Università degli Studi di Napoli “Federico II” Department of Electrical Engineering and Information Technology (DIETI)

Master’s Degree in Electronics Engineering

Supervisor Ch.mo Prof. Santolo Daliento

Assistant supervisor ing. Pierluigi Guerriero

Candidate Luigi Maria Di Nardo

L. M. Di Nardo

Growth of the PV market in Italy (fromG.S.E.)

failures

architectural shadows

Need for monitoring

Difficulty in locating

Need to estimate the efficiency of these plants

[2]

Failure or shadowing detection

06:00 09:00 12:00 15:00 18:00 21:000

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Istr

ing

[A]

Time [h]

Quantification of yield loss due to failures and malfunctions

[3]

L. M. Di Nardo

o Introduction to the state of the art

o Automated tools for diagnostics o Automatic detection of failures and malfunctions o Graph of the electrical parameters

o Smart String

o Experimental results 06:00 09:00 12:00 15:00 18:00 21:000

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4000Ps

tring

[W]

Time [h]

[4]

L. M. Di Nardo

[5]

Monitoring at panel level

Monitoring at inverter level

sensor for the monitoring that operates at panel level and provides direct measurement data

device that connects to the inverter and provides production data

inverter

L. M. Di Nardo

Monitoring of PV plants – State of the art [6]

L. M. Di Nardo

Intuitive device interface and web interface

Easy installation and usage Presentation of performance data

and system information, in graphical or tabular format

× Provides information on the actual production only and not on the producibility

× Not possibile to identify the nature and location of any fault

× Potentially very long time of intervention

Monitoring of PV plants – State of the art

Complete and useful web interface for the maintenance operator

Complete monitoring of all elements of the system

Panel level monitoring Detection and location of faults and

problems Estimation of producibility from

direct measurements in the field. × Computational complexity

proportional to the size of the field × Cost proportional to the size of the

field.

[7]

L. M. Di Nardo

• Limited computational complexity

• Minimisation of data processing times

• Not too many additional electrical connections

• Costs which don't grow too much with the growth of the dimensions

• Fast localisation of faults

Solution?

Monitoring of large PV plants [8]

L. M. Di Nardo

Innovative system for the diagnostics and monitoring of large PV plants at string level. Two measurement boards for each string; Wireless communication to the String

Unit; Modbus communication to the remote

station; Web interface and configurable alarms

system.

Introducing the device Smart String 1/2

Design Production

[9]

L. M. Di Nardo

Introducing the device Smart String 2/2

Available info Voc, Isc, V and I of the panels directly

monitored Temperature of the cells of the panels directly

monitored I, V and P of each string Energy produced by the string. Power producible by the string. Detectable malfunctions • Production interruption. • Current mismatch among strings connected in

parallel. • Identification of the causes of power loss at

panel level (panels directly monitored) • Identification of the causes of power loss at

string level.

[10]

L. M. Di Nardo

Identification of power loss events

Development of a fully automated procedure for the detection of power loss events.

Reading measurement

data

Research and identification of power loss

events

Creation of the events

Grouping events by temporal contiguity and verification of

daily repetition

Estimation of the energy loss for each group

of events

Yield losses graph

generation

Detection methods: Bypass events; Power loss with respect to

the estimated power; Power loss with respect to

the maximum power detected.

[11]

L. M. Di Nardo

Graph of the electrical parameters

GUI to quickly show the electrical parameters of the PV strings. Graph of:

Isc and Voc of monitored panels

Operating I and V of monitored panels

Pstring of each string Highest instantaneous Pstring

of the entire field Ability to select strings for which

activate the display Ability to select the date and time. Ability to make the plot of different

days overlapped.

[12]

L. M. Di Nardo

PV plant under test Located in Maddaloni, southern Italy, on a flat roof.

Rated output PV plant 83,385 kWp

327 PV panels,rated output 255 W each

3 inverters, rated output 27,6 kWp each

15 strings with 18 panels

3 strings with 19 panels

South

[13]

L. M. Di Nardo

Bypass detection 1/5 Using the automated procedure is possible to search for bypass conditions .

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Istri

ng [A

]

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Isc panel n° 9 (string 1)

Isc panel n° 1 (string 1)

Istring (string 1) In standard conditions

Approximately at 9:00 a.m.

Presumed shadowing on

the plant

03 October 2013

[14]

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 > 𝐼𝐼𝐼𝐼𝐼𝐼𝑝𝑝𝑝𝑝𝑝𝑝.9

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 < 𝐼𝐼𝐼𝐼𝐼𝐼

L. M. Di Nardo

Bypass detection 2/5 Search for confirmation of the bypass observing the operating voltages of the monitored modules

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Vst

r [V

]

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Vstr panel n° 9 (string 1)

Vstr panel n° 1 (string 1)

03 October 2013 Lowering the operating voltage to 1/3 of the rated one.

2 sub-panels of the panel n°9 are

bypassed

[15]

L. M. Di Nardo

Bypass detection 3/5

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Vst

ring

[V]

Time [h]

Vstring string1

03 October 2013

Vstring string1 07 October 2013

Monitoring for several days, it turns out that the 'malfunction' is repetitive.

Architectural shadows

[16]

L. M. Di Nardo

Bypass detection 4/5

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Nor

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rgy

Loss

bypass event string 1

02 October 2013

bypass event string 1

03 October 2013

The software automatically identifies the repetition of the bypass events. Discovers them, groups them together and estimates the energy lost.

The width of the bars indicates the duration of the event, the height is proportional to the energy lost.

[17]

L. M. Di Nardo

Bypass detection 5/5 The colours indicate the energy lost. Observation period of about two and a half months long.

The plot recalls the layout of the PV plant

[18]

The loss of energy is due to the bypass

events

L. M. Di Nardo

Performance analysis 1/2

The plot recalls the layout of the PV plant

Non-negligible losses due to shadowing

[19]

Confront with the maximum power measured on the plant

L. M. Di Nardo

Performance analysis 2/2

The plot recalls the layout of the PV plant

Non-negligible room for improvement on

some strings

Confront with the maximum theoretical power estimated by direct measures.

[20]

𝑃𝑃𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼=𝐼𝐼𝐼𝐼𝐼𝐼∗𝑉𝑉𝑜𝑜𝐼𝐼∗𝐹𝐹𝐹𝐹∗𝐼𝐼

L. M. Di Nardo

Picture of the PV plant 1/2 Correspondence between the identified shadowing conditions and the surroundings.

[21]

Picture of the plant in S-W direction

L. M. Di Nardo

Picture of the PV plant 2/2 Correspondence between the identified shadowing conditions and the surroundings.

[22]

Picture of the plant in S direction

L. M. Di Nardo

Conclusions [23]

Step forward with respect to the state of the art, in particular for large PV plants

Ability to obtain information about the condition of the plant with knowledge of the plant layout only.

Quick individualisation of power loss events due to: bypass; lower power than the estimated one; lower power than the maximum detected one.

Quantification of the power loss Features of the developed software Innovative yield analysis obtained from direct measures

in the field.

L. M. Di Nardo