panels strings

Inverters

Transformers


New advanced featuresOriented to performance

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 performanceCalculations

Modules Efficiency

Tracking Efficiency

Cabling efficiency

Inverters and Swicthing Efficiency

Trasnformers efficiency

Irradiation

Temperature

Strings Inverters Inverters output

Modules Characteristics

Tracking- Perfect- Optimal

distribution

Inverter characteristics

Swicthing scheme

Transformers characteristics

Real Position

String Tracker Inverters Transformer

Trafo

Counter

DC cable Design charactericits

DC fieldA

V

A

V

A

V

A

V


Real performanceDevices for measuring

Measurements devices:

Weather station

Pyranometers

Reference cells

Inclinometers

Strings measurements

Inverters measurement

Input DC

Output ac

Transformers

Electrical metering


Theoretical performanceCalculation methods

Equipment characteristics

Modules behavior

Tracking models

Perfect

Optimal

Cabling design

Switching, inverter curves

Transformers performance curves

Control system strategy and features

PLCs, SCADA, Databases


Energy balance reports

18/12/2009 Modules

Plant Líne String RadiationOutput

MeasuredOutput

Calculated Eff. Measured Eff. Calculated Ratio

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 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 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%

Summary -- -- 72 KWh 10,4 KWh 10,98 Kwh 13,71% 14,5% 93,80%


Production increase.

Nubosidad

Disminución de irradiancia debido a la posición horizontal

0,00

200,00

400,00

600,00

800,00

1000,00

1200,00

7:59 10:23 12:47 15:11Hora

Irra

dia

ncia

(W

/m2)

Wind position.

Production in normal conditions

Production during high wind

Irradiancia aprovechada en caso de viento

0

100

200

300

400

500

600

7:59 9:11 10:23 11:35 12:47 13:59 15:11 16:23

Horas con seguidor en posición horizontal

Irrad

ianc

ia (W

/m2)

Irradiancia aprovechada con conmutación

0

20

40

60

80

100

120

140

160

180

200

4:48 7:12 9:36 12:00 14:24 16:48 19:12

Hora

Con granizo

Hail Position

Production in normal conditions

Production during hail situation.

MIPL system optimizationAutomatic Switching system during hail and high wind


Irradiancia día 23

0,00

100,00

200,00

300,00

400,00

500,00

600,00

700,00

800,00

0:00 2:24 4:48 7:12 9:36 12:00 14:24 16:48 19:12 21:36

Tiempo (h)

Dawn

Cloudiness

Dawn - nightfallr

Red color area production increase

MIPL system optimizationAutomatic Switching system in dawn, nightfall and clouds


Solar standard solutionTechnology improvements

0 Kwh

10 Kwh

20 Kwh

30 Kwh

40 Kwh

50 Kwh

60 Kwh

70 Kwh

80 Kwh

90 Kwh

100 Kwh

6:

00

7:

00

8:

00

9:

00

10

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11

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12

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:00

PV Plant 1 PV Plant 3 PV Plant 2

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

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


Photovoltaical power plant (PV) Reference plant

advanced solutions for solar plants milan infracon, head of solar center of competence, june 10th...

Documents

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solar trackingmipl algorithm

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